Training module 1: Summary

Draft (Step 2) guideline ICH E9(R1) Estimands and Sensitivity Analysis in Clinical Trials Training mdule 1: Summary Addendum t ICH E9 Statistical Principles fr Clinical Trials ICH E9(R1) Expert Wrking Grup June 2018 Internatinal Cuncil fr Harmnisatin f Technical Requirements fr Registratin f Pharmaceuticals fr Human Use 1

Mdule 1 Summary Disclaimer This presentatin includes the authrs views n thery and practice regarding estimands and sensitivity analysis in clinical trials. The presentatin des nt represent fficial guidance r plicy f authrities r industry and it des nt prvide additinal guidance beynd ICH E9(R1). The current slide deck reflects the cntent n the Draft ICH E9(R1) addendum up t the time f its publicatin fr public cnsultatin. Cntent may be changed as a result f the cnsultatin phase. 2

Legal Ntice ICH E9(R1) Step 2 Training Material Mdule 1 Summary This presentatin is prtected by cpyright and may be used, reprduced, incrprated int ther wrks, adapted, mdified, translated r distributed under a public license prvided that ICH's cpyright in the presentatin is acknwledged at all times. In case f any adaptatin, mdificatin r translatin f the presentatin, reasnable steps must be taken t clearly label, demarcate r therwise identify that changes were made t r based n the riginal presentatin. Any impressin that the adaptin, mdificatin r translatin f the riginal presentatin is endrsed r spnsred by the ICH must be avided. The presentatin is prvided "as is" withut warranty f any kind. In n event shall the ICH r the authrs f the riginal presentatin be liable fr any claim, damages r ther liability arising frm the use f the presentatin. The abve-mentined permissins d nt apply t cntent supplied by third parties. Therefre, fr dcuments where the cpyright vests in a third party, permissin fr reprductin must be btained frm this cpyright hlder. 3

Intrductin nte ICH E9(R1) Step 2 Training Material Mdule 1 Summary These slides were prduced as training material t accmpany the Draft ICH E9(R1) addendum. The intentin is t supprt the scientific cmmunity in the cmprehensin f a new framewrk t define estimands based n the trial bjective and cnsidering intercurrent events. Fr this purpse, mst f the cntent f this addendum is presented in a practical fashin, accmpanied by examples and case studies dealing with estimands and sensitivity analysis in clinical trials, based n the experience f Expert Wrking Grup members. The training material is divided in three main mdules: mdule 1 (summary), mdule 2 (cmprehensive slide deck) and mdule 3 (generic example). Mdule 2 is cmpsed by 6 submdules that crrespnd t sectins A.1 t A.6 f the addendum. 4

Mdule 1 Summary Training mdules Mdule 1: Summary Mdule 2: Cmprehensive slide deck Mdule 2.1: Intrductin Mdule 2.2: Framewrk Mdule 2.3: Estimands Mdule 2.4: Impact n trial design and cnduct Mdule 2.5: Impact n trial analysis Mdule 2.6: Dcumenting estimands and sensitivity analysis Mdule 3: Generic example 5

Mdule 1 Summary ICH E9(R1) Table f Cntents A.1. Purpse and Scpe A.2. A Framewrk t Align Planning, Design, Cnduct, Analysis and Interpretatin A.3. Estimands A.3.1. Descriptin A.3.2. Strategies fr Addressing Intercurrent Events Mdule 2.3 A.3.3. Cnstructin f Estimands - A.3.3.1. General Cnsideratins - A.3.3.2. Cnsideratins f Therapeutic and Experimental Cntext A.4. Impact n Trial Design and Cnduct Mdule 2.4 A.5. Impact n Trial Analysis A.5.1. Main Estimatin Mdule 2.1 A.5.2. Sensitivity Analysis - A.5.2.1. Rle f Sensitivity Analysis - A.5.2.2. Chice f Sensitivity Analysis A.5.3. Supplementary Analysis A.6. Dcumenting Estimands and Sensitivity Analysis A.7. A Generic Example A.7.1. One Intercurrent Event Mdule 3 A.7.2. Tw Intercurrent Events Glssary Mdule 2.5 Mdule 2.6 Mdule 2.2 6

Mdule 1 Summary Check Mdule 2.2 Summary The addendum aims t imprve the planning, design, analysis and interpretatin f clinical trials. Clear trial bjectives shuld be translated int key scientific questins f interest by defining suitable estimands. An estimand defines the target f estimatin fr a particular trial bjective (i.e. what is t be estimated ). 7

Mdule 1 Summary Check Mdule 2.1 Summary The addendum aims t imprve the planning, design, analysis and interpretatin f clinical trials. Withut a precise descriptin f the trial bjective and the treatment effect that is targeted fr testing and estimatin there is a risk that: the study will nt be designed apprpriately t address its bjective; the statistical analyses will be misaligned t the trial bjective and the target f estimatin; the treatment effect that is reprted will be incrrectly interpreted, which risks misleading decisin makers. 8

Mdule 1 Summary Check Mdule 2.2 Summary This addendum presents a structured framewrk t link trial bjectives t a suitable trial design and tls fr estimatin and hypthesis testing. Trial bjective Estimand Target f estimatin = WHAT TO ESTIMATE Methd f estimatin = HOW TO ESTIMATE Main estimatr Main estimate 9

Mdule 1 Summary Check Mdule 2.2 Summary The addendum aims t facilitate dialgue regarding the treatment effects that a clinical trial shuld seek t estimate: between disciplines (medics, statisticians, etc). between spnsr and regulatr. Having clarity in the trial bjectives when describing the treatment effect f interest at the planning stage shuld infrm apprpriate chices abut trial design, data cllectin and statistical analysis. 10

Mdule 1 Summary Check Mdule 2.3 Summary The descriptin f an estimand will nt be cmplete withut reflecting hw ptential intercurrent events are addressed in the scientific questin f interest. Intercurrent events have the ptential t ccur after treatment initiatin and either preclude bservatin f the variable r affect its interpretatin, e.g.: use f an alternative treatment, perhaps a rescue medicatin; discntinuatin f treatment; terminal events such as death. Study discntinuatin, lss-t-fllw-up r ther missing data are nt intercurrent events and are nt reflected in the estimand, but instead represent limitatins t the data t be addressed in the analysis. 11

Mdule 1 Summary Check Mdule 2.3 Summary Different strategies are available fr hw t address ptential intercurrent events t reflect the scientific questin f interest. The chice f strategy fr each intercurrent event shuld be the subject f multi-disciplinary discussin between, e.g. statisticians and clinicians, and discussin between spnsr and regulatr. The disease setting and the aim f treatment will influence the chice f strategy. Different strategies can be used alne r in cmbinatin t address multiple different intercurrent events. 12

Mdule 1 Summary Check Mdule 2.4 Summary Having specified an estimand (=WHAT TO ESTIMATE), the addendum addresses impact n trial design, cnduct and analysis (=HOW TO ESTIMATE). In respect f estimatin, the addendum calls fr greater precisin n what is labelled as missing data. Specifically, having clarity in the estimand gives a basis fr planning which data need t be cllected and hence which data, when nt cllected, present a missing data prblem t be addressed. 13

Mdule 1 Summary Check Mdule 2.5 Summary The addendum gives a mre precise fcus t requirements fr sensitivity analysis. With an agreed estimand, and a pre-specified statistical analysis that is aligned t that estimand, sensitivity analysis can fcus n sensitivity t deviatins frm assumptins in respect f a particular analysis, rather than sensitivity t the chice f analytic apprach. 14

Mdule 1 Summary Check Mdule 2.6 Summary The addendum calls fr changes in trial dcumentatin. Estimands shuld be defined and explicitly specified in the clinical trial prtcl. The prtcl and the statistical analysis plan shuld pre-specify the main estimatr fr each estimand, tgether with a suitable sensitivity analysis t explre the rbustness under deviatins frm its assumptins. 15

Next mdule: 2.1. Intrductin Understanding treatment effects and mtivatin fr the ICH E9(R1) addendum. Scpe f the dcument and examples. Internatinal Cnference n Harmnisatin f Technical Requirements fr Registratin f Pharmaceuticals fr Human Use 16

Draft (Step 2) guideline ICH E9(R1) Estimands and Sensitivity Analysis in Clinical Trials Training mdule 2.1: Intrductin Addendum t ICH E9 Statistical Principles fr Clinical Trials ICH E9(R1) Expert Wrking Grup June 2018 Internatinal Cuncil fr Harmnisatin f Technical Requirements fr Registratin f Pharmaceuticals fr Human Use 1

Mdule 2.1 Intrductin Disclaimer This presentatin includes the authrs views n thery and practice regarding estimands and sensitivity analysis in clinical trials. The presentatin des nt represent fficial guidance r plicy f authrities r industry and it des nt prvide additinal guidance beynd ICH E9(R1). The current slide deck reflects the cntent n the Draft ICH E9(R1) addendum up t the time f its publicatin fr public cnsultatin. Cntent may be changed as a result f the cnsultatin phase. 2

Legal Ntice ICH E9(R1) Step 2 Training Material Mdule 2.1 Intrductin This presentatin is prtected by cpyright and may be used, reprduced, incrprated int ther wrks, adapted, mdified, translated r distributed under a public license prvided that ICH's cpyright in the presentatin is acknwledged at all times. In case f any adaptatin, mdificatin r translatin f the presentatin, reasnable steps must be taken t clearly label, demarcate r therwise identify that changes were made t r based n the riginal presentatin. Any impressin that the adaptin, mdificatin r translatin f the riginal presentatin is endrsed r spnsred by the ICH must be avided. The presentatin is prvided "as is" withut warranty f any kind. In n event shall the ICH r the authrs f the riginal presentatin be liable fr any claim, damages r ther liability arising frm the use f the presentatin. The abve-mentined permissins d nt apply t cntent supplied by third parties. Therefre, fr dcuments where the cpyright vests in a third party, permissin fr reprductin must be btained frm this cpyright hlder. 3

Intrductin nte ICH E9(R1) Step 2 Training Material Mdule 2.1 Intrductin These slides were prduced as training material t accmpany the Draft ICH E9(R1) addendum. The intentin is t supprt the scientific cmmunity in the cmprehensin f a new framewrk t define estimands based n the trial bjective and cnsidering intercurrent events. Fr this purpse, mst f the cntent f this addendum is presented in a practical fashin, accmpanied by examples and case studies dealing with estimands and sensitivity analysis in clinical trials, based n the experience f Expert Wrking Grup members. The training material is divided in three main mdules: mdule 1 (summary), mdule 2 (cmprehensive slide deck) and mdule 3 (generic example). Mdule 2 is cmpsed by 6 submdules that crrespnd t sectins A.1 t A.6 f the addendum. 4

Mdule 2.1 Intrductin Training mdules Mdule 1: Summary Mdule 2: Cmprehensive slide deck Mdule 2.1: Intrductin Mdule 2.2: Framewrk Mdule 2.3: Estimands Mdule 2.4: Impact n trial design and cnduct Mdule 2.5: Impact n trial analysis Mdule 2.6: Dcumenting estimands and sensitivity analysis Mdule 3: Generic example 5

Mdule 2.1 Intrductin ICH E9(R1) Table f Cntents A.1. Purpse and Scpe A.2. A Framewrk t Align Planning, Design, Cnduct, Analysis and Interpretatin A.3. Estimands A.3.1. Descriptin A.3.2. Strategies fr Addressing Intercurrent Events Mdule 2.3 A.3.3. Cnstructin f Estimands - A.3.3.1. General Cnsideratins - A.3.3.2. Cnsideratins f Therapeutic and Experimental Cntext A.4. Impact n Trial Design and Cnduct Mdule 2.4 A.5. Impact n Trial Analysis A.5.1. Main Estimatin Mdule 2.1 A.5.2. Sensitivity Analysis - A.5.2.1. Rle f Sensitivity Analysis - A.5.2.2. Chice f Sensitivity Analysis A.5.3. Supplementary Analysis A.6. Dcumenting Estimands and Sensitivity Analysis A.7. A Generic Example A.7.1. One Intercurrent Event Mdule 3 A.7.2. Tw Intercurrent Events Glssary Mdule 2.5 Mdule 2.6 Mdule 2.2 6

Outline f Mdule 2.1 Understanding treatment effects Cncerns with current practice that mtivate the guidance Scpe f the addendum t ICH E9 Examples ICH E9(R1) Step 2 Training Material Mdule 2.1 Intrductin 7

Mdule 2.1 Intrductin Objectives f Mdule 2.1: Intrduce the purpse f the addendum, describing the imprtance f understanding and accurately quantifying treatment effects. Explain cncerns with the misalignment f trial bjectives, design and analysis in current practice. Intrduce the cncept f intercurrent events as events that cmplicate the descriptin and interpretatin f treatment effects. Examples are given. Intrduce a cherent framewrk, including: the intrductin f estimands; a revised definitin fr sensitivity analysis. 8

ICH E9(R1) Training Material Mdule 2.1 Intrductin Example 2: Chrnic cnditins Specifically, a treatment effect is estimated under ITT: Regardless f adherence, use f ther medicatins r change in backgrund medicatin. This will nt necessarily islate any adverse (r beneficial) effect f the medicine itself n cardivascular utcmes. Understanding What t d instead? The framewrk at least prvides a basis fr discussin f alternative appraches that might prvide a mre relevant basis fr decisin making. treatment effects ITT: Intentin-T-Treat 9

Mdule 2.1 Intrductin Understanding treatment effects Fr regulatry apprval a medicinal prduct shuld have therapeutic efficacy and a psitive risk-benefit. In additin t evidence that is statistically cmpelling evidence f efficacy, assessment f efficacy cnsiders the magnitude f the beneficial treatment effects. Treatment effects are estimated frm clinical trials. The treatment effects f interest shuld be specified and agreed befre chices are made designing a clinical trial t estimate them. 10

Mdule 2.1 Intrductin Understanding treatment effects Treatment effect: hw the utcme f treatment cmpares t what wuld have happened t the same subjects under different treatment cnditins: e.g. difference between treatment A and n treatment; e.g. difference between treatment A and treatment B. Which difference? Let s illustrate with a trial in Type II Diabetes Mellitus: If we specify a scientific questin f interest as the treatment difference between treatment and n treatment n HbA1c at Week 24 there is still ambiguity. The next slides explain why there is ambiguity HbA1c: Glycated haemglbin 11

Mdule 2.1 Intrductin Understanding treatment effects Cnsider a medicine fr Type II Diabetes Mellitus. What happens in clinical practice (and in clinical trials)? Sme patients will tlerate a medicine and adhere t its administratin schedule, thers will nt. Sme subjects will require changes in dse f cncmitant medicatin r administratin f additinal medicatin (e.g. rescue medicatin, treatment switch, etc.), thers will nt. In a ppulatin r in a clinical trial 12

