Current Issues in Clinical Trials A Biostatistician s perspective

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1 Current Issues in Clinical Trials A Biostatistician s perspective Centra de Recerca Matematica CRM Seminar 10 September 2015 BARCELONA CATALUNYA Urania Dafni National and Kapodistrian University of Athens Frontier Science Foundation-Hellas

2 Statistical Issues in the Molecular Era Adaptive Design Surrogate Endpoints Composite Endpoints Selective Crossover Multiple testing CRM - BARCELONA 10 September

3 Molecular medicine has truly blossomed 10 years ~40 drug approvals based on specific tumor biomarkers Edward Kim, 2015 ASCO Cancer Treatment From Chemotherapy TO TARGETED therapies Clinical trials need to be modernized for the molecular age Aim: Find better ways to match drugs with patients and studying them in large and diverse populations Roy Herbst, 21 st Century Cures: Modernizing Clinical Trials, July , Testimony before the subcommittee on Health of the US House of representatives CRM - BARCELONA 10 September

4 Statistical trial designs in the era of targeted therapies Larger organized trials to identify biomarkers Tumor Registries in numerous consortiums, e.g., Lung Cancer Mutation Consortium Adaptive trial designs: Umbrella & Basket trials Edward Kim, 2015 ASCO Classical Designs Multi-stage Designs Adaptive Designs/Bayesian Adaptive Designs Multi-component Clinical trial Designs CRM - BARCELONA 10 September

5 Classification by Objective CRM - BARCELONA 10 September

6 Clinical trials in the era of targeted therapies: Goals - Test the methods of assessment of marker alteration in normal and tumor tissue samples - Get guidance in the determination of cut points - Get a preliminary assessment of efficacy within molecularly defined subsets Establish clinical value by careful retrospective assessment of the marker Modified from Sumithra et al. (2015) ASCO Educational Book Validate the marker and companion diagnostic CRM - BARCELONA 10 September

7 Prognostic & predictive marker Correlation between biomarker and true clinical endpoint makes a prognostic marker. Correlation between biomarker and true clinical endpoint does not make a predictive biomarker. Interaction between biomarker status and treatment CRM - BARCELONA 10 September

8 Prognostic & predictive Prognostic Clinical Endpoint <- Worse Better -> Clinical Endpoint <- Worse Better -> Std Exp Std Exp Treatment Treatment 60% 50% 40% 30% 20% 10% 0% Control Factor present Factor absent Treated Clinical Endpoint <- Worse Better -> Predictive Prognostic and predictive Clinical Endpoint <- Worse Better -> Std Treatment Exp Std Treatment Exp CRM - BARCELONA 10 September

9 0 Design of clinical trials for biomarker research in oncology Initial Validation: Phase II testing Definitive validation: Phase III testing All comers (stratified by marker status) designs Enrichment designs Adaptive designs Mandrekar SJ and Sargent DJ, Clin Investig 2011 CRM - BARCELONA 10 September

10 Biomarker research: Phase II testing Enrichment designs Based on the paradigm that the benefit will be restricted to a biomarker-defined subgroup of patients. Patients are screened for the presence or absence of a biomarker profile and only those who either have or do not have the profile are included All comers (stratified by marker status) designs All patients meeting the eligibility criteria, without exploring the biomarker status in question, are entered (Sargent et al., 2005; Mandrekar and Sargent, 2009) Ability to provide adequate tissue CRM - BARCELONA 10 September

11 Biomarker research: Phase III Trials Develop companion diagnostics? Selection of patients No enrichment by biomarker Enrichment at the beginning Enrichment after an interim analysis Treatment allocation Ignore/independent of biomarkers Depend on biomarkers CRM - BARCELONA 10 September

12 Randomized design Freidlin B, et al. J Natl Cancer Inst. 2010;102:152. CRM - BARCELONA 10 September

13 Enrichment approach: Strong scientific rational The enrichment design should be used when the mechanism of the disease biomarker is well understood! Patient selection and enrichment approaches are becoming increasingly relevant; But, to support the enrichment strategy there is a need of: sufficient biologic rationale, understanding of the mechanism of action of the drug, assay characteristics, and validated cut points Sumithra et al. (2015) ASCO Educational Book CRM - BARCELONA 10 September

14 Slide 6 CRM - BARCELONA 10 September Presented By Richard Simon at 2015 ASCO Annual Meeting

15 Allcomers - Marker by treatment interaction designs All patients meeting the eligibility criteria are entered. The marker status is used as a stratification factor and patients are randomized to treatment choices within each marker-based subgroup. Prospective sample size specification for each marker-based subgroup. A separate evaluation of the treatment effect is performed in the two-marker defined subgroups or first a test of interaction is carried out. If interaction non-significant, comparison of treatments can take place in the overall population. CRM - BARCELONA 10 September

16 Randomized design All comers Freidlin B, et al. J Natl Cancer Inst. 2010;102:152. CRM - BARCELONA 10 September

17 Adaptive Designs (AD) Treatment effect independent adaptive designs Covariate-Adaptive Randomization Minimization Re-estimation of sample size based on lower event rate They do not inflate the type I error - Regulatory approved Other types of adaptive designs Continual reassessment method (CRM) for phase I trials and seamless phase II/III designs Well understood and recommended Treatment effect dependent adaptive designs One or more of the design features (sample size, patient inclusion criteria, treatment groups compared, treatment allocation ratio, or even the primary endpoint) can be adapted, depending on the observed treatment effect. Limitations of Adaptive Clinical Trials By Marc Buyse, ScD Educational Book Manuscript ASCO 2012 CRM - BARCELONA 10 September

18 Allcomers Sequential testing strategy designs Similar in principle to classical RCT designs Single Primary hypothesis Tested for overall population first and then in a prospectively planned subset if the overall test is not significant, or In the marker-defined subgroup first and then in the entire population if the subgroups analysis is significant (closed testing procedure) Strategy largely driven by three statistical parameters: type I error (α), type II error (β), targeted difference or target effect size (δ). CRM - BARCELONA 10 September

19 MAMS Trials: Sophisticated Adaptive Designs Multi-arm multi-stage (MAMS) trials form a very broad class of design: Comparisons may be between active treatments and control treatment, or pairwise between all arms. Stopping for efficacy may mean the trial stops, or just the relevant arm (or stopping for efficacy may not be allowed). The endpoint tested may be the same at each interim analysis, or may differ. Expected sample size is very important to control as it determines efficiency of drug development process, as well as how many patients are exposed to ineffective treatments (Wason and Jaki, 2012). CRM - BARCELONA 10 September

20 Traditional vs. MAMS trial designs Bratton (2014) CRM - BARCELONA 10 September

21 Multi-component Clinical trial Design: Courtesy of Solange Peters (ESMO Workshop-Vienna 2015) CRM - BARCELONA 10 September

22 0 Multi-component Clinical trial Design Umbrella Study Concept Evaluate different therapy/biomarker combinations in a single type of cancer I-SPY 2, BATTLE, BATTLE-2, Lung-MAP Basket Study Concept Evaluate the effect of specific genetic or molecular biomarker regardless of the type or subtype of cancer on which it occurs. MATCH, SPECTAlung CRM - BARCELONA 10 September

23 Slide 3 CRM - BARCELONA 10 September Presented By Sumithra Mandrekar at 2015 ASCO Annual Meeting

24 Umbrella Study Adaptive design A variety of marker signatures and drugs can be tested under one umbrella protocol. The success of the drug-biomarker subgroup is assessed in an ongoing manner: Randomization ratio can be altered Under-performing drugs are eliminated Biomarker subgroups are eliminated Key requirements: a rapid and reliable endpoint, real time access to all clinical and biological data. CRM - BARCELONA 10 September

25 List of Umbrella Trials CRM - BARCELONA 10 September Presented By Sumithra Mandrekar at 2015 ASCO Annual Meeting

26 BATTLE trial Figure 1. Schematic diagram of the flow of the BATTLE trial. All eligible patients will be enrolled in the Umbrella protocol first. Tissue biopsy will be taken for biomarker profiling. According to the characteristics of the four biomarker categories, patients will be assigned to the biomarker group 1 to 5 in a sequential order. The symbols +, -, and x correspond to the positive, negative, and either positive or negative biomarker status, respectively. Patients will be adaptively randomized to one of the four treatments according to their biomarker groups. The dashed arrow indicates the putative effective treatment for each of the biomarker groups CRM - BARCELONA 10 September

27 BATTLE trial The adaptive phase II trial BATTLE involved 255 patients with advanced NSCLC. At the end of the adaptive phase, when the trial ended, the erlotinib arm had 59 patients; the vandetanib arm, 54; the sorafenib arm, 105; and the erlotinib-plus-bexarotene arm, 37. Trial proved feasibility of such a huge logistic challenge Identified interaction between the treatments and markers (DCR of 79% with sorafenib in the KRAS/BRAF marker group but only 14% with erlotinib). Efficiency: Too long (from 12/2006)? Too many patients? Kim et al, Cancer Discovery, 2011 CRM - BARCELONA 10 September

28 Umbrella & Basket trials Key points Umbrella trials incorporate a central infrastructure for screening and identification of patients with a focus on a single tumor type or histology. Different therapy/biomarker combinations are evaluated in a single type of cancer: multiple subtrials that test targeted therapeutics within molecularly defined subsets are embedded within the umbrella framework. Basket trial designs offer the possibility to include multiple molecularly defined subpopulations, across histologic subtypes or tumor types, in one cohesive design to evaluate the targeted therapy in question. Patients with the different types of cancer are evaluated in separate substudies or baskets. Sumithra et al. (2015) ASCO Educational Book CRM - BARCELONA 10 September

29 Basket Trial Design Phenotype-to-Genotype strategy Helpful to identify therapies for rarer, harder to characterize cancers Patients with multiple types of cancer are enrolled as long as they have the particular molecular alteration of interest Extraordinary Responders Drive Concept of Basket Trial Design, ASCO Daily News, June 2014 CRM - BARCELONA 10 September

30 Basket Trial Design Frontier Science Foundation Hellas CRM - BARCELONA 10 September

31 Basket Trial Design: Example of ERBB2 Frontier Science Foundation Hellas CRM - BARCELONA 10 September

32 Basket Trial Design Provide insight into the functionality of the same genomic aberration across different tumor types Warning: if the trial has insufficient representation of patients with tumor types that harbor the aberration of interest, then concerns for false-negative conclusions would emerge Adaptive strategy: If early signals of antitumor activity are seen in particular tumor types harboring the relevant aberration, then accrual of more patients with these histologies will occur Sleijfer, 2013 JCO: Designing Transformative Clinical Trials in the Cancer Genome Era Frontier Science Foundation Hellas CRM - BARCELONA 10 September

33 Basket trials Limitations Require a strong scientific rationale for the molecular markerdrug pairing and reliable assay development for the marker of interest. Genomic variants have unknown or differential variability across tumor types (BRAF, HER2, EGFR expression) and there is often uncertainty about whether a particular mutation in a tumor of a particular histologic type should be considered actionable for treatment with a given drug. Resolving these uncertainties, however, is the reason for doing the study. Very rare cancers are often studied so that a randomized setting may be impossible. Statistical uncertainties: lack of or limited justification of sample sizes Sumithra et al. (2015) ASCO Educational Book CRM - BARCELONA 10 September

34 Concluding Remarks The use of adaptive design methods in clinical trials is motivated by its flexibility and efficiency, but there are still challenges in implementation. The flexibility afforded by adaptive designs should be compensated for by the potential loss in credibility associated with their use: Lack of justification of sample sizes (mostly in basket trials) Patient and tumor heterogeneity effect on the numerical variability of results Multiple comparison issues arising due to excessive testing of subgroups CRM - BARCELONA 10 September

35 Curse of multiplicity Common practice to perform multiple subgroup analyses The probability of a false positive finding (type-i error) increases as the number of subgroup analyses increases 10 analyses conducted > 40% chance at least 1 yields p analyses conducted > 50% chance at least 1 yields p 0.05 Easy to find one subgroup in which the treatment appears to work CRM - BARCELONA 10 September

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37 Concluding Remarks The methods for matching drugs to tumors are still rudimentary and numerous challenges remain to be addressed adequately (Sumithra et al ASCO Educational Book) The future will tell which of these innovative designs are useful and when they constitute a definite improvement over classic approaches. CRM - BARCELONA 10 September

38 Statistical Issues Adaptive Design Surrogate Endpoints Composite Endpoints Selective Crossover Multiple testing CRM - BARCELONA 10 September

39 Surrogate Endpoints A surrogate endpoint is one measured in place of the biologically or clinically definitive endpoint Critical issue is validity in assessing treatment s effect on clinical outcome based on its effect on the surrogate Points to Consider Surrogates are Disease-Specific Trials Using Surrogates Can Mislead (e.g., CAST, Cardiac Arrhythmia Suppression Trial) CRM - BARCELONA 10 September

40 Conceptual Framework for Assessing a Potential Surrogate Y=clinical outcome (e.g., Time to Progression) X=treatment group (e.g., Targeted therapy or Standard) Z=possible surrogate (e.g., Pathologic Response ) Suppose that data show that : X is associated with Y (Targeted therapy slows Progression) X is associated with Z (Targeted therapy improves Pathologic Response ) Z is associated with Y (Complete Response is associated with lower risk of Progression) Does it follow that Z (Pathologic Response ) is a valid surrogate for Y (Progression)?? CRM - BARCELONA 10 September

41 Conceptual Framework for Assessing a Potential Surrogate (continued) Required condition: P(Y X, Z) = P(Y Z) In words: effect of X (treatment) on Y (clinical outcome) is entirely a result of its effect on Z (surrogate). Thus, once we take account of Z, taking account of X doesn t add any information In practice, evaluation of surrogates is difficult Need outcome data for both Y (time to progression) and Z (pathologic response) for treated and untreated subjects Technical evaluation can involve complex statistical methods, especially when surrogate is a marker, that can be measured repeatedly over time CRM - BARCELONA 10 September

42 Surrogate Markers Difficult to fully assess whether a marker is a complete surrogate in the sense defined previously Often unrealistic to expect a single marker to be a complete surrogate If marker lies on one of several causal pathways to clinical outcome, then it s possible that: A treatment with a beneficial clinical effect can have little/no effect on surrogate A treatment with minimal clinical benefit can have large effect on surrogate Be cautious in use and interpretation of surrogates! CRM - BARCELONA 10 September

43 Statistical Issues Adaptive Design Surrogate Endpoints Composite Endpoints Selective Crossover Multiple testing CRM - BARCELONA 10 September

44 Composite Endpoint (CE) Rationale To address all efficacy measures deemed relevant to the success of a new treatment without the limitations imposed by multiplicity and competing risks problems to increase the power to detect a significant benefit induced by the new treatment by adding more common components to the relevant endpoint CRM - BARCELONA 10 September

45 Composite Endpoint (CE) The CE ε * of ε 1 and ε 2 : Relevant endpoint (RE), ε 1,that could be used as the primary endpoint for efficacy e.g., composite of cardiovascular death, stroke, resuscitated cardiac arrest and MI Additional endpoint of interest, (AE), ε 2 (secondary) e.g., hospitalization for unstable angina, coronary revascularization (ε 2 ). CRM - BARCELONA 10 September

46 Relevant Endpoint (RE) Cardiovascular death Resuscitated cardiac arrest Myocardial infarction Stroke ε 1 RELEVANT ENDPOINT CRM - BARCELONA 10 September 2015

47 Additional Endpoint (AE) Cardiovascular death Resuscitated cardiac arrest Myocardial infarction Stroke ε 1 RELEVANT ENDPOINT Hospitalization due to unstable angina Hospitalization due to coronary revascularization ε 2 ADDITIONAL ENDPOINT CRM - BARCELONA 10 September 2015

48 Composite Endpoint (CE) Cardiovascular death Resuscitated cardiac arrest Myocardial infarction Stroke ε 1 RELEVANT ENDPOINT Hospitalization due to unstable angina Hospitalization due to coronary revascularization ε 2 ADDITIONAL ENDPOINT ε * : COMPOSITE ENDPOINT CRM - BARCELONA 10 September 2015

49 Composite Endpoint (CE) The CE ε * of ε 1 and ε 2 Informed decision based on Asymptotic Relative Efficiency (ARE) rule ARE(ε*,ε1)>1 Use the CE instead of the RE ARE(ε*,ε1)<= 1 Retain the RE Gómez and Lagakos, Stat in Med, 2012 ; DOI: /sim CRM - BARCELONA 10 September

50 ARE depends on a) whether or not the two endpoints of interest include a terminal event (death), b) the probabilities p 1 and p 2 of observing events ε 1 and ε 2, respectively, for the control group, c) the treatment effect with respect to ε 1 and ε 2 given by the hazard ratios HR 1 and HR 2, d) the correlation between the times to event ε 1 and ε 2. Gómez and Lagakos, Stat in Med, 2012 ; DOI: /sim CRM - BARCELONA 10 September

51 ARE of composite versus relevant endpoint for a range of Spearman correlation coefficients and different values of HR 2 (β 1 = 2, β 2 = 2, p 1 = 0.082, HR 1 = 0.81, p 2 = 0.104). CRM - BARCELONA 10 September

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53 Thank you for your attention CRM - BARCELONA 10 September