NATIONAL INSTITUTE FOR HEALTH AND CLINICAL EXCELLENCE. SINGLE TECHNOLOGY APPRAISAL Certolizumab Pegol for Rheumatoid Arthritis

Transcription

1 NATIONAL INSTITUTE FOR HEALTH AND CLINICAL EXCELLENCE SINGLE TECHNOLOGY APPRAISAL Certolizumab Pegol for Rheumatoid Arthritis Clarifications and Reponses to NICE 1

2 Contents Executive summary... 3 Section A: Clarifications of the effectiveness data... 5 A1 Literature searching... 5 A2 Systematic review... 5 A3 Certolizumab pegol trials... 6 A4 Adverse event data... 7 A5 Meta-analysis A6 Indirect/mixed treatment comparisons Section B: Clarification of cost-effectiveness analysis B1 Structure of the model B2 Utility mapping methodology B3 Model assumptions B4 Cost estimates in the model B5 Sensitivity analysis B6 Miscellaneous B7 Excel model B8 Proposed Patient Access Scheme Section C: Revised cost-effectiveness modelling SCENARIO A2 Revised CE results (incorporating PAS) SCENARIO B2 Revised CE results (not incorporating PAS) Section D: Additional references

3 Executive summary Overview In this document UCB provides NICE additional evidence as requested in the letter from Meindert Boysen of 10 July In particular, we provide: 1) An item by item response to clarification questions asked by NICE/ERG. These responses are based on the originally submitted economic model and are presented in sections A and B according to NICE s question numbering. 2) Revised cost-effectiveness modelling, presented in section C. This incorporates three changes to the submitted economic model and, in doing so, addresses points of clarification raised in sections A and B. The revised model and results presented in section C should be considered as a replacement to the model and results presented in and with the original submission. Section summary For section A (clarification of the effectiveness data), none of the questions were highlighted as priorities by NICE/ERG, and the responses to these questions did not raise any issues inconsistent with the original submission. For section B (clarification on cost-effectiveness analysis), the majority of the questions posed by NICE/ERG were addressed by providing further explanation and detail, and correcting any misunderstandings. This included: a full listing of all variables used in the model (B1a) tabular presentation of the sequence of drugs used in the model (B1b) expanded explanation of the derivation of response rates from indirect comparisons (B1d) expanded explanation and rationale for the methodology used to model the relationship between HAQ-DI and mortality (B1e) and utility mapping methodology (B2) presentation of side-by-side comparisons of response rates from metaanalyses/indirect analyses and individual trials (B6c) confirmation that CZP will be available for home injection for all users (B3b) UCB has, unlike previous models submitted to NICE, modelled utility using directly measured EQ-5D data as far as possible, rather than HAQ. This approach is clarified and the rationale behind it explained in detail (B2a) 3

4 For three priority issues, the NICE/ERG request for clarification highlighted amendments and improvements required for the submitted model. This has resulted in a revised model presented in section C. The changes from the originally submitted model are as follows. The assumptions around the HAQ progression rate have been revised the impact this revision has on the originally submitted results is presented in B3a, and the new assumptions are incorporated into the revised model presented in section C. (B3a) The assumption in the original submission of differential adverse event effects between comparator therapies is now considered inappropriate, and thus the newly presented model presented in section C does not directly include any adverse events. (B1a) More precise figures were presented for the probability of treatment discontinuation, with a sensitivity analysis describing how these figures affect the ICERs presented in the original submission. These figures were also used in generating the revised model in section C. (B1c) In addition, certain priority questions regarding sensitivity analyses and presentation of uncertainty analyses involved generation of further results. These results are presented in section B in relation to the originally submitted model and in section C in relation to the revised model. The main additional output was as follows: A sensitivity analysis was presented around the assumptions of drug wastage/non-wastage, both based on the original submission, and in section C, based on the revised model. In all scenarios, this sensitivity analysis showed that CZP+MTX remained dominant over infliximab + MTX. (B4a) Adjusted baseline HAQ scores. (B5b) Presentation of PSA, including cost effectiveness acceptability frontiers. (B6a/b) Section D provides additional references. Conclusion The revised model continues to demonstrate the cost-effectiveness of CZP within the NHS in England and Wales, with a probability greater than 50% of being within NICE s 30,000 per QALY threshold for CZP + MTX compared to combination therapies, and greater than 40% for CZP compared to monotherapies (Table 42 and Table 44). By accounting for the proposed Patient Access Scheme, further savings are provided to the NHS and the degree of cost-effectiveness is even greater. 4