Mdule 2.1 Intrductin Understanding treatment effects: Cnsider a medicine fr Type II Diabetes Mellitus. What happens in clinical practice (and in clinical trials)? Sme patients will tlerate a medicine and adhere t its administratin schedule, thers will nt. Sme subjects will require changes in dse f cncmitant medicatin r administratin f additinal medicatin (e.g. rescue medicatin, treatment switch, etc.), thers will nt. Sme patients will tlerate and adhere t the treatment, thers will nt 13

Mdule 2.1 Intrductin Understanding treatment effects: Cnsider a medicine fr Type II Diabetes Mellitus. What happens in clinical practice (and in clinical trials)? Sme patients will tlerate a medicine and adhere t its administratin schedule, thers will nt. Sme subjects will require changes in dse f cncmitant medicatin r administratin f additinal medicatin (e.g. rescue medicatin, treatment switch, etc.), thers will nt. Sme patients will require additinal medicatin, thers will nt Different treatment effects can be cnstructed. 14

Mdule 2.1 Intrductin Understanding treatment effects: Multiple definitins f treatment effects Check draft addendum sectin A.3.2! Sme patients will tlerate and adhere t the treatment, thers will nt Different measures f effect f treatment n HbA1c at week 24 regardless f adherence (i.e. whether the patient is able t remain n treatment). r in the hypthetical cnditin that all patients culd adhere t treatment. r in the strata f this ppulatin that can adhere t treatment. 15

Mdule 2.1 Intrductin Understanding treatment effects: Multiple definitins f treatment effects Check draft addendum sectin A.3.2! Sme patients will require additinal medicatin, thers will nt Different measures f effect f treatment n HbA1c at week 24 regardless f whether additinal medicatin is used. r in the hypthetical cnditin that additinal medicatin was nt available. r in the strata f this ppulatin that d nt require additinal medicatin. 16

Mdule 2.1 Intrductin Understanding treatment effects: Check draft addendum sectin A.3.2! Nt all alternatives can be reliably estimated. Nt all alternatives will be equally acceptable fr regulatry decisin making! 17

Mdule 2.1 Intrductin Understanding treatment effects: Intercurrent events ccur after treatment initiatin and either preclude bservatin f the variable r affect its interpretatin. Multiple events may need t be cnsidered in the same ppulatin, and even fr the same subject. Sme patients will tlerate the treatment, thers will nt Sme patients will require rescue medicatin, thers will nt 18

Mdule 2.1 Intrductin Understanding treatment effects: The key pint is that multiple, different treatment effects can be cnsidered. Befre designing and analysing a clinical trial it shuld be clear which treatment effects are f interest: fr regulatry decisin making; fr ptimal cmmunicatin f treatment effects t patients, prescribers and ther stakehlders. 19

Mdule 2.1 Intrductin Understanding treatment effects: Multiple different treatment effects can be cnsidered What regulatry cntext is already available n the questin f defining treatment effects? ICH E9 includes a reference t a treatment plicy (included in the intentin t treat principle), but n alternatives: ( ) the effect f a treatment plicy can be best assessed by evaluating n the basis f the intentin t treat a subject (i.e. the planned treatment regimen) rather than the actual treatment given. It has the cnsequence that subjects allcated t a treatment grup shuld be fllwed up, assessed and analysed as members f that grup irrespective f their cmpliance t the planned curse f treatment. Intentin-t-treat principle definitin, ICH E9 Glssary 20

Mdule 2.1 Intrductin Understanding treatment effects: Multiple different treatment effects can be cnsidered Treatment plicy is based n assessments regardless f what happens t patients (discntinuatin f treatment, use f additinal medicatins etc.). Data frm thse assessments are used in the statistical analysis fr measuring treatment effects. Is that what we want t knw fr decisin making? There fllws a case study, purely fr illustrating that: multiple, different treatment effects can be described. prblems arise when the treatment effect f interest is nt defined and agreed in advance. 21

Mdule 2.1 Intrductin Example: Type II diabetes mellitus Primary endpint: Change in HbA1c frm baseline t 24 weeks. Applicant: Estimand nt stated Data cllected after initiatin f rescue medicatin were excluded frm the analysis. LOCF used fr imputatin. Regulatr: Target f estimatin and analytical apprach cnsidered While FDA has implicitly endrsed LOCF imputatin fr diabetes trials in the past, there is nw mre awareness in the statistical cmmunity f the limitatins f this apprach. Instead I have included a sensitivity analysis in which the primary HbA1c utcmes are used regardless f rescue treatment, and n statistical adjustment is made fr rescue. This apprach is als imperfect, but it cmes clser t being a true intent-t-treat (ITT) analysis... HbA1c: Glycated haemglbin LOCF: Last bservatin carried frward 22

Mdule 2.1 Intrductin Example: Type II diabetes mellitus Different perspectives n the inclusin f data: Applicant: Remve data after initiatin f rescue medicatin, and impute. Use f rescue medicatin FDA: Include all data regardless f initiatin f rescue medicatin Use f rescue medicatin In this case, absence f an agreed treatment effect f interest led t a primary analysis that was nt aligned t the treatment effect f interest preferred by the regulatr. ITT: Intentin-t-treat ITT principle fllwed up, assessed and analysed as members f that grup irrespective f their cmpliance t the planned curse f treatment requires a cmparisn f treatment plicies dapagliflzin plus rescue versus cntrl plus rescue. 23

Mdule 2.1 Intrductin Example: Type II diabetes mellitus The applicant had nt specified the treatment effect f interest: It shuld nt be the chices fr data cllectin and data analysis that determine the questin f interest! The chice f trial design and data cllectin might have resulted in difficulties fr estimatin r inadequate statistical pwer fr the analysis, and differences in interpretatin f the trial data. This illustrates the imprtance n reaching agreement ver what t estimate: the estimand. 24

Mdule 2.1 Intrductin Estimand Estimand Is the target f estimatin t address the scientific questin f interest psed by the trial bjective. Attributes f an estimand include the ppulatin f interest, the variable (r endpint) f interest, the specificatin f hw intercurrent events are reflected in the scientific questin f interest, and the ppulatinlevel summary fr the variable. Estimand definitin, ICH E9(R1) Draft addendum 25

Mdule 2.1 Intrductin Example: Type II diabetes mellitus In this case FDA and Applicant use different appraches t deal with the use f rescue medicatin. We can identify tw different treatment effects: This is an intercurrent event. t assess the treatment effect if rescue medicatin is nt available ; t assess the treatment effect regardless f whether rescue medicatin is used. These are apprpriately thught f as different questins (estimands) and nt tw different answers t the same questin. 26

Intercurrent events ICH E9(R1) Step 2 Training Material Mdule 2.1 Intrductin Intercurrent events Events that ccur after treatment initiatin and either preclude bservatin f the variable r affect its interpretatin. Intercurrent events definitin, ICH E9(R1) Draft addendum These events (e.g. death) that preclude the bservatin f the variable, shuld nt be cnfused with missing data resulting frm lss t fllw-up! 27

Mdule 2.1 Intrductin Check draft addendum sectin A.3.2! Strategies t address intercurrent events A strategy reflects the chice made n hw t address intercurrent events, in rder t describe the treatment effect that is targeted. The addendum intrduces (at least) five strategies that can be used alne r in cmbinatin t address multiple different intercurrent events. Treatment plicy Principal stratum Cmpsite While n treatment Hypthetical 28

ICH E9(R1) Training Material Mdule 2.1 Intrductin Example 2: Chrnic cnditins Specifically, a treatment effect is estimated under ITT: Regardless f adherence, use f ther medicatins r change in backgrund medicatin. This will nt necessarily islate any adverse (r beneficial) effect f the medicine itself n cardivascular utcmes. Cncerns with What t d instead? The framewrk at least prvides a basis fr discussin f alternative appraches that might prvide a mre relevant basis fr decisin making. current practice ITT: Intentin-T-Treat 29

Mdule 2.1 Intrductin Cncerns with current practice Currently: Targets f estimatin that are nt clearly stated and cannt necessarily be inferred frm infrmatin in the study prtcl and statistical analysis plan. Chices are made fr data handling and statistical analysis that are nt cnsistent with the treatment effect f interest Endpint Statistical analysis Ppulatin Missing data Practice shuld be reversed: the target f estimatin shuld be clear frm the study prtcl; The statistical analysis shuld be aligned t the agreed target f estimatin. 30

Mdule 2.1 Intrductin Cncerns with current practice Use f the Intentin-t-treat principle: ITT is mentined in prtcls, statistical analysis plans and trial reprts, but nly as a way f specifying which patients are included in the analysis set, and nt t reflect the principle that patients will be fllwed-up regardless f their cmpliance t the planned curse f treatment. Lack f clarity exists in the majrity f clinical trials ver which treatment effect is being estimated: if it is nt aligned t the ITT principle and treatment plicy, then what? Estimating smething ther than an effect aligned t the ITT principle might be acceptable, but the alternative must be precisely specified and agreed. ITT: Intentin-t-treat 31

Mdule 2.1 Intrductin Cncerns with current practice Missing Data In additin t study discntinuatins, patients wh discntinue assigned treatment, start anther treatment, r die are all treated generically as missing data causing prblems fr analysis and inference. Sme methds fr handling f missing data in current practice mis-represent treatment effects that are nevertheless used in drug licensing decisins and reprted t prescribers. E.g. mdelling t address the exclusin f data fr patients wh discntinue treatment, based n thse patients wh d nt, when estimating a treatment effect aligned t the ITT principle. ITT: Intentin-t-treat 32

Mdule 2.1 Intrductin Cncerns with current practice Targets f estimatin Targets f estimatin, nce identified, might nt be relevant t decisin makers (e.g. regulatrs, HTA bdies, payers, prescribers and patients): Different treatment effects might be relevant t different decisin makers. External validity There are cncerns that clinical trials are less representative if the spnsr tries t avid the ccurrence f, r the impact f, intercurrent events that will ccur in clinical practice, e.g. patients discntinuing frm treatment r using additinal medicatins. HTA: Health technlgy assessment 33

Mdule 2.1 Intrductin Cncerns with current practice Sensitivity analysis Analyses currently labelled as sensitivity analyses can in fact have different targets f estimatin (estimands), s that cnsistent results between analyses shuld nt necessarily be expected. Requirements that a brad range f sensitivity analyses all give cnsistent results might unnecessarily increase the hurdle fr demnstratin f therapeutic efficacy. 34

Mdule 2.1 Intrductin Cncerns with current practice Analysis sets Trials ften include repeated measurements n the same subject and the Full Analysis Set is incrrectly specified. ICH E9 strngly recmmends that analysis f superirity trials be based n the full analysis set, but eliminatin f planned measurements n sme subjects can have similar cnsequences t excluding subjects altgether frm the analysis. Per-prtcl analysis is subject t severe bias: can the need t explre the impact f prtcl vilatins and deviatins be addressed in a way that is less biased and mre interpretable than naïve analysis f the per-prtcl set? The treatment effect f interest shuld be defined in a way that determines the ppulatin f subjects t be included in the estimatin and the bservatins frm each subject t be included in the analysis cnsidering the ccurrence f intercurrent events. 35

Cncerns with current practice Bad drugs might appear gd and gd drugs might appear bad! Bad drugs might appear gd: ICH E9(R1) Step 2 Training Material Mdule 2.1 Intrductin Cnsider a drug fr a chrnic cnditin fr which efficacy and txicity are related. Patients wh benefit are mre likely t experience txicity. In a trial sme patients expsed t the drug will benefit n mre than patients expsed t placeb; thers will benefit but need t discntinue frm treatment due t txicity. Even if n-ne benefits in the lng term, it can be imagined that chices made n which data t cllect and include, and the analytical apprach, can either create r exaggerate a psitive treatment effect. 36

Mdule 2.1 Intrductin Cncerns with current practice Bad drugs might appear gd and gd drugs might appear bad! [illustratin cntinued] Gd drugs might appear bad: Cnsider a placeb-cntrlled trial testing a medicine that is, in fact, effective, where patients can use rescue medicatin when experiencing inadequate respnse. Imagine that use f rescue ccurs markedly mre n placeb than n experimental treatment and that rescue medicatin is als effective. A treatment effect defined regardless f whether additinal medicatin is used and estimated accrdingly, is likely t be lwer than the effect f treatment in the hypthetical cnditin that additinal medicatin was nt available r in the strata f this ppulatin that d nt require additinal medicatin (see Slide 16). 37

ICH E9(R1) Training Material Mdule 2.1 Intrductin Example 2: Chrnic cnditins Specifically, a treatment effect is estimated under ITT: Regardless f adherence, use f ther medicatins r change in backgrund medicatin. This will nt necessarily islate any adverse (r beneficial) effect f the medicine itself n cardivascular utcmes. Scpe f the What t d instead? The framewrk at least prvides a basis fr discussin f alternative appraches that might prvide a mre relevant basis fr decisin making. addendum t ICH E9 ITT: Intentin-T-Treat 38

Mdule 2.1 Intrductin Framewrk A framewrk and language are intrduced t: Prmte alignment between trial bjectives, design (data cllectin), cnduct, analysis and inference; Prmte understanding that trial bjectives cannt be translated int estimands withut reflecting hw ptential intercurrent events are addressed in the scientific questin f interest; Prmte discussin f different strategies t handle intercurrent events in rder t identify and describe the treatment effects that reflect the scientific questins f interest. Clinical Trial 39

Mdule 2.1 Intrductin Framewrk A framewrk and language are intrduced t: Define a treatment effect f interest - befre a trial is designed and cnducted - that is relevant t use f a medicine in clinical practice; Highlight the imprtance f cnsidering whether a main analysis will derive an estimate which is reliable fr inference; Re-define missing data; Re-define sensitivity analysis and the regulatry assessment f rbustness; Intrduce supplementary analysis as any ther analysis cnducted t fully investigate and understand the trial data. 40

Opprtunities ICH E9(R1) Step 2 Training Material Mdule 2.1 Intrductin Aligning drug develpers and regulatry bdies expectatins fr the target treatment effect in advance has the ptential t give: Mre meaningful descriptins f treatment effects fr licensing and prescribing decisins; Clinical trials with designs that are aligned t agreed bjectives; Increased transparency with respect t data analysis and inference; Mre predictable regulatry assessment prcedures. 41

Mdule 2.1 Intrductin Scpe The ICH E9(R1) addendum builds n ICH E9: i.e. its primary fcus is cnfirmatry clinical trials; clarity n treatment effects f interest fr regulatry decisin making is demanded. Hwever, the framewrk is applicable whenever treatment effects are t be estimated and tested: in ther phases f clinical develpment, including pst-authrisatin; in clinical trials and in bservatinal studies; regardless f therapeutic area r experimental design. 42

ICH E9(R1) Training Material Mdule 2.1 Intrductin Example 2: Chrnic cnditins Specifically, a treatment effect is estimated under ITT: Regardless f adherence, use f ther medicatins r change in backgrund medicatin. This will nt necessarily islate any adverse (r beneficial) effect f the medicine itself n cardivascular utcmes. Examples What t d instead? The framewrk at least prvides a basis fr discussin f alternative appraches that might prvide a mre relevant basis fr decisin making. The need fr defining estimands ITT: Intentin-T-Treat 43

Mdule 2.1 Intrductin Example 1: End f life in cancer patients Check Mdule 3: Generic example Cntext: Fr end f life cancer patients, medicines might be develped and administered t maintain weight, functining and quality f life fr the duratin f the patient s remaining life. Measures f efficacy: Bdy weight, bdy cmpsitin (DEXA), hand-grip strength and QL assessment scales. Intercurrent event: A nn-negligible prprtin f patients will die, which needs t be cnsidered in defining the treatment effect f interest. DEXA: Dual-energy X-ray absrptimetry QL: Quality f life 44

Mdule 2.1 Intrductin Check Mdule 3: case studies Example 1: End f life in cancer patients This is based n a real example where, withut statement f the estimand (treatment effect f interest) deaths were handled as missing data and utcmes imputed / mdelled accrding t the analysis plan; interpretatin f trial results was difficult; estimates f effect calculated n that basis had questinable relevance t effects in clinical practice; trying t identify an utcme at Week 12 fr the statistical analysis, fr the patients wh had previusly died, was arguably meaningless. What treatment effect shuld be f interest cnsidering that sme patients will die within 12 weeks f initiating treatment? 45

Mdule 2.1 Intrductin Example 2: Cardivascular safety Cntext: In sme chrnic cnditins (e.g. diabetes) current practice is t cnduct large utcme trials t exclude large harmful effects n cardivascular safety, with the ptential t demnstrate a beneficial effect. E.g. patients are randmised t a treatment vs. placeb n tp f a backgrund regimen that might differ between patients. Measures f efficacy: Patient are fllwed t bserve MACE events, ften ver a perid f years. MACE: Majr adverse cardiac events 46

Mdule 2.1 Intrductin Example 2: Cardivascular safety Intercurrent events: Patients discntinue assigned treatment, initiate new treatment and change backgrund treatment. Analyses can be cnducted, largely in line with the ITT principle (i.e. subjects are fllwed up, assessed and analysed irrespective f their cmpliance t the planned curse f treatment) in respect f these intercurrent events. Estimand: (E.g.) Difference between treatment and placeb in incidence f MACE regardless f whether patients discntinue assigned treatment, initiate new treatment r change backgrund treatment. Will this prvide an estimate f a treatment effect that is relevant t understanding the cardivascular risk f the treatment? Are there better strategies fr addressing these intercurrent events that give greater insights int the safety prfile f the treatment? ITT: Intentin-t-treat MACE: Majr adverse cardiac events 47

Next mdule: 2.2. Framewrk Implicatins n design and cnduct f clinical trials and in the perfrmance f statistical analyses Internatinal Cnference n Harmnisatin f Technical Requirements fr Registratin f Pharmaceuticals fr Human Use 48

Draft (Step 2) guideline ICH E9(R1) Estimands and Sensitivity Analysis in Clinical Trials Training mdule 2.2 Framewrk Addendum t ICH E9 Statistical Principles fr Clinical Trials ICH E9(R1) Expert Wrking Grup June 2018 Internatinal Cuncil fr Harmnisatin f Technical Requirements fr Registratin f Pharmaceuticals fr Human Use 1

Mdule 2.2 - Framewrk Disclaimer This presentatin includes the authrs views n thery and practice regarding estimands and sensitivity analysis in clinical trials. The presentatin des nt represent fficial guidance r plicy f authrities r industry and it des nt prvide additinal guidance beynd ICH E9(R1). The current slide deck reflects the cntent n the Draft ICH E9(R1) addendum up t the time f its publicatin fr public cnsultatin. Cntent may be changed as a result f the cnsultatin phase. 2

Legal Ntice ICH E9(R1) Step 2 Training Material Mdule 2.2 - Framewrk This presentatin is prtected by cpyright and may be used, reprduced, incrprated int ther wrks, adapted, mdified, translated r distributed under a public license prvided that ICH's cpyright in the presentatin is acknwledged at all times. In case f any adaptatin, mdificatin r translatin f the presentatin, reasnable steps must be taken t clearly label, demarcate r therwise identify that changes were made t r based n the riginal presentatin. Any impressin that the adaptin, mdificatin r translatin f the riginal presentatin is endrsed r spnsred by the ICH must be avided. The presentatin is prvided "as is" withut warranty f any kind. In n event shall the ICH r the authrs f the riginal presentatin be liable fr any claim, damages r ther liability arising frm the use f the presentatin. The abve-mentined permissins d nt apply t cntent supplied by third parties. Therefre, fr dcuments where the cpyright vests in a third party, permissin fr reprductin must be btained frm this cpyright hlder. 3

Intrductin nte ICH E9(R1) Step 2 Training Material Mdule 2.2 - Framewrk These slides were prduced as training material t accmpany the Draft ICH E9(R1) addendum. The intentin is t supprt the scientific cmmunity in the cmprehensin f a new framewrk t define estimands based n the trial bjective and cnsidering intercurrent events. Fr this purpse, mst f the cntent f this addendum is presented in a practical fashin, accmpanied by examples and case studies dealing with estimands and sensitivity analysis in clinical trials, based n the experience f Expert Wrking Grup members. The training material is divided in three main mdules: mdule 1 (summary), mdule 2 (cmprehensive slide deck) and mdule 3 (generic example). Mdule 2 is cmpsed by 6 submdules that crrespnd t sectins A.1 t A.6 f the addendum. 4

Mdule 2.2 - Framewrk Training mdules Mdule 1: Summary Mdule 2: Cmprehensive slide deck Mdule 2.1: Intrductin Mdule 2.2: Framewrk Mdule 2.3: Estimands Mdule 2.4: Impact n trial design and cnduct Mdule 2.5: Impact n trial analysis Mdule 2.6: Dcumenting estimands and sensitivity analysis Mdule 3: Generic example 5

Mdule 2.2 - Framewrk ICH E9(R1) Table f Cntents A.1. Purpse and Scpe A.2. A Framewrk t Align Planning, Design, Cnduct, Analysis and Interpretatin A.3. Estimands A.3.1. Descriptin A.3.2. Strategies fr Addressing Intercurrent Events Mdule 2.3 A.3.3. Cnstructin f Estimands - A.3.3.1. General Cnsideratins - A.3.3.2. Cnsideratins f Therapeutic and Experimental Cntext A.4. Impact n Trial Design and Cnduct Mdule 2.4 A.5. Impact n Trial Analysis A.5.1. Main Estimatin Mdule 2.1 A.5.2. Sensitivity Analysis - A.5.2.1. Rle f Sensitivity Analysis - A.5.2.2. Chice f Sensitivity Analysis A.5.3. Supplementary Analysis A.6. Dcumenting Estimands and Sensitivity Analysis A.7. A Generic Example A.7.1. One Intercurrent Event Mdule 3 A.7.2. Tw Intercurrent Events Glssary Mdule 2.5 Mdule 2.6 Mdule 2.2 6

Mdule 2.2 - Framewrk Outline f Mdule 2.2 A new framewrk fr clinical trials Alignment between trial bjective, design, planning, cnduct, analysis and interpretatin Hw this framewrk can imprve discussins between spnsrs and regulatrs n the suitability f designs and the interpretatin f results 7

Mdule 2.2 - Framewrk A new framewrk Clear trial bjectives shuld be translated int key scientific questins f interest by defining suitable estimands. Clear trial bjective Key scientific questin f interest Estimand ( what is t be estimated ) 8

Mdule 2.2 - Framewrk Aligning target f estimatin, methd f estimatin, and sensitivity analysis, fr a given trial bjective Trial bjective Estimand Target f estimatin = WHAT Methd f estimatin = HOW Main estimatr Main estimate 9

Mdule 2.2 - Framewrk Aligning target f estimatin, methd f estimatin, and sensitivity analysis, fr a given trial bjective Methd f estimatin = HOW Trial bjective Estimand Main estimatr Main estimate Target f estimatin = WHAT The main estimatr will be underpinned by certain assumptins. T explre the rbustness f inferences frm the main estimatr t deviatins frm its underlying assumptins, sensitivity analysis shuld be cnducted, in frm f ne r mre analyses, targeting the same estimand. 10

Mdule 2.2 - Framewrk Aligning target f estimatin, methd f estimatin, and sensitivity analysis, fr a given trial bjective Trial bjective Estimand Target f estimatin = WHAT Methd f estimatin = HOW Main estimatr Sensitivity estimatr 1 Sensitivity estimatr 2 Main estimate Sensitivity estimate 1 Sensitivity estimate 2 Sensitivity analysis 11

Mdule 2.2 - Framewrk Aligning target f estimatin, methd f estimatin, and sensitivity analysis, fr a given trial bjective In general, it is imprtant t prceed sequentially. The trial bjective shuld determine the chice f estimands and the estimands shuld determine the chice f estimatrs, nt the reverse. Trial bjective Estimand Main estimatr Main estimate Where significant issues exist t derive a reliable estimate fr a particular estimand, the trial bjectives need t be recnsidered frm tp-dwn t main estimatr (green arrws). The main estimatr shuld never define the trial bjective frm bttm-up (red arrws). 12

A new framewrk ICH E9(R1) Step 2 Training Material Mdule 2.2 - Framewrk Clinical Trial Apprpriate estimands will be the main determinant fr aspects f trial design, cnduct and analysis. 13

A new framewrk ICH E9(R1) Step 2 Training Material Mdule 2.2 - Framewrk A cmmn language and cmmn understanding f this framewrk will help spnsrs planning trials and regulatrs in their reviews, enhancing the interactins between these parties when discussing the suitability f designs, and the interpretatin f results, t supprt drug licensing. Regulatrs ESTIMAND @$#! + ESTIMAND &?% Spnsr 14

Next mdule: 2.3. Estimands Descriptin, strategies and cnstructin f estimands Internatinal Cnference n Harmnisatin f Technical Requirements fr Registratin f Pharmaceuticals fr Human Use 15

Draft (Step 2) guideline ICH E9(R1) Estimands and Sensitivity Analysis in Clinical Trials Training mdule 2.3: Estimands Addendum t ICH E9 Statistical Principles fr Clinical Trials ICH E9(R1) Expert Wrking Grup June 2018 Internatinal Cuncil fr Harmnisatin f Technical Requirements fr Registratin f Pharmaceuticals fr Human Use 1

Mdule 2.3 - Estimands Disclaimer This presentatin includes the authrs views n thery and practice regarding estimands and sensitivity analysis in clinical trials. The presentatin des nt represent fficial guidance r plicy f authrities r industry and it des nt prvide additinal guidance beynd ICH E9(R1). The current slide deck reflects the cntent n the Draft ICH E9(R1) addendum up t the time f its publicatin fr public cnsultatin. Cntent may be changed as a result f the cnsultatin phase. 2

Legal Ntice ICH E9(R1) Step 2 Training Material Mdule 2.3 - Estimands This presentatin is prtected by cpyright and may be used, reprduced, incrprated int ther wrks, adapted, mdified, translated r distributed under a public license prvided that ICH's cpyright in the presentatin is acknwledged at all times. In case f any adaptatin, mdificatin r translatin f the presentatin, reasnable steps must be taken t clearly label, demarcate r therwise identify that changes were made t r based n the riginal presentatin. Any impressin that the adaptin, mdificatin r translatin f the riginal presentatin is endrsed r spnsred by the ICH must be avided. The presentatin is prvided "as is" withut warranty f any kind. In n event shall the ICH r the authrs f the riginal presentatin be liable fr any claim, damages r ther liability arising frm the use f the presentatin. The abve-mentined permissins d nt apply t cntent supplied by third parties. Therefre, fr dcuments where the cpyright vests in a third party, permissin fr reprductin must be btained frm this cpyright hlder. 3

Intrductin nte ICH E9(R1) Step 2 Training Material Mdule 2.3 - Estimands These slides were prduced as training material t accmpany the Draft ICH E9(R1) addendum. The intentin is t supprt the scientific cmmunity in the cmprehensin f a new framewrk t define estimands based n the trial bjective and cnsidering intercurrent events. Fr this purpse, mst f the cntent f this addendum is presented in a practical fashin, accmpanied by examples and case studies dealing with estimands and sensitivity analysis in clinical trials, based n the experience f Expert Wrking Grup members. The training material is divided in three main mdules: mdule 1 (summary), mdule 2 (cmprehensive slide deck) and mdule 3 (generic example). Mdule 2 is cmpsed by 6 submdules that crrespnd t sectins A.1 t A.6 f the addendum. 4

Mdule 2.3 - Estimands Training mdules Mdule 1: Summary Mdule 2: Cmprehensive slide deck Mdule 2.1: Intrductin Mdule 2.2: Framewrk Mdule 2.3: Estimands Mdule 2.4: Impact n trial design and cnduct Mdule 2.5: Impact n trial analysis Mdule 2.6: Dcumenting estimands and sensitivity analysis Mdule 3: Generic example 5

ICH E9(R1) Table f Cntents A.1. Purpse and Scpe A.2. A Framewrk t Align Planning, Design, Cnduct, Analysis and Interpretatin A.3. Estimands A.3.1. Descriptin A.3.2. Strategies fr Addressing Intercurrent Events A.3.3. Cnstructin f Estimands - A.3.3.1. General Cnsideratins - A.3.3.2. Cnsideratins f Therapeutic and Experimental Cntext A.4. Impact n Trial Design and Cnduct Mdule 2.4 A.5. Impact n Trial Analysis A.5.1. Main Estimatin A.5.2. Sensitivity Analysis - A.5.2.1. Rle f Sensitivity Analysis - A.5.2.2. Chice f Sensitivity Analysis A.5.3. Supplementary Analysis ICH E9(R1) Step 2 Training Material Mdule 2.3 - Estimands Mdule 2.1 A.6. Dcumenting Estimands and Sensitivity Analysis A.7. A Generic Example A.7.1. One Intercurrent Event Mdule 3 A.7.2. Tw Intercurrent Events Glssary Mdule 2.5 Mdule 2.6 Mdule 2.3 Mdule 2.2 6

Mdule 2.3 - Estimands Outline f Mdule 2.3 Fur attributes (A thrugh D) that tgether describe an estimand Strategies fr addressing intercurrent events General cnsideratins fr the cnstructin f estimands Therapeutic and experimental cnsideratins fr the cnstructin f estimands 7

Mdule 2 A.3.1. Descriptin A.3.2 Strategies fr addressing intercurrent events A.3.3 Cnstructin f estimands 8

Mdule 2.3 - Estimands Quantificatin f treatment effect A central questin fr drug develpment and licensing... Hw des the utcme f a treatment cmpares t what wuld have happened t the same subjects under different treatment cnditins? e.g. had they nt received the treatment r had they received a different treatment. Intercurrent events need t be cnsidered. The ccurrence f intercurrent events may depend n treatment cnditins (e.g. had they nt received the treatment r had they received a different treatment) which can impact data cllected t assess clinical utcmes. An estimand defines in detail what needs t be estimated t address a specific scientific questin f interest. A descriptin f an estimand includes fur attributes. 9

Mdule 2.3 - Estimands Descriptin f an estimand Ppulatin Variable Ppulatin-level summary % The descriptin f an estimand will nt be cmplete withut reflecting hw ptential intercurrent events are addressed in the scientific questin f interest. Rescue medicatin Death 10

Mdule 2.3 - Estimands A. Ppulatin Patients targeted by the scientific questin B. Variable (r endpint) C. Intercurrent event The specificatin f hw t accunt fr intercurrent events t reflect the scientific questin f interest D. Ppulatin-level summary fr the variable Prvides, as required, a basis fr a cmparisn between treatment cnditins Measure(s) required t address the scientific questin (t be btained fr each patient) 11

Mdule 2.3 - Estimands A. Ppulatin B. Variable Tgether these attributes describe the C. Intercurrent event The specificatin f hw t accunt fr intercurrent events t reflect the scientific questin f interest Patients targeted by the scientific questin (r endpint) estimand Measure(s) required t address the scientific questin (t be btained fr each D. patient) defining the target Ppulatin-level f estimatin. summary fr the variable Prvides, as required, a basis fr a cmparisn between treatment cnditins 12

Mdule 2.3 - Estimands Descriptin f an estimand A. Ppulatin Patients targeted by the scientific questin The ppulatin is typically characterised thrugh inclusin/exclusin criteria in the study prtcl. In sme cases a subgrup is f interest defined by a baseline characteristic; defined in terms f a ptential intercurrent event, fr example, the stratum f patients wh can tlerate and remain n treatment. Nte the imprtant difference in hw the subgrup (baseline characteristics) and the stratum (ptential intercurrent event nce treatment is received) are defined. 13

Mdule 2.3 - Estimands Descriptin f an estimand B. Variable (r endpint) Measure(s) required t address the scientific questin (t be btained fr each patient) HbA1c: Glycated haemglbin AUC: Area under the curve HbA1c The variable typically cnsists f measurements taken: e.g. bld pressure; functins theref: e.g. change frm baseline t ne year in HbA1c; quantities related t bserved events: e.g. time f death, number f relapses. The variable may als be used t address intercurrent events such as discntinuatin f interventin using measurements taken prir t discntinuatin f treatment (e.g., AUC f HbA1c until discntinuatin); r using cmpsites (e.g., treatment failure defined as nn-respnse r treatment discntinuatin). 14

Mdule 2.3 - Estimands Descriptin f an estimand Check definitin f IE in Mdule 2.1 C. Intercurrent event The specificatin f hw t accunt fr intercurrent events t reflect the scientific questin f interest Rescue medicatin Intercurrent events can present in multiple frms and can affect the interpretatin f the variable. Clinical trials are typically faced with mre than ne type f intercurrent event. The set f intercurrent events fr cnsideratin will depend n the specific therapeutic setting and trial bjective. Death IE: Intercurrent event 15

Mdule 2.3 - Estimands Descriptin f an estimand Intercurrent events Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Treatment discntinuatin due t adverse events Treatment discntinuatin due t lack f efficacy Use f rescue medicatin Death Treatment switch Change in backgrund therapy Study discntinuatin? Randmisatin TIMELINE Data cllectin fr the variable 16

Mdule 2.3 - Estimands Descriptin f an estimand C. Intercurrent event The specificatin f hw t accunt fr intercurrent events t reflect the scientific questin f interest Rescue medicatin Death Hw t accunt fr intercurrent events? Fr example, accunting fr it as part f the variable (cmpsite strategy, while-n-treatment strategy); by disregarding its ccurrence / as part f the treatment (treatment-plicy strategy); as part f the ppulatin (principal stratificatin strategy) r; as mdel-based predictins under the riginally randmised treatment (hypthetical strategy). 17

Missing data ICH E9(R1) Step 2 Training Material Mdule 2.3 - Estimands Intercurrent events are nt a missing data prblem. Lss-t-fllw-up is nt generally regarded as an intercurrent event. Indeed missing data and lss-t-fllw-up are irrelevant t the cnstructin f the estimand. Missing data Data that wuld be meaningful fr the analysis f a given estimand but were nt cllected. They shuld be distinguished frm data that d nt exist r data that are nt cnsidered meaningful because f an intercurrent event. Missing data definitin, ICH E9(R1) Draft addendum 18

Mdule 2.3 - Estimands Descriptin f an estimand D. Ppulatin-level summary fr the variable Prvides, as required, a basis fr a cmparisn between treatment cnditins. HbA1c: Glycated haemglbin % It culd be, fr example, a mean r a prprtin r, pssibly, a hazard rate. In case f treatment cmparisns, examples are: the difference in mean change frm baseline t ne year in HbA1c, r the difference r rati in the prprtin f subjects meeting specified criteria, under tw different treatment cnditins. a hazard rati, t-year event-rate difference r restricted mean survival time difference. 19

Mdule 2.3 - Estimands A. Ppulatin Patients targeted by the scientific questin independently. D. Ppulatin-level summary fr the variable Prvides, as required, a basis fr a cmparisn between treatment cnditins B. Variable (r endpint) The estimand attributes A thrugh D are C. Intercurrent inter-related event and shuld nt be cnsidered The specificatin f hw t accunt fr intercurrent events t reflect the scientific questin f interest Measure(s) required t address the scientific questin (t be btained fr each patient) 20

Mdule 2 A.3.1. Descriptin A.3.2 Strategies fr addressing intercurrent events A.3.3 Cnstructin f estimands 21 21

Mdule 2.3 - Estimands Hw are ptential intercurrent events reflected in the scientific questin f interest? 4 Attributes Treatment plicy Cmpsite Hypthetical 5 Strategies Principal stratum While n treatment 22

Mdule 2.3 - Estimands Strategies fr addressing intercurrent events At least 5 Strategies t cnsider when addressing different intercurrent events. A strategy is chsen fr each intercurrent event t reflect the scientific questin f interest; different strategies can be used fr different intercurrent events. The relevance f each strategy will depend n the therapeutic and experimental cntext Nt all strategies will be equally acceptable fr regulatry decisin making! 23

Mdule 2.3 - Estimands Hw are ptential intercurrent events reflected in the scientific questin f interest? Let s take an example Drug X Indicated fr a chrnic, nn-lifethreatening disease Respnse t treatment: mnitred mnthly (cntinuus measurement). Main scientific questin: cmparisn f Drug X t placeb at mnth 6 best addressed by a randmised clinical trial. Intercurrent events: Use f placeb in the clinical trial is cnsidered ethical but nly if prvisin is made fr subjects t discntinue their treatment and use rescue medicatin due t lack f efficacy (after which it is still pssible t cllect data fr the variable). It is als pssible t cllect data after ther intercurrent events such as discntinuatin f treatment due t an adverse event, but nt fr intercurrent events such as death (cnsidered very unlikely in this setting). 24

Mdule 2.3 - Estimands If we assume that n intercurrent events ccur (unrealistic) example Estimand: Difference in means between treatment cnditins in the change frm baseline t mnth 6 in the designated measurement in the targeted patient ppulatin. Let s cnsider 3 patients and what culd happen if n intercurrent events ccurred (this slide), and under each strategy (fllwing slides). Patient 1 Patient 2 Baseline measurement 6 mnths N intercurrent event Treatment cmplete N intercurrent event Treatment cmplete Legend: 6-mnth value has been cllected Part f patient time curse cnsidered 25

Mdule 2.3 - Estimands Nw assuming that there are intercurrent events 26

Mdule 2.3 - Estimands 1. Treatment-plicy strategy Strategy labels are just fr ease f reference In this strategy, the ccurrence f the intercurrent event is irrelevant: the data cllected fr the variable f interest are used regardless f whether r nt the intercurrent event ccurs. Fr example, when specifying hw t accunt fr rescue medicatin as an intercurrent event, ccurrence f the intercurrent event is ignred (disregarded) and the bservatins cllected after use f rescue medicatin fr the variable f interest are used. In general, this strategy cannt be implemented when values fr the variable after the intercurrent event d nt exist fr all subjects. Fr example, an estimand based n this strategy cannt be cnstructed with respect t a variable that cannt be measured due t death. 27

Mdule 2.3 - Estimands 1. Treatment-plicy strategy - example Estimand: Difference in means between treatment cnditins in the change frm baseline t mnth 6 in the targeted patient ppulatin, regardless f whether rescue medicatin was used. If rescue medicatin (intercurrent event) is used Baseline measurement 6 mnths Patient 1 Patient 2 Patient 3 Start f rescue medicatin? N intercurrent event Treatment cmplete Intercurrent event N intercurrent event Treatment incmplete Legend:? Applying the treatment plicy strategy Patient 1 Patient 2 Patient 3 6-mnth value has been cllected 6-mnth value has nt been cllected, data are missing Patient lst t fllw-up Start f rescue medicatin? N intercurrent event Treatment cmplete 6-mnth value still cnsidered; IE disregarded N intercurrent event Patient included but data are missing Intercurrent event (IE) - in grey when disregarded Part f patient time curse cnsidered 28 Part f patient time curse nt bserved and needs t be imputed/predicted

Mdule 2.3 - Estimands 2. Cmpsite strategy Strategy labels are just fr ease f reference In this strategy, the ccurrence f the intercurrent event is taken t be a cmpnent f the variable, i.e. the intercurrent event is integrated with ne r mre ther measures f clinical utcme as the variable f interest. Fr example, the variable might be defined as a cmpsite f n use f rescue medicatin and a favurable clinical utcme, i.e. patient becmes a nn-respnder if they need t use rescue medicatin. Alternatively, fr a numerical variable, experiencing an intercurrent event might be ascribed an extreme unfavurable value and a suitable summary measure selected (e.g. median r trimmed mean). 29

Mdule 2.3 - Estimands 2. Cmpsite strategy Smetimes an event being cnsidered as intercurrent is itself the mst meaningful variable that can be measured fr quantifying the treatment effect f interest. This can be the case with death: the fact that a subject has died may be much mre meaningful than bservatins befre death, and bservatins after death will nt exist. 30

Mdule 2.3 - Estimands 2. Cmpsite strategy - example The estimand assesses the treatment effect based n a clinically meaningful change in the designated measurement in patients wh d nt take rescue medicatin. Applying the cmpsite strategy Baseline measurement 6 mnths Patient 1 Patient 2 Start f rescue medicatin This is nt the nly way t address intercurrent events using the cmpsite strategy; ther ways include trimmed means r quantiles. N intercurrent event + clinically meaningful change Intercurrent event; N need t cllect 6-mnthvalue Patient 3 N intercurrent event + change nt clinically meaningful Legend: 6-mnth value has been cllected and utcme is psitive 6-mnth value has been cllected and utcme is negative Part f patient time curse cnsidered Part f patient time curse nt cnsidered. Intercurrent event as part f the cmpsite variable. Time f intercurrent event marks the end f data cllectin. 31

Mdule 2.3 - Estimands 3. Hypthetical strategy Strategy labels are just fr ease f reference In this strategy, a hypthetical scenari is envisaged in which the intercurrent event wuld nt ccur. Fr example, when rescue medicatin must be made available fr ethical reasns, a treatment effect f interest might cncern the utcmes if rescue medicatin had nt been available. The value t reflect that scientific questin f interest is that which the variable wuld have taken in the hypthetical scenari defined. In this example, the value t be cnsidered wuld have been the ne cllected if patients had nt had rescue medicatin available. By definitin, such value cannt be bserved but will need t be implicitly r explicitly predicted / imputed. 32

Mdule 2.3 - Estimands 3. Hypthetical strategy Imprtant t keep in mind The hypthetical scenari shuld cnsider reasnable situatins, e.g. a scenari where a txic medicine is cnsidered t be nn-txic is nt usually relevant fr decisin making. Care is required t precisely describe the hypthetical cnditins reflecting the scientific questin f interest in the cntext f the specific trial. 33

Mdule 2.3 - Estimands 3. Hypthetical strategy - example The estimand assesses the treatment effect in an alternative, hypthetical setting where rescue medicatin was nt available t subjects. Applying the hypthetical strategy Baseline measurement 6 mnths Patient 1 Patient 2 Start f rescue medicatin N intercurrent event - Treatment cmplete N need t cllect 6- mnth value Legend: 6-mnth value has been cllected and utcme is psitive N need t cllect 6-mnth value Part f patient time curse cnsidered Part f patient time curse nt bserved and needs t be imputed/predicted Intercurrent event hypthetically nt present. Time f intercurrent event marks the end f data cllectin. 34

Mdule 2.3 - Estimands 4. Principal stratum strategy Strategy labels are just fr ease f reference In this strategy, principal stratificatin is defined by a patient s ptential intercurrent events n either r bth treatments. It is a subset f the brader ppulatin; Fr example, patients wh can tlerate and remain n either treatment; the target ppulatin is then cnsidered t be the stratum in which an intercurrent event wuld nt ccur in either grup; A specific stratum r several strata culd be f interest, e.g. adherence t treatment regardless f adherence t cntrl. The scientific questin f interest relates t the treatment effect nly within that stratum / thse strata. Effects in principal strata shuld be clearly distinguished frm any type f subgrup r per-prtcl analyses where membership is based n the trial data. 35

Mdule 2.3 - Estimands 4. Principal stratum strategy It is nt pssible in general t identify these subjects directly, either in advance f the trial since the ccurrence f the intercurrent event cannt be perfectly predicted, r based n the data frm a parallel-grup randmised cntrlled trial because each patient will be bserved n ne treatment nly. 36

Placeb ICH E9(R1) Step 2 Training Material Mdule 2.3 - Estimands 4. Principal stratum strategy - example A given patient can receive either treatment r placeb. When receiving treatment sme patients will use rescue medicatin, thers nt. The same applies fr patients n placeb. The estimand assesses the treatment effect in the stratum f patients which wuld nt use rescue medicatin regardless t which treatment arm they wuld be assigned. Patients fall int exactly ne f these fur strata: S 00 : stratum f patients wh require rescue medicatin independently f treatment r placeb; S 01 : stratum f patients wh require rescue medicatin n placeb and d nt require it n treatment; S 10 : stratum f patients wh require rescue medicatin n treatment and d nt require it n placeb; S 11 : stratum f patients wh d nt require rescue medicatin independently f treatment r placeb. This is the nly stratum where the intercurrent event f use f rescue medicatin des nt ccur. A ppulatin can be defined by membership f ne r mre f these strata. Treatment Rescue medicatin N rescue medicatin Rescue medicatin S 00 S 01 N rescue medicatin S 10 S 11 37

Mdule 2.3 - Estimands 5. While n treatment strategy Strategy labels are just fr ease f reference In this strategy, respnse t treatment prir t the ccurrence f the intercurrent event is f interest. This culd be, fr example, the last measurement befre death (r ther intercurrent event) r at a pre-defined time-pint, whichever cmes first. If a variable is measured repeatedly, its values up t the time f the intercurrent event may be cnsidered t accunt fr the intercurrent event, rather than the value at the same fixed time pint fr all subjects. Fr example: - subjects with a terminal illness may discntinue a purely symptmatic treatment because they die, yet it is relevant t measure the success f the treatment based n the effect n symptms befre death. - subjects might discntinue treatment, and in sme circumstances it will be f interest t assess the risk f an adverse drug reactin during the perid f adherence t treatment. 38

Mdule 2.3 - Estimands 5. While n treatment strategy - example The estimand assesses the treatment effect n the variable measurement. The variable chsen here addresses the utcmes while being n treatment, i.e. befre start f rescue medicatin. Applying the while n treatment strategy Baseline measurement 6 mnths Patient 1 Patient 2 Start f rescue medicatin Difficulties arise in deriving an estimate that is reliable fr inference when fllw-up times are different between grups. N intercurrent event - Treatment cmplete Data cllected befre the ccurrence f the intercurrent event will be used t determine the variable Legend: End pint value has been cllected Part f patient time curse cnsidered Part f patient time curse nt cnsidered Time f intercurrent event marks end f data cllectin (merged with green dt). 39

Mdule 2.3 - Estimands Multiple intercurrent events example The strategies presented befre can be used alne r in cmbinatin t address multiple different intercurrent events. Returning t the example: Drug X, indicated fr the treatment f a chrnic, nn-life-threatening disease. Respnse t treatment: mnitred mnthly (cntinuus measurement). Main scientific questin: cmparisn f drug X t placeb at mnth 6. Intercurrent events: Use f placeb in the clinical trial is cnsidered ethical but nly if prvisin is made fr subjects t discntinue their treatment and use rescue medicatin due t lack f efficacy (after which it is still pssible t cllect data fr the variable). This is als the case after ther intercurrent events such as discntinuatin f treatment due t an adverse event, but nt fr intercurrent events such as death (cnsidered very unlikely in this setting). Secnd intercurrent event 40

Mdule 2.3 - Estimands Multiple intercurrent events example Treatment-plicy strategy t accunt fr bth intercurrent events: Estimand: Difference in means between treatment cnditins in the change frm baseline t mnth 6 in the targeted patient ppulatin, regardless f whether r nt rescue medicatin had been used and regardless f treatment discntinuatin due any adverse events. Applying treatment-plicy x treatment-plicy strategy Patient 1 Patient 2 Patient 3 Baseline Discntinuatin due t AE measurement Start f rescue medicatin 6 mnths N intercurrent event Treatment cmplete 6-mnth value still cnsidered; IE disregarded 6-mnth value still cnsidered; IE disregarded Legend: 6-mnth value has been cllected Part f patient time curse cnsidered IE Intercurrent events (in grey when disregarded) Intercurrent event 41

Legend: Multiple intercurrent events example 6-mnth value has been cllected N need t cllect 6-mnth value Part f patient time curse cnsidered ICH E9(R1) Step 2 Training Material Mdule 2.3 - Estimands Cmbinatin f hypthetical strategy and treatmentplicy strategy t accunt fr the tw intercurrent events: Estimand: Difference in means between treatment cnditins in the change frm baseline t mnth 6 in the targeted patient ppulatin had rescue medicatin nt been made available t subjects prir t mnth six and regardless f study treatment discntinuatin due t an adverse event. Applying hypthetical x treatment-plicy strategy Patient 1 Patient 2 Patient 3 Baseline Discntinuatin due t AE measurement Start f t rescue medicatin 6 mnths N intercurrent event Treatment cmplete N need t cllect 6- mnth value 6-mnth value still cnsidered; intercurrent event disregarded Part f patient time curse nt bserved and needs t be imputed/predicted 42 Intercurrent event hypthetically nt present. Time f intercurrent event marks the end f data cllectin. Intercurrent event (in grey when disregarded)

Mdule 2.3 - Estimands Multiple intercurrent events The definitin f a clinically meaningful estimand needs t encmpass all intercurrent events that are likely t ccur and are clinically relevant in a given clinical trial setting. The descriptin f the treatment effect being targeted cannt be fully understd withut cnsidering hw t address the intercurrent events in the estimand. Taking int accunt intercurrent events is equally imprtant fr the chices made abut the design, cnduct and statistical analysis. Cnsidering the five strategies discussed abve, all pssible cmbinatins f strategies fr tw types f intercurrent events can be cnsidered, althugh nt all cmbinatins will be clinically relevant. 43

Mdule 2.3 - Estimands Further cnsideratins n strategies The chice f strategies must be the bject f a multidisciplinary discussin in particular, between spnsrs and regulatrs. The descriptin f the preferred strategy fr handling each intercurrent event shuld be precisely defined in the study prtcl, as well as the reasns fr that chice. 44

Mdule 2 A.3.1. Descriptin A.3.2 Strategies fr addressing intercurrent events A.3.3 Cnstructin f estimands 45

Mdule 2 A.3.3 Cnstructin f estimands A.3.3.1 General cnsideratins 46 46

Mdule 2.3 - Estimands Cnsidering intercurrent events Clinical Trial Each intercurrent event that may ccur in the clinical trial and that will affect the interpretatin f the results f the trial needs t be cnsidered explicitly in the cnstructin f the estimand. This will Unambiguusly describe the treatment effect f interest; Prmte the relevance f the treatment effect described t patients and physicians. 47

Mdule 2.3 - Estimands Cnsidering intercurrent events Clinical Trial But It s nt always pssible t fresee every relevant kind f intercurrent event at the planning stage. Trial reprting shuld then discuss nt nly the way unfreseen intercurrent events were handled in the analysis but als the effect n what the revised analysis estimates. 48

Mdule 2.3 - Estimands Cnstructin f an estimand It is a multi-disciplinary undertaking and shuld be the subject f discussin between spnsrs and regulatrs. Clinicians Spnsrs Objectives, estimands and designs f prspective CTs Regulatrs Clinical trial design and cnduct δ α Statisticians Other disciplines 49 CTs: Clinical Trials

Mdule 2.3 - Estimands Cnstructin f an estimand The cnstructin f an estimand shuld be: Cnsequent t the trial bjectives and shuld precede chices relating t data cllectin and analytic appraches; Clinically interpretable, in terms f the ppulatin and endpint, but als in terms f the strategies t accunt fr intercurrent events and, finally, the summary measure; Duly justified cnsidering the therapeutic setting and the treatment gals f the interventin, frm which the key scientific questins f interest can be derived. 50

Mdule 2.3 - Estimands Aviding r ver-simplifying this prcess risks misalignment between trial bjectives, trial design, data cllectin and statistical analysis! Clinical Trial 51

Mdule 2.3 - Estimands Cnstructin f an estimand: Hw d we d it? Trial bjective Estimand Main estimatr Main estimate Where significant issues exist t derive a reliable estimate fr a particular estimand, the trial bjectives need t be re-cnsidered frm tp-dwn t main estimatr (green arrws). The main estimatr shuld never define the trial bjective frm bttm-up (red arrws). 52

Mdule 2 A.3.3 Cnstructin f estimands A.3.3.2. Cnsideratins f Therapeutic and Experimental Cntext 53

Mdule 2.3 - Estimands Imprtant t keep in mind The aim f the EWG was t develp and present a framewrk t facilitate these imprtant discussins and nt t give therapy-area specific recmmendatins. The pints raised n subsequent slides are fr reflectin and are nt designed t influence the preferred chice f strategy. EWG: ICH E9(R1) Expert Wrking Grup 54

What can influence the cnstructin f the estimand? Disease setting: Are alternative treatment ptins available? - E.g. fr glycaemic cntrl in Type II diabetes mellitus multiple therapies are licensed. Can individual respnse t treatment be mnitred ver time? - E.g. in hypertensin, bld pressure is mnitred frequently t assess respnse t treatment, inadequate respnse wuld be detected in a timely manner. Aim f treatment: ICH E9(R1) Step 2 Training Material Mdule 2.3 - Estimands Cntrlling symptms? - E.g. pain cntrl in the treatment f migraine. Mdifying the curse f disease r preventin f disease? - E.g. delay t the nset f dementia in prdrmal Alzheimer s disease. 55

Mdule 2.3 - Estimands Ntes abut the treatment-plicy strategy Rescue medicatin The treatment fails but values after rescue are still cnsidered (the intercurrent event is disregarded) Treatment plicy strategies might be mre generally acceptable t supprt regulatry decisin making. Als in settings where: Estimands based n alternative strategies might be cnsidered f greater clinical interest; N ther estimatrs can supprt a reliable estimate r rbust inference. Treatment plicy strategies might ffer the pssibility t btain a reliable estimate f a treatment effect that is still relevant. The resulting estimates shuld be presented alng with a discussin f the limitatins, in terms f trial design, data cllectin r statistical analysis. 56

Mdule 2.3 - Estimands Ntes abut the cmpsite strategy Rescue medicatin The treatment fails: patient cnsidered as nn-respnder N need t cllect data after use f rescue medicatin Example: select a binary variable. Respnse is based n a predefined threshld f change in scre in the absence f the intercurrent event. Patients wh experience the intercurrent event (rescue medicatin in this case) will be cnsidered nn-respnders. This dichtmisatin f cntinuus scres wuld result in a different variable, and thus a different estimand. 57

Mdule 2.3 - Estimands Ntes abut the hypthetical strategy Rescue medicatin N need t cllect the end pint value Part f patient time curse nt bserved and imputed/predicted Define the scientific questin f interest based n the effect if rescue medicatin had nt been available. The hypthetical cnditins described must therefre be justified fr the quantificatin f an interpretable treatment effect that is relevant t the use f the medicine in clinical practice. If the intercurrent event als depends n the reasn fr the event ccurring, bth event and reasn have t be defined mre precisely and recrded accurately in the clinical trial. 58

Mdule 2.3 - Estimands The experimental situatin shuld always be cnsidered! In particular, the chice f the cntrl arm might influence the manner in which rescue r ther cncmitant medicatins are permitted in the trial. 59

Next mdule: 2.4. Impact n trial design and cnduct Implicatins n design and cnduct f clinical trials and in the perfrmance f statistical analyses Internatinal Cnference n Harmnisatin f Technical Requirements fr Registratin f Pharmaceuticals fr Human Use 60

Draft (Step 2) guideline ICH E9(R1) Estimands and Sensitivity Analysis in Clinical Trials Training mdule 2.4: Impact n trial design and cnduct Addendum t ICH E9 Statistical Principles fr Clinical Trials ICH E9(R1) Expert Wrking Grup June 2018 Internatinal Cuncil fr Harmnisatin f Technical Requirements fr Registratin f Pharmaceuticals fr Human Use 1

Mdule 2.4 Impact n trial design and cnduct Disclaimer This presentatin includes the authrs views n thery and practice regarding estimands and sensitivity analysis in clinical trials. The presentatin des nt represent fficial guidance r plicy f authrities r industry and it des nt prvide additinal guidance beynd ICH E9(R1). The current slide deck reflects the cntent n the Draft ICH E9(R1) addendum up t the time f its publicatin fr public cnsultatin. Cntent may be changed as a result f the cnsultatin phase. 2

Legal Ntice ICH E9(R1) Step 2 Training Material Mdule 2.4 Impact n trial design and cnduct This presentatin is prtected by cpyright and may be used, reprduced, incrprated int ther wrks, adapted, mdified, translated r distributed under a public license prvided that ICH's cpyright in the presentatin is acknwledged at all times. In case f any adaptatin, mdificatin r translatin f the presentatin, reasnable steps must be taken t clearly label, demarcate r therwise identify that changes were made t r based n the riginal presentatin. Any impressin that the adaptin, mdificatin r translatin f the riginal presentatin is endrsed r spnsred by the ICH must be avided. The presentatin is prvided "as is" withut warranty f any kind. In n event shall the ICH r the authrs f the riginal presentatin be liable fr any claim, damages r ther liability arising frm the use f the presentatin. The abve-mentined permissins d nt apply t cntent supplied by third parties. Therefre, fr dcuments where the cpyright vests in a third party, permissin fr reprductin must be btained frm this cpyright hlder. 3

Intrductin nte ICH E9(R1) Step 2 Training Material Mdule 2.4 Impact n trial design and cnduct These slides were prduced as training material t accmpany the Draft ICH E9(R1) addendum. The intentin is t supprt the scientific cmmunity in the cmprehensin f a new framewrk t define estimands based n the trial bjective and cnsidering intercurrent events. Fr this purpse, mst f the cntent f this addendum is presented in a practical fashin, accmpanied by examples and case studies dealing with estimands and sensitivity analysis in clinical trials, based n the experience f Expert Wrking Grup members. The training material is divided in three main mdules: mdule 1 (summary), mdule 2 (cmprehensive slide deck) and mdule 3 (generic example). Mdule 2 is cmpsed by 6 submdules that crrespnd t sectins A.1 t A.6 f the addendum. 4

Mdule 2.4 Impact n trial design and cnduct Training mdules Mdule 1: Summary Mdule 2: Cmprehensive slide deck Mdule 2.1: Intrductin Mdule 2.2: Framewrk Mdule 2.3: Estimands Mdule 2.4: Impact n trial design and cnduct Mdule 2.5: Impact n trial analysis Mdule 2.6: Dcumenting estimands and sensitivity analysis Mdule 3: Generic example 5

Mdule 2.4 Impact n trial design and cnduct ICH E9(R1) Table f Cntents A.1. Purpse and Scpe A.2. A Framewrk t Align Planning, Design, Cnduct, Analysis and Interpretatin A.3. Estimands A.3.1. Descriptin A.3.2. Strategies fr Addressing Intercurrent Events A.3.3. Cnstructin f Estimands - A.3.3.1. General Cnsideratins - A.3.3.2. Cnsideratins f Therapeutic and Experimental Cntext A.4. Impact n Trial Design and Cnduct Mdule 2.4 A.5. Impact n Trial Analysis A.5.1. Main Estimatin Mdule 2.1 A.5.2. Sensitivity Analysis - A.5.2.1. Rle f Sensitivity Analysis - A.5.2.2. Chice f Sensitivity Analysis A.5.3. Supplementary Analysis A.6. Dcumenting Estimands and Sensitivity Analysis A.7. A Generic Example A.7.1. One Intercurrent Event Mdule 3 A.7.2. Tw Intercurrent Events Glssary Mdule 2.5 Mdule 2.6 Mdule 2.3 Mdule 2.2 6

Mdule 2.4 Impact n trial design and cnduct Outline f Mdule 2.4 ICH E9(R1) will have implicatins n hw we design and cnduct clinical trials and perfrm statistical analyses Identificatin f estimand(s) at the design stage requires infrmed discussin with all stakehlders Certain estimands may require r benefit frm nn-standard designs and/r endpints 7

Mdule 2.4 Impact n trial design and cnduct Impact n Trial Design The design f a trial needs t be aligned t the chice f the estimand r estimands that reflect the primary trial bjectives and which will frm the basis t establish whether thse bjectives have been met. ICH E6 lists imprtant aspects f trial design that shuld be stated in the prtcl. Alignment with the trial bjective and estimand shuld be sught when cnsidering, fr example: The type f trial (e.g., duble-blind, placeb-cntrlled, parallel design); Duratin f subject participatin, discntinuatin criteria fr individual subjects, subject withdrawal criteria, medicatins permitted befre and during the trial; Methds fr timing and assessing variables, prcedures fr mnitring subject cmpliance, specificatin f efficacy and/r safety parameters; The methds and timing fr assessing, recrding, and analysing efficacy and/r safety parameters. 8

Mdule 2.4 Impact n trial design and cnduct Impact n Data Cllectin The agreed estimand dictates the data that need t be cllected during the trial. Different estimands might have different requirements regarding data cllectin. Each trial is likely t have multiple estimands, which means that data cllectin shuld be determined by the need t address them all. The amunt f patient withdrawals frm the study desn t influence the relevance f a particular strategy r estimand, but the impact f ptential patient withdrawals n estimatin needs careful cnsideratin. 9

Mdule 2.4 Impact n trial design and cnduct Impact n Data Cllectin An estimand based n a treatment-plicy strategy requires the value fr the variable t be cllected fr all subjects regardless (i.e. befre and after) f the intercurrent event. Start f rescue medicatin Value at final time-pint cnsidered; Intercurrent event disregarded In cntrast, an estimand in which the variable is defined as a cmpsite f n use f rescue medicatin and a favurable clinical utcme des nt require cllectin f data after the use f rescue medicatin. Start f rescue medicatin Intercurrent event determines failure. N need t cllect value at the final time-pint Effrts shuld be made t cllect all data that are relevant t supprt a statistical analysis aligned t the estimands f interest. 10

Mdule 2.4 Impact n trial design and cnduct Design Optins Several nn-standard designs are available that can be aligned t the chice f the estimand r estimands that reflect the primary trial bjectives: Enrichment designs: incrprate a run-in perid in which a subset f the subjects are selected based n their likelihd f experiencing the intercurrent event f interest, e.g. the ppulatin f interest culd be patients wh tlerate the experimental treatment. Randmised withdrawal designs: all subjects are initially treated with the experimental treatment, then, subjects that have a psitive respnse t the experimental treatment are randmly selected either t remain n the experimental treatment r t be switched t placeb r alternative cntrl. Titratin designs: permit flexible dsing t accmmdate individual differences in drug respnse which may allw mre subjects t cntinue n the assigned treatment by reducing number f intercurrent events. Nt all types f design may be acceptable in all situatins. Apprpriate dialgues with regulatrs may be necessary. 11

Other imprtant aspects A precise descriptin f the estimand(s) f interest shuld infrm sample size calculatins. Fr summarising data acrss clinical trials, the same estimand shuld be cnsidered at the planning stage f the cntributing trials. Similar cnsideratins apply, fr example, t the design f a metaanalysis r the use f external cntrl grups fr the interpretatin f single-arm trials. A trial may have multiple bjectives translated int multiple estimands. ICH E9(R1) Step 2 Training Material Mdule 2.4 Impact n trial design and cnduct The multiplicity adjustments fr multiple endpints r multiple sub-grups can equally be applied t multiple estimands. 12

Next mdule: 2.5. Impact n trial analysis Rle and chice f sensitivity analysis and supplementary analysis in light f the estimand framewrk Internatinal Cnference n Harmnisatin f Technical Requirements fr Registratin f Pharmaceuticals fr Human Use 13

Draft (Step 2) guideline ICH E9(R1) Estimands and Sensitivity Analysis in Clinical Trials Training mdule 2.5: Impact n trial analysis Addendum t ICH E9 Statistical Principles fr Clinical Trials ICH E9(R1) Expert Wrking Grup June 2018 Internatinal Cuncil fr Harmnisatin f Technical Requirements fr Registratin f Pharmaceuticals fr Human Use 1

Mdule 2.5 Impact n trial analysis Disclaimer This presentatin includes the authrs views n thery and practice regarding estimands and sensitivity analysis in clinical trials. The presentatin des nt represent fficial guidance r plicy f authrities r industry and it des nt prvide additinal guidance beynd ICH E9(R1). The current slide deck reflects the cntent n the Draft ICH E9(R1) addendum up t the time f its publicatin fr public cnsultatin. Cntent may be changed as a result f the cnsultatin phase. 2

Legal Ntice ICH E9(R1) Step 2 Training Material Mdule 2.5 Impact n trial analysis This presentatin is prtected by cpyright and may be used, reprduced, incrprated int ther wrks, adapted, mdified, translated r distributed under a public license prvided that ICH's cpyright in the presentatin is acknwledged at all times. In case f any adaptatin, mdificatin r translatin f the presentatin, reasnable steps must be taken t clearly label, demarcate r therwise identify that changes were made t r based n the riginal presentatin. Any impressin that the adaptin, mdificatin r translatin f the riginal presentatin is endrsed r spnsred by the ICH must be avided. The presentatin is prvided "as is" withut warranty f any kind. In n event shall the ICH r the authrs f the riginal presentatin be liable fr any claim, damages r ther liability arising frm the use f the presentatin. The abve-mentined permissins d nt apply t cntent supplied by third parties. Therefre, fr dcuments where the cpyright vests in a third party, permissin fr reprductin must be btained frm this cpyright hlder. 3

Intrductin nte ICH E9(R1) Step 2 Training Material Mdule 2.5 Impact n trial analysis These slides were prduced as training material t accmpany the Draft ICH E9(R1) addendum. The intentin is t supprt the scientific cmmunity in the cmprehensin f a new framewrk t define estimands based n the trial bjective and cnsidering intercurrent events. Fr this purpse, mst f the cntent f this addendum is presented in a practical fashin, accmpanied by examples and case studies dealing with estimands and sensitivity analysis in clinical trials, based n the experience f Expert Wrking Grup members. The training material is divided in three main mdules: mdule 1 (summary), mdule 2 (cmprehensive slide deck) and mdule 3 (generic example). Mdule 2 is cmpsed by 6 submdules that crrespnd t sectins A.1 t A.6 f the addendum. 4

Mdule 2.5 Impact n trial analysis Training mdules Mdule 1: Summary Mdule 2: Cmprehensive slide deck Mdule 2.1: Intrductin Mdule 2.2: Framewrk Mdule 2.3: Estimands Mdule 2.4: Impact n trial design and cnduct Mdule 2.5: Impact n trial analysis Mdule 2.6: Dcumenting estimands and sensitivity analysis Mdule 3: Generic example 5

Mdule 2.5 Impact n trial analysis ICH E9(R1) Table f Cntents A.1. Purpse and Scpe A.2. A Framewrk t Align Planning, Design, Cnduct, Analysis and Interpretatin A.3. Estimands A.3.1. Descriptin A.3.2. Strategies fr Addressing Intercurrent Events A.3.3. Cnstructin f Estimands - A.3.3.1. General Cnsideratins - A.3.3.2. Cnsideratins f Therapeutic and Experimental Cntext A.4. Impact n Trial Design and Cnduct Mdule 2.4 A.5. Impact n Trial Analysis A.5.1. Main Estimatin Mdule 2.1 A.5.2. Sensitivity Analysis - A.5.2.1. Rle f Sensitivity Analysis - A.5.2.2. Chice f Sensitivity Analysis A.5.3. Supplementary Analysis A.6. Dcumenting Estimands and Sensitivity Analysis A.7. A Generic Example A.7.1. One Intercurrent Event Mdule 3 A.7.2. Tw Intercurrent Events Glssary Mdule 2.5 Mdule 2.6 Mdule 2.3 Mdule 2.2 6

Outline f Mdule 2.5 Main estimatin Sensitivity analysis Rle f sensitivity analysis Chice f sensitivity analysis Supplementary analysis ICH E9(R1) Step 2 Training Material Mdule 2.5 Impact n trial analysis 7

Mdule 2.5 Impact n trial analysis Main estimatin An analytic apprach, r estimatr, that is aligned with a given estimand shuld be implemented. Fr example, if addressing use f rescue medicatin with a treatment-plicy strategy; Analysis based n cntinued data cllectin after the intercurrent event and use f thse bserved data in the analysis wuld be aligned t the estimand, Nt attempting t cllect thse data and/r regarding data after rescue medicatin as missing then analysing thrugh MMRM with a MAR assumptin wuld nt be aligned t the estimand. Trial bjective Estimand Main estimatr Main estimate MMRM: Mixed Mdels fr Repeated Measures MAR: Missing At Randm 8

Mdule 2.5 Impact n trial analysis Main estimatin The estimatr selected shuld be able t prvide an estimate n which a reliable interpretatin can be based. An imprtant cnsideratin fr whether a reliable estimate will be available is the number and plausibility f assumptins that need t be made in the main analysis, including thse assciated with the use f mdelling t address missing data r t replace bserved data (as required fr estimatin in the hypthetical strategy). 9

Main estimatin ICH E9(R1) Step 2 Training Material Mdule 2.5 Impact n trial analysis Any assumptins made shuld be explicitly stated, and sensitivity analysis shuld be used t assess the rbustness f the results t the underlying assumptins. 10

Sensitivity analysis ICH E9(R1) Step 2 Training Material Mdule 2.5 Impact n trial analysis Sensitivity analysis Is a series f analyses targeting the same estimand, with differing assumptins t explre the rbustness f inferences frm the main estimatr t deviatins frm its underlying mdelling assumptins and limitatins in the data. Sensitivity analysis definitin, ICH E9(R1) Draft addendum 11

Mdule 2.5 Impact n trial analysis Main estimatin: missing data The addendum calls fr greater precisin n what is labelled as missing data. Assessments scheduled after an intercurrent event has ccurred (e.g. discntinuatin f treatment) shuld nt autmatically be set t missing: the relevance f these assessments will be determined by the strategy selected. Having clarity in the estimand gives a basis fr planning which data need t be cllected and hence which data, when nt cllected, present a missing data prblem t be addressed. 12

Missing data ICH E9(R1) Step 2 Training Material Mdule 2.5 Impact n trial analysis Missing data Data that wuld be meaningful fr the analysis f a given estimand but were nt cllected. They shuld be distinguished frm data that d nt exist r data that are nt cnsidered meaningful because f an intercurrent event. Missing data definitin, ICH E9(R1) Draft addendum 13

Mdule 2.5 Impact n trial analysis Check slides 28 and 34 f Mdule 2.3 Main estimatin: missing data Fr example: If planning t address the use f rescue medicatin with the treatmentplicy strategy, assessments scheduled after the use f rescue medicatin shuld cntinue as planned. Any assessments nt taken are missing data. If planning t address the use f rescue medicatin with a hypthetical strategy, assessments scheduled after the use f rescue medicatin might nt be required fr estimatin. Methds t address the prblem presented by missing data can be selected t align with the chsen estimand. 14

Mdule 2.5 Impact n trial analysis Rle f sensitivity analysis Sensitivity analysis is used t evaluate the rbustness f inferences made n a particular estimand t limitatins in the data and deviatins frm the assumptins used in the statistical mdel fr the main estimatr. With an agreed estimand, and a pre-specified statistical analysis that is aligned t that estimand, sensitivity analysis can fcus n sensitivity t deviatins frm assumptins in respect f a particular analysis rather than sensitivity t the chice f analytic apprach. 15

Mdule 2.5 Impact n trial analysis Rle f sensitivity analysis In general, the statistical assumptins that underpin the main estimatr shuld be clearly dcumented. One r mre analyses, fcused n the same estimand, shuld then be pre-specified t investigate the impact f deviatins frm these assumptins. Missing data require particular attentin in a sensitivity analysis because the assumptins underlying any methd may be hard t justify fully and may be impssible t test. 16

Mdule 2.5 Impact n trial analysis Supplementary analysis If the estimate crrespnding t a given estimatr, and assciated inference, is shwn t be rbust thrugh sensitivity analysis, then the interpretatin f trial results shuld fcus n the main estimatr fr each selected estimand. Any ther analysis that is planned, presented r requested in rder t mre fully investigate and understand the trial data and the effects f treatment is referred t as a supplementary analysis. Supplementary analysis might target different estimands, r target the same estimand based n a different analytical apprach. 17

Mdule 2.5 Impact n trial analysis Supplementary analysis Supplementary analysis Is a general descriptin fr analyses that are cnducted in additin t the main and sensitivity analysis t prvide additinal insights int the understanding f the treatment effect. The term describes a brader class f analyses than sensitivity analyses. Supplementary analysis definitin, ICH E9(R1) Draft addendum 18

Mdule 2.5 Impact n trial analysis Supplementary analysis Supplementary analysis shuld be clearly distinguished frm sensitivity analysis, and in general, shuld be given a lwer pririty relative t a sensitivity analysis. Fr example, investigating assumptins f nrmality assciated with ANOVA wuld result in sensitivity analysis; analysing the dataset instead based n cmparisn f medians wuld be cnsidered a supplementary analysis. ANOVA: Analysis f variance 19

Next mdule: 2.6. Dcumenting estimands and sensitivity analysis Impact f the addendum n prtcl writing, study design, data analysis and interpretatin Internatinal Cnference n Harmnisatin f Technical Requirements fr Registratin f Pharmaceuticals fr Human Use 20

Draft (Step 2) guideline ICH E9(R1) Estimands and Sensitivity Analysis in Clinical Trials Training mdule 2.6: Dcumenting estimands and sensitivity analysis Addendum t ICH E9 Statistical Principles fr Clinical Trials ICH E9(R1) Expert Wrking Grup June 2018 Internatinal Cuncil fr Harmnisatin f Technical Requirements fr Registratin f Pharmaceuticals fr Human Use 1

Mdule 2.6 - Dcumenting estimands and sensitivity analysis Disclaimer This presentatin includes the authrs views n thery and practice regarding estimands and sensitivity analysis in clinical trials. The presentatin des nt represent fficial guidance r plicy f authrities r industry and it des nt prvide additinal guidance beynd ICH E9(R1). The current slide deck reflects the cntent n the Draft ICH E9(R1) addendum up t the time f its publicatin fr public cnsultatin. Cntent may be changed as a result f the cnsultatin phase. 2

Legal Ntice ICH E9(R1) Step 2 Training Material Mdule 2.6 - Dcumenting estimands and sensitivity analysis This presentatin is prtected by cpyright and may be used, reprduced, incrprated int ther wrks, adapted, mdified, translated r distributed under a public license prvided that ICH's cpyright in the presentatin is acknwledged at all times. In case f any adaptatin, mdificatin r translatin f the presentatin, reasnable steps must be taken t clearly label, demarcate r therwise identify that changes were made t r based n the riginal presentatin. Any impressin that the adaptin, mdificatin r translatin f the riginal presentatin is endrsed r spnsred by the ICH must be avided. The presentatin is prvided "as is" withut warranty f any kind. In n event shall the ICH r the authrs f the riginal presentatin be liable fr any claim, damages r ther liability arising frm the use f the presentatin. The abve-mentined permissins d nt apply t cntent supplied by third parties. Therefre, fr dcuments where the cpyright vests in a third party, permissin fr reprductin must be btained frm this cpyright hlder. 3

Intrductin nte ICH E9(R1) Step 2 Training Material Mdule 2.6 - Dcumenting estimands and sensitivity analysis These slides were prduced as training material t accmpany the Draft ICH E9(R1) addendum. The intentin is t supprt the scientific cmmunity in the cmprehensin f a new framewrk t define estimands based n the trial bjective and cnsidering intercurrent events. Fr this purpse, mst f the cntent f this addendum is presented in a practical fashin, accmpanied by examples and case studies dealing with estimands and sensitivity analysis in clinical trials, based n the experience f Expert Wrking Grup members. The training material is divided in three main mdules: mdule 1 (summary), mdule 2 (cmprehensive slide deck) and mdule 3 (generic example). Mdule 2 is cmpsed by 6 submdules that crrespnd t sectins A.1 t A.6 f the addendum. 4

Training mdules Mdule 1: Summary Mdule 2: Cmprehensive slide deck Mdule 2.1: Intrductin Mdule 2.2: Framewrk Mdule 2.3: Estimands Mdule 2.4: Impact n trial design and cnduct Mdule 2.5: Impact n trial analysis Mdule 2.6: Dcumenting estimands and sensitivity analysis Mdule 3: Generic example ICH E9(R1) Step 2 Training Material Mdule 2.6 - Dcumenting estimands and sensitivity analysis 5

ICH E9(R1) Table f Cntents A.1. Purpse and Scpe A.2. A Framewrk t Align Planning, Design, Cnduct, Analysis and Interpretatin A.3. Estimands A.3.1. Descriptin A.3.2. Strategies fr Addressing Intercurrent Events A.3.3. Cnstructin f Estimands - A.3.3.1. General Cnsideratins - A.3.3.2. Cnsideratins f Therapeutic and Experimental Cntext A.4. Impact n Trial Design and Cnduct Mdule 2.4 A.5. Impact n Trial Analysis A.5.1. Main Estimatin Mdule 2.1 A.5.2. Sensitivity Analysis - A.5.2.1. Rle f Sensitivity Analysis - A.5.2.2. Chice f Sensitivity Analysis A.5.3. Supplementary Analysis A.6. Dcumenting Estimands and Sensitivity Analysis A.7. A Generic Example A.7.1. One Intercurrent Event Mdule 3 A.7.2. Tw Intercurrent Events Glssary ICH E9(R1) Step 2 Training Material Mdule 2.6 - Dcumenting estimands and sensitivity analysis Mdule 2.5 Mdule 2.6 Mdule 2.3 Mdule 2.2 6

Mdule 2.6 - Dcumenting estimands and sensitivity analysis Outline f Mdule 2.6 Incrprating estimands in the writing f a trial prtcl and statistical analysis plan Ptential impact f ICH E9(R1) n study cnduct Data analysis and interpretatin 7

Mdule 2.6 - Dcumenting estimands and sensitivity analysis Incrprating estimands in prtcl writing Clinical Trial Trial Prtcl Estimands shuld be defined and explicitly specified in the clinical trial prtcl. The primary estimand pre-specified in the trial prtcl shuld crrespnd t the primary trial bjective. The prtcl and the statistical analysis plan shuld pre-specify the main estimatr that is aligned with the primary estimand and leads t the primary analysis. Suitable sensitivity analysis shuld be planned t explre the rbustness under deviatins frm its assumptins. 8

Mdule 2.6 - Dcumenting estimands and sensitivity analysis Incrprating estimands in prtcl writing Clinical Trial Trial Prtcl Estimands fr secndary trial bjectives (e.g. related t secndary variables) that are likely t supprt regulatry decisins shuld be described prperly, each with a crrespnding main estimatr. Suitable sensitivity analysis shuld be planned. Additinal trial bjectives may be cnsidered fr explratry purpses, leading t additinal estimands. It is nt a regulatry requirement t dcument in detail an estimand fr each explratry questin. 9

Mdule 2.6 - Dcumenting estimands and sensitivity analysis Incrprating estimands in prtcl writing Clinical Trial Trial Prtcl Where different scientific questins f interest call fr materially different estimands, it is recmmended that these shuld be fully dcumented. The chice f the primary estimand will usually be the main determinant fr aspects f trial design. 10

Mdule 2.6 - Dcumenting estimands and sensitivity analysis Incrprating estimands in prtcl writing The trial analysis summarised in the prtcl shuld be aligned with the primary estimand with regards t (including but nt limited t): Handling f intercurrent events, fr example: - Use f rescue medicatin; - Changes in permitted medicatins; - Switching treatments; - Discntinuing treatment; - Subject deaths. The selectin f subjects t be included in the analyses. Multiplicity issues e.g. if there is mre than ne estimand. 11

Mdule 2.6 - Dcumenting estimands and sensitivity analysis Incrprating estimands in prtcl writing Clinical Trial Trial Prtcl ICH E9(R1) des nt mandate where in the prtcl estimands shuld be described, nr mandate hw estimands shuld be described. 12

Mdule 2.6 - Dcumenting estimands and sensitivity analysis Incrprating estimands in the SAP Clinical Trial Trial Prtcl Statistical Analysis Plan Full details f the planned statistical analyses shuld align with the estimand(s) defined, and nt the ther way arund! SAP: Statistical Analysis Plan 13

Mdule 2.6 - Dcumenting estimands and sensitivity analysis Ptential impact n study cnduct Clinical Trial The ccurrence f anticipated intercurrent events shuld be mnitred during the study. Estimands defined in the trial prtcl culd be subsequently affected by issues arising during study cnduct. The impact f any unanticipated intercurrent events ccurring and/r ther study cnduct issues pssibly affecting the primary estimand shuld be evaluated. A prtcl amendment culd be cnsidered depending n the nature and significance f such issues t the primary estimand. 14

Mdule 2.6 - Dcumenting estimands and sensitivity analysis Data analysis and interpretatin Clinical Trial Results frm the main, sensitivity and any supplementary analyses shuld be reprted systematically in the clinical trial reprt, specifying whether each analysis was pre-specified, intrduced while the trial was still blinded, r perfrmed pst hc. 15

Mdule 2.6 - Dcumenting estimands and sensitivity analysis Data analysis and interpretatin Clinical Trial Fr intercurrent events that were nt freseen at the design stage, and were identified during the cnduct f the trial with n pssibility t frmally amend the estimand specified in the prtcl, the spnsr shuld: explain hw the unfreseen intercurrent events were handled in the analysis; explain the impact n what the revised analysis estimates (i.e. the impact n the pre-specified estimand). Cnsultatin with regulatrs t agree the preferred handling f intercurrent events nt initially freseen might be advisable. 16

Next mdule: 3. Generic example Internatinal Cnference n Harmnisatin f Technical Requirements fr Registratin f Pharmaceuticals fr Human Use 17

Draft (Step 2) guideline ICH E9(R1) Estimands and Sensitivity Analysis in Clinical Trials Training mdule 3: Generic Example Addendum t ICH E9 Statistical Principles fr Clinical Trials ICH E9(R1) Expert Wrking Grup June 2018 Internatinal Cuncil fr Harmnisatin f Technical Requirements fr Registratin f Pharmaceuticals fr Human Use 1

Mdule 3 Generic example Disclaimer This presentatin includes the authrs views n thery and practice regarding estimands and sensitivity analysis in clinical trials. The presentatin des nt represent fficial guidance r plicy f authrities r industry and it des nt prvide additinal guidance beynd ICH E9(R1). The current slide deck reflects the cntent n the Draft ICH E9(R1) addendum up t the time f its publicatin fr public cnsultatin. Cntent may be changed as a result f the cnsultatin phase. 2

Legal Ntice ICH E9(R1) Step 2 Training Material Mdule 3 Generic example This presentatin is prtected by cpyright and may be used, reprduced, incrprated int ther wrks, adapted, mdified, translated r distributed under a public license prvided that ICH's cpyright in the presentatin is acknwledged at all times. In case f any adaptatin, mdificatin r translatin f the presentatin, reasnable steps must be taken t clearly label, demarcate r therwise identify that changes were made t r based n the riginal presentatin. Any impressin that the adaptin, mdificatin r translatin f the riginal presentatin is endrsed r spnsred by the ICH must be avided. The presentatin is prvided "as is" withut warranty f any kind. In n event shall the ICH r the authrs f the riginal presentatin be liable fr any claim, damages r ther liability arising frm the use f the presentatin. The abve-mentined permissins d nt apply t cntent supplied by third parties. Therefre, fr dcuments where the cpyright vests in a third party, permissin fr reprductin must be btained frm this cpyright hlder. 3

Intrductin nte ICH E9(R1) Step 2 Training Material Mdule 3 Generic example These slides were prduced as training material t accmpany the Draft ICH E9(R1) addendum. The intentin is t supprt the scientific cmmunity in the cmprehensin f a new framewrk t define estimands based n the trial bjective and cnsidering intercurrent events. Fr this purpse, mst f the cntent f this addendum is presented in a practical fashin, accmpanied by examples and case studies dealing with estimands and sensitivity analysis in clinical trials, based n the experience f Expert Wrking Grup members. The training material is divided in three main mdules: mdule 1 (summary), mdule 2 (cmprehensive slide deck) and mdule 3 (generic example). Mdule 2 is cmpsed by 6 submdules that crrespnd t sectins A.1 t A.6 f the addendum. 4

Mdule 3 Generic example Training mdules Mdule 1: Summary Mdule 2: Cmprehensive slide deck Mdule 2.1: Intrductin Mdule 2.2: Framewrk Mdule 2.3: Estimands Mdule 2.4: Impact n trial design and cnduct Mdule 2.5: Impact n trial analysis Mdule 2.6: Dcumenting estimands and sensitivity analysis Mdule 3: Generic example 5

ICH E9(R1) Table f Cntents A.1. Purpse and Scpe A.2. A Framewrk t Align Planning, Design, Cnduct, Analysis and Interpretatin A.3. Estimands A.3.1. Descriptin A.3.2. Strategies fr Addressing Intercurrent Events A.3.3. Cnstructin f Estimands - A.3.3.1. General Cnsideratins - A.3.3.2. Cnsideratins f Therapeutic and Experimental Cntext A.4. Impact n Trial Design and Cnduct Mdule 2.4 A.5. Impact n Trial Analysis A.5.1. Main Estimatin A.5.2. Sensitivity Analysis - A.5.2.1. Rle f Sensitivity Analysis - A.5.2.2. Chice f Sensitivity Analysis A.5.3. Supplementary Analysis ICH E9(R1) Step 2 Training Material Mdule 3 Generic example Mdule 2.1 A.6. Dcumenting Estimands and Sensitivity Analysis A.7. A Generic Example A.7.1. One Intercurrent Event Mdule 3 A.7.2. Tw Intercurrent Events Glssary Mdule 2.5 Mdule 2.6 Mdule 2.3 Mdule 2.2 6

Estimands and Sensitivity Analysis A thinking prcess 7

A thinking prcess ICH E9(R1) Step 2 Training Material Mdule 3 Generic example 1 2 3 4 5 6 Therapeutic setting and intent f treatment determining a trial bjective Identify intercurrent events Discuss strategies t address intercurrent events Cnstruct the estimand(s) Align chices n trial design, data cllectin and methd f estimatin Identify assumptins fr the main analysis and suitable sensitivity analyses t investigate these assumptins 7 Dcument the chsen estimands 8

A thinking prcess ICH E9(R1) Step 2 Training Material Mdule 3 Generic example 1 2 3 4 5 6 Therapeutic setting and intent f treatment determining a trial bjective Identify intercurrent events Discuss strategies t address intercurrent events Cnstruct the estimand(s) Align chices n trial design, data cllectin and methd f estimatin Identify assumptins fr the main analysis and suitable sensitivity analyses t investigate these assumptins 7 Dcument the chsen estimands 9

Mdule 3 Generic example Check Mdule 2.1 1 Therapeutic setting and intent f treatment determining a trial bjective The therapeutic setting and the intent f treatment will determine the trial bjective. An understanding f the therapeutic setting includes the disease r cnditin under study and, fr example, the availability f ther treatment ptins and the pssibilities t mnitr individual respnse t treatment. Diagnsis, treatment and preventin are different intents f treatment and will determine different trial bjectives. Tgether these will start t shape the estimand attributes (Mdule 2.3). Check Mdule 2.3 10

Mdule 3 Generic example 1 Therapeutic setting and intent f treatment determining a trial bjective Example Drug X Fr example, a treatment might be intended fr a chrnic nn-life-threatening cnditin. The develpment prgramme might aim t investigate the effect f treatment t cntrl a sign r symptm f the disease. This will give rise t initial thughts fr the target ppulatin and the variable. Fr the example, cnsider a variable measured mnthly n a cntinuus scale (e.g. systlic bld pressure, SBP, after 6 mnths in hypertensin). SBP: Systlic bld pressure 11

A thinking prcess ICH E9(R1) Step 2 Training Material Mdule 3 Generic example 1 2 3 4 5 6 Therapeutic setting and intent f treatment determining a trial bjective Identify intercurrent events Discuss strategies t address intercurrent events Cnstruct the estimand(s) Align chices n trial design, data cllectin and methd f estimatin Identify assumptins fr the main analysis and suitable sensitivity analyses t investigate these assumptins 7 Dcument the chsen estimands 12

Mdule 3 Generic example Check Mdule 2.1 2 Identify intercurrent events Intercurrent events Events that ccur after treatment initiatin and either preclude bservatin f the variable r affect its interpretatin. Intercurrent events definitin, ICH E9(R1) Draft addendum These events (e.g. death) that preclude the bservatin f the variable, shuld nt be cnfused with missing data resulting frm lss t fllw-up that can ccur in a clinical trial 13

Mdule 3 Generic example Check Mdule 2.1 2 Identify intercurrent events Cmmn intercurrent events Treatment discntinuatin/nn-adherence t treatment, perhaps identified as multiple separate intercurrent events distinguished by reasn (e.g. treatment discntinuatin due t lack f efficacy, due t txicity, ); Use f additinal medicatin, perhaps distinguished by type and / r reasn, e.g.: - rescue medicatin due t lack f efficacy; treatment switch; use f prhibited medicatin; change in backgrund medicatin; Death and ther terminal events. Address each intercurrent event that will affect the interpretatin f the trial results, nt nly the mst frequently ccurring 14

Mdule 3 Generic example 2 Identify intercurrent events Examples f intercurrent events that might be freseen 1. Treatment discntinuatin/nn-adherence t treatment. 2. Use f additinal medicatin (that will impact measurements f SBP). Example Drug X SBP: Systlic bld pressure 15

A thinking prcess ICH E9(R1) Step 2 Training Material Mdule 3 Generic example 1 2 3 4 5 6 Therapeutic setting and intent f treatment determining a trial bjective Identify intercurrent events Discuss strategies t address intercurrent events Cnstruct the estimand(s) Align chices n trial design, data cllectin and methd f estimatin Identify assumptins fr the main analysis and suitable sensitivity analyses t investigate these assumptins 7 Dcument the chsen estimands 16

Mdule 3 Generic example Check Mdule 2.3 3 Discuss strategies t address intercurrent events Discuss Cnsider different strategies t address these tw intercurrent events (slides 19-21 and 22-24 respectively). Reflect hw t accunt fr intercurrent events thrugh the estimand attributes A-D (see Mdule 2.3, illustrated n the fllwing slides). 17

Mdule 3 Generic example Check Mdule 2.3 3 Discuss strategies t address intercurrent events Chse each strategy t reflect the scientific questin f interest. Each chice f strategy shuld be driven by the trial bjective and nt by the preferred chice f estimatr. Cnsider: Is what is t be estimated relevant fr spnsr, regulatr and prescriber decisin making? Can an estimate that is reliable fr inference be btained? The chice f strategy fr each intercurrent event shuld be the subject f multi-disciplinary discussin within the spnsr s team, within the regulatr s team and between spnsr and regulatr. 18

Mdule 3 Generic example 3 Discuss strategies t address intercurrent events Cnsider the pssible strategies t address each intercurrent event Objective = quantifying the effects f treatment n SBP. Intercurrent event (1) = treatment discntinuatin. Cnsider different strategies: Example Drug X 1. Treatment plicy: t investigate a treatment effect at mnth 6 regardless f whether r nt the patients cntinue with treatment (specified in attribute C). SBP: Systlic bld pressure 19

Mdule 3 Generic example 3 Discuss strategies t address intercurrent events Example Drug X 2. Cmpsite: fr example, the treatment effect is established based n a cmpsite variable cmbining a clinically meaningful change in SBP and cntinuatin f treatment at mnth 6. Thus bth lack f meaningful change and treatment discntinuatin will be captured as unfavurable utcmes (specified, if using a cmpsite variable, in attribute B). 3. Hypthetical: t investigate a treatment effect at mnth 6 if all patients cntinue with treatment (specified in attribute C). SBP: Systlic bld pressure 20

Mdule 3 Generic example 3 Discuss strategies t address intercurrent events Example Drug X 4. Principal stratum: t investigate the effect f treatment at mnth 6 in the stratum f the ppulatin wh wuld be able t cntinue treatment with drug X (specified in attribute A). 5. While n treatment: t investigate the effect f treatment while the patient cntinues t take their treatment rather than at a fixed time (i.e. 6 mnths) after initiatin f treatment (specified in attribute B). 21

Mdule 3 Generic example 3 Discuss strategies t address intercurrent events Cnsider the pssible strategies t address each intercurrent event Objective = quantifying the effects f the treatment n SBP. Intercurrent event (2) = use f additinal medicatin that affects SBP. Cnsider different strategies: Example Drug X 1. Treatment plicy: investigate the treatment effect at mnth 6 regardless f whether r nt the patient uses additinal medicatin. This strategy will capture bth the effect f Drug X and the effect f additinal medicatin (specified in attribute C). SBP: Systlic bld pressure 22

Mdule 3 Generic example 3 Discuss strategies t address intercurrent events Example Drug X 2. Cmpsite: fr example, the treatment effect is established based n a cmpsite variable indicating a clinically meaningful change btained withut the use f additinal medicatin at mnth 6. This will capture bth lack f meaningful change and the use f additinal medicatin as unfavurable utcmes (specified, if using a cmpsite variable, in attribute B). 3. Hypthetical: investigate the treatment effect at mnth 6 if additinal medicatin wuld nt be available t patients. This strategy aims t capture the effect f drug X withut the effect f additinal medicatin (specified in attribute C). 23

Mdule 3 Generic example 3 Discuss strategies t address intercurrent events Example Drug X 4. Principal stratum: t investigate the effect f treatment at mnth 6 in the stratum f the ppulatin wh wuld nt use additinal medicatin n drug X (specified in attribute A). 5. While n treatment: t investigate the effect f treatment befre the pint at which the patient uses additinal medicatin (specified in attribute B). 24

A thinking prcess ICH E9(R1) Step 2 Training Material Mdule 3 Generic example 1 2 3 4 5 6 Therapeutic setting and intent f treatment determining a trial bjective Identify intercurrent events Discuss strategies t address intercurrent events Cnstruct the estimand(s) Align chices n trial design, data cllectin and methd f estimatin Identify assumptins fr the main analysis and suitable sensitivity analyses t investigate these assumptins 7 Dcument the chsen estimands 25

Mdule 3 Generic example 4 Cnstruct the estimand(s) Example Drug X Select a preferred strategy fr each intercurrent event: Fr illustratin* we chse: Treatment discntinuatin/nn-adherence t treatment Treatment plicy (specified in attribute C). Use f additinal r alternative medicatin that affects SBP Hypthetical (specified in attribute C). * This is nt an indicatin f regulatry preference r plicy SBP: Systlic bld pressure 26

Mdule 3 Generic example 4 Cnstruct the estimand(s) Definitin f the estimand (4 attributes): A. Ppulatin: defined thrugh apprpriate inclusin/exclusin criteria t reflect the targeted patient ppulatin fr apprval. B. Endpint: change frm baseline t mnth 6 in SBP. Example Drug X C. Hw t accunt fr intercurrent events: if additinal medicatin was nt available t patients prir t mnth 6 and regardless f whether r nt the patient cntinue with treatment D. Ppulatin-level summary: difference in variable means between treatment cnditins. SBP: Systlic bld pressure 27

Mdule 3 Generic example 4 Cnstruct the estimand(s) A descriptin f the estimand might be*: Example Drug X The difference in means between treatment cnditins in the target patient ppulatin fr the change frm baseline t mnth 6 in SBP, if additinal medicatin was nt available t patients prir t mnth 6 and regardless f whether r nt the patient cntinues with treatment. * This is nt an indicatin f regulatry preference r plicy SBP: Systlic bld pressure 28

Mdule 3 Generic example 4 Cnstruct the estimand(s) Select a preferred strategy fr each intercurrent event: As a secnd illustratin* we chse: Example Drug X Treatment discntinuatin/nn-adherence t treatment Treatment plicy (specified in attribute C). Use f additinal r alternative medicatin that affects SBP Cmpsite (specified in attribute B). * This is nt an indicatin f regulatry preference r plicy SBP: Systlic bld pressure 29

Mdule 3 Generic example 4 Cnstruct the estimand(s) Definitin f the secnd estimand (4 attributes): Example Drug X A. Ppulatin: defined thrugh apprpriate inclusin/exclusin criteria t reflect the targeted patient ppulatin fr apprval. B. Endpint: binary respnse variable indicating a successful respnse if a clinically meaningful change frm baseline t mnth 6 in SBP is btained withut the use f additinal medicatin C. Hw t accunt fr intercurrent events: regardless f whether r nt the patient cntinue with treatment. D. Ppulatin-level summary: difference in respnse prprtins between treatment cnditins. SBP: Systlic bld pressure 30

Mdule 3 Generic example 4 Cnstruct the estimand(s) A descriptin f the estimand might be*: Example Drug X The difference in respnse prprtins between treatment cnditins in the target patient ppulatin in successful respnses at mnth 6 in SBP withut use f additinal medicatin, regardless f whether r nt the patient cntinues with treatment. * This is nt an indicatin f regulatry preference r plicy SBP: Systlic bld pressure 31

A thinking prcess ICH E9(R1) Step 2 Training Material Mdule 3 Generic example 1 2 3 4 5 6 Therapeutic setting and intent f treatment determining a trial bjective Identify intercurrent events Discuss strategies t address intercurrent events Cnstruct the estimand(s) Align chices n trial design, data cllectin and methd f estimatin Identify assumptins fr the main analysis and suitable sensitivity analyses t investigate these assumptins 7 Dcument the chsen estimands 32

Mdule 3 Generic example Check Mdule 2.4 5 Align chices n trial design, data cllectin and methd f estimatin In additin t chsing strategies t reflect the scientific questin f interest, it is necessary t determine that an apprpriate trial design and statistical analysis can be perfrmed. Specifically: Cnsider the data that need t be cllected. - Difficulties in data cllectin fllwing the ccurrence f an intercurrent event are nt necessarily a strng ratinale t change an estimand r strategy Cnsider whether an estimate f treatment effect can be derived that is reliable fr decisin making. 33

Mdule 3 Generic example Check Mdule 2.4 5 Align chices n trial design, data cllectin and methd f estimatin A balance might need t be struck between an estimand f greater clinical relevance estimated with an analytical apprach that requires numerus assumptins, and an estimand f less clinical relevance that can be estimated with an analytical apprach requiring few assumptins. This will depend n the extent t which sensitivity analysis can prvide cnfidence in the reliability f the results. 34

Mdule 3 Generic example 5 Align chices n trial design, data cllectin and methd f estimatin Example Drug X A randmised cntrlled trial cmparing Drug X t placeb is cnsidered the mst apprpriate design t investigate the research questin. Cnsider the data that wuld need t be cllected, e.g. fr the use f additinal medicatin: fr a treatment-plicy strategy it shuld be planned that all measurements f SBP regardless f use f additinal medicatin are cllected thrughut the trial; fr a hypthetical strategy measurements f SBP after the use f additinal medicatin wuld nt need t be cllected (fr this estimand, but might be required fr thers). SBP: Systlic bld pressure 35

Mdule 3 Generic example 5 Align chices n trial design, data cllectin and methd f estimatin Example Drug X Cnsider whether an estimate f treatment effect can be derived that is reliable fr decisin making, e.g. fr the use f additinal medicatin: fr a treatment-plicy strategy if all data are cllected then analysis might rely n few assumptins. Otherwise, estimatin will rely n mdel assumptins that require extensive sensitivity analysis. fr a hypthetical strategy measurements f SBP after the use f additinal medicatin are nt relevant and values must be imputed r predicted, relying n mdel assumptins that require mre extensive sensitivity analysis. SBP: Systlic bld pressure 36

Mdule 3 Generic example 5 Align chices n trial design, data cllectin and methd f estimatin Example Drug X Cnsider whether an estimate f treatment effect can be derived that is reliable fr decisin making, e.g. fr the use f additinal medicatin (cntinued): fr a principal stratum strategy estimatin f a treatment effect will be cnfunded unless the subjects within that stratum can be identified befre randmisatin. Otherwise, estimatin will rely n mdel assumptins that will require extensive sensitivity analysis. fr a while n treatment strategy estimatin f a treatment effect will require strng assumptins when the ccurrence and timing f an intercurrent event is related t treatment and will require extensive sensitivity analysis. The extent f sensitivity analysis required in estimatin might preclude the chice f a particular estimand. 37

Mdule 3 Generic example 5 Align chices n trial design, data cllectin and methd f estimatin Estimand* (slide 28): The difference in means between treatment cnditins in the target patient ppulatin fr the change frm baseline t mnth 6 in SBP, if additinal medicatin was nt available t patients prir t mnth 6 and regardless f whether r nt the patient cntinues with treatment. Discuss and select a methd fr estimatin* that is aligned t the estimand: Example Drug X e.g. ANCOVA with imputatin f the measurements after the patient uses additinal medicatin, perhaps based n data in the placeb grup. SBP: Systlic bld pressure ANCOVA: Analysis f cvariance * This is nt an indicatin f regulatry preference r plicy 38

A thinking prcess ICH E9(R1) Step 2 Training Material Mdule 3 Generic example 1 2 3 4 5 6 Therapeutic setting and intent f treatment determining a trial bjective Identify intercurrent events Discuss strategies t address intercurrent events Cnstruct the estimand(s) Align chices n trial design, data cllectin and methd f estimatin Identify assumptins fr the main analysis and suitable sensitivity analyses t investigate these assumptins 7 Dcument the chsen estimands 39

Mdule 3 Generic example Check Mdule 2.5 6 Identify assumptins fr the main analysis and suitable sensitivity analysis t investigate these assumptins Sensitivity analysis Is a series f analyses targeting the same estimand, with differing assumptins t explre the rbustness f inferences frm the main estimatr t deviatins frm its underlying mdelling assumptins and limitatins in the data. Sensitivity analysis definitin, ICH E9(R1) Draft addendum Assumptins made by the analytical apprach must be investigated thrugh apprpriate sensitivity analysis. Analyses cnducted fr reasns ther than t investigate the assumptins made by the main analysis are supplementary analysis. 40