5 Section A: Clarifications of the effectiveness data A1 Literature searching a. Please state if searches for ongoing studies were undertaken. Please provide details. Searches for ongoing studies were not undertaken, as these would be unlikely to provide finalised results of similar quality to the fully published studies included in the systematic review. However hand searching was undertaken in order to capture results that were finalised but not yet fully published. There are no ongoing trials that UCB are aware of, funded by UCB or others, involving CZP in RA. b. Flow diagram (figure 3, section 6.1) mentions three references retrieved from hand searching please state which sources were hand searched. As stated in Section 6.1 of the submission, abstracts from the following conferences were hand searched: European League against Rheumatism (EULAR), 2004 to 2008, via the website American College of Rheumatology (ACR), 2006 to 2008, via the website If a study was found published as a conference abstract and had no link to a primary publication from a journal, a full publication of the study was searched via PubMed. These references are described as hand searched in the systematic review flow diagram. A2 Systematic review a. Please provide a list of non-english language studies that were identified, but not included in the review. Seven references published since 2007 were identified which could possibly have met the systematic review inclusion criteria but were excluded for the reason that they were non-english language studies. Information about studies dating from 2007 or earlier that were excluded on grounds of language is not available. 5

6 Table 1: Studies excluded due to non-english language Author Title Journal Nasonova VA., Lukina [Neutralisation of interferon gamma--a new trend Terapevticheskii GV., Sigidin IaA. in therapy of rheumatoid arthritis]. [Russian] Arkhiv Iking-Konert C. Therapy-refractive rheumatoid arthritis: Aktuelle Effectiveness and reliability of abatecept and Rheumatologie infliximab. Heinzl S. Rheumatoid arthritis: New data about Deutsches tocilizumab. Arzteblatt Schlegel A. Dejaco C., Duftner C. Iking-Konert C. Jungmayr P. Anti-interleukin-6 antibodies: Tocilizumab in rheumatoid arthritis and in juvenile idiopathic arthritis. Effect of interleukin-6 receptor inhibition with tocilizumab in patients with rheumatoid arthritis (OPTION study): A double-blind, placebocontrolled, randomised trial. Tocilizumab inhibits radiological progression in rheumatoid arthritis. Rheumatoid arthritis: IL-6 inhibition with tocilizumab interrupts the inflammation. Medizinische Monatsschrift fur Pharmazeuten Journal fur Mineralstoffwechsel Aktuelle Rheumatologie Deutsche Apotheker Zeitung Year Citation (5) (5) (36) A (9) (2) (1) (32) A3 Certolizumab pegol trials a. Please identify the number of cross-overs that occurred in the CZP trials. The trials were not designed as cross over trials and no patients from any of the trials moved between arms during the active phase of the studies. At certain completion points patients were enrolled into the open label extension from different arms of the placebo control of RAPID 1 and RAPID 2. In these two trials most patients exit from one treatment arm at one specific timepoint (week 16 withdrawers or end of study completers). b. Please identify the times at which losses-to follow up occurred for patients in the CZP trials. Losses to follow up were very small during the two main trials, RAPID 1 and RAPID 2 with one patient lost from each arm in RAPID 1 (one from placebo and one from each of the two active arms, so three patients in total) and no patients lost to follow up in the RAPID 2 trial. c. Please state the proportion of values for ACR 20, 50, and 70 that were LOCF for each arm in the CZP trials. The proportion of ACR 20 scores that were not observed but instead imputed at endpoint (week 24) as last observation carried forward or NRI of the RAPID 1 and RAPID 2 trials is shown in Table 2. Comment [DM1]: Why is placebo so high? Comment [DM2]: What is this abbreviation? 6

7 Table 2: Proportion of ACR 20 scores at endpoint in RAPID 1 and RAPID 2 that were imputed Placebo + MTX CZP 200 mg + MTX CZP 400 mg + MTX RAPID 1 77% 32% 26% RAPID 2 87% 29% 27% d. Please provide the trial report (and/or other analyses) for the long term extension of the RAPID 1 study (and RAPID 2 if available) and any other long term CZP studies. No formal study report was created for the long term extension Rapid 1 trial. Data is available as tables. Specific tables can be provided upon request. Additionally abstracts and posters have been created and will be provided upon request. A4 Adverse event data a. Please state what is the source of adverse event data presented in section 6.7. The safety data presented is based on the summary of safety of certolizumab pegol out of 10 studies in RA, with the cut off date specified (August 2007). b. Please explain why the total number of patients with RA (2367, p.93) included in the safety analyses is higher than the total number in the three included studies (1819 in safety population, based on figures 4-6). The overall RA population (2367) includes pooled safety data from 1 pharmacokinetic study, 6 placebo-controlled, and 3 open-label extension studies in RA subjects as follows: A one-dose (16-subject), open-label pharmacokinetic Study PHA001. Analyses of the overall summary of exposure and of adverse events (AEs) are included where appropriate. Two Phase 2, double-blind placebo-controlled Studies C87002 (CDP ) and C87004 (CDP ). Four Phase 3 double-blind, placebo-controlled Studies C87011 (CDP ), C87014 (CDP ), C87027 (CDP ), and C87050 (CDP ). 7

8 Three ongoing Phase 3 open-label Studies C87015 (CDP ; extension to C87011 and C87014), C87028 (CDP ; extension to C87027), and C87051 (CDP ; extension to C87050). The safety population (1819) refers to patients in trials CDP , CDP and CDP c. Please provide adverse event data separately for: RCTs on CZP compared to control in patients with RA, long-term extensions of the above, and any other study categories. We present in Table 3 to Table 6 results from UCB trials describing the time of occurrence of adverse events. There were no adverse events beyond 18 months in CZP 200 mg every 2 weeks. Table 3: Time of occurrence of adverse events in all studies, CZP 200 mg every 2 weeks, safety population, 0-18 months Time of occurrence <6 months 6-<12 months months N=640 N=263 N=251 N1 N2 P N1 N2 P N1 N2 P Any adverse events Intensity (a) Mild Moderate Severe Relationship to study drug (b) No/unrelated/none Unlikely Possible Probable Highly probable Yes/Related/Definite Related to study drug (c) Serious adverse events Adverse events leading to death Adverse events leading to withdrawal Table 4: Time of occurrence of adverse events in all studies, CZP 400 mg every 4 weeks, safety population, 0-18 months Time of occurrence <6 months 6-<12 months months N=513 N=418 N=353 N1 N2 P N1 N2 P N1 N2 P Any adverse events Intensity (a) Mild Moderate Severe Relationship to study drug (b) No/unrelated/none Unlikely Possible Probable Highly probable Yes/Related/Definite Related to study drug (c) Serious adverse events

9 Adverse events leading to death Adverse events leading to withdrawal Table 5: Time of occurrence of adverse events in all studies, CZP 400 mg every 4 weeks, safety population, months Time of occurrence 18-<24 months 24-<30 months 30-<36 months N=307 N=282 N=268 N1 N2 P N1 N2 P N1 N2 P Any adverse events Intensity (a) Mild Moderate Severe Relationship to study drug (b) No/unrelated/none Unlikely Possible Probable Highly probable Yes/Related/Definite Related to study drug (c) Serious adverse events Adverse events leading to death Adverse events leading to withdrawal Table 6: Time of occurrence of adverse events in all studies, CZP 400 mg every 4 weeks, safety population, 36 months onwards Time of occurrence 36-<42 months 42-<48 months >=48 months N=260 N=233 N=145 N1 N2 P N1 N2 P N1 N2 P Any adverse events Intensity (a) Mild Moderate Severe Relationship to study drug (b) No/unrelated/none Unlikely Possible Probable Highly probable Yes/Related/Definite Related to study drug (c) Serious adverse events Adverse events leading to death Adverse events leading to withdrawal Notes to the tables: AEs are displayed as: N1: Number of AEs N2: number of subjects N3: percentage of subjects A subject experiencing more than one AE in a category is counted only once in that category. AEs with missing intensity are counted as Severe. AEs with missing 9

10 relationship are counted as Related. AEs with a missing value for Serious are counted as Serious. (a) Adverse events with changing intensity over the course of a study are included only for the maximum reported intensity. (b) Adverse events with changing relationship to study drug over the course of time are included only for the maximum reported relationship. (c) Adverse events with a possible, probable, highly probable, definite or yes (where only yes/no recorded) relationship to study drug are categorised as Related. Denominators are based on the number of patients who could have had an adverse event in the time period indicated. d. Please indicate for the adverse event data what the time on treatment was and what the length of follow-up was. At the cutoff date, the total exposure was 4065 patient years with certolizumab pegol in RA. This corresponded to a mean duration of exposure of 20.6 months. A summary of exposure by duration is presented in Table 7. Table 7: Duration of exposure to CZP Duration of exposure (months) % Patients < to < to < to < to < to < to < to < e. Please state how the adverse event results in patients with RA compare to results from studies of CZP in non-ra patients. In the placebo-controlled studies, the percentage of subjects in the All CZP Doses group who experienced treatment-emergent adverse events (TEAEs) when compared with the respective placebo group for each of the indications of RA, CD and psoriasis was similar. Adverse events (AEs) in the System Organ Class (SOC) of Infections and infestations were the most commonly reported AEs across all indications. Differences were seen in the reported incidence of AEs in the Gastrointestinal (GI) SOC for CD as compared with RA. While the incidence rate of events per 100 patient-years was 10

11 similar between placebo and the All CZP groups within each indication, in line with GI pathology the overall incidence rate of GI events in CD was approximately 4-fold higher than that in RA. In RA, there was a higher incidence rate of Cardiovascular AEs (including hypertension, cerebrovascular accidents, arrhythmias and heart failure) than seen in CD. The increased incidence of cardiovascular events seen in RA most likely reflects the RA patient population s predisposition to cardiovascular disease, other underlying comorbidities, and higher mean age as compared with the CD patient population. For psoriasis, there were too few subjects studied to discern a difference in AE incidence as compared with RA. f. Please state what was the proportion of patients with RA excluded from CZP trials due to an association with infections and TB. In Studies 027 and 050 patients were excluded due to a history of chronic infection, recent serious or life-threatening infection, or any current sign or symptom that may indicate an infection, as were patients at a high risk of infection in the Investigator s opinion. Additionally, patients with a history of tuberculosis, positive chest X ray for tuberculosis, or positive (defined as positive in duration per local medical practice) PPD skin test were excluded. Patients with a positive PPD skin test associated with previous vaccination and where there was no clinical or radiographic suspicion of TB could be enrolled at the discretion of the Investigator. Consideration was asked to be given to the fact that a positive PPD skin test with prior vaccination did not exclude latent TB. Of the 1262 patients screened and the 114 patients excluded from randomization in Study 050, approximately 21 (21/1262, 2%) were excluded due to the tuberculosis criteria and 4 (4/1262, 0.3%) due to the infection criteria. Of the 733 patients screened and the 280 patients excluded from randomization in Study 027, approximately 41 (41/733, 6%) were excluded due to the tuberculosis criteria and 6 (6/733, 0.8%) due to the infection criteria. A5 Meta-analysis a. As stated (p.67), relative risks (ACR results) in tables 20 and 21 are calculated CZP/ comparator, but in the forest plots (figures 27-29) are represented comparator/ CZP. Please explain why the results are presented differently. Please clarify forest plots and tables to indicate 11

12 which of CZP or control is the more effective intervention (e.g. by adding CZP more effective of control more effective as appropriate below the axis in the forest plots). Similarly for figures In summary, in Tables 20 and 21 of the submission, effect size estimates greater than 1 indicate that the effect is higher for CZP + MTX than MTX + placebo. The reverse is true of Figures 27 to 36 of the submission. The following paragraphs contain further explanation. Relative risks in Table 20 of the submission were presented as CZP + MTX relative to the comparator, placebo + MTX. ACR risk ratios greater than one are indicative in this table of CZP + MTX being more effective than placebo + MTX; ACR risk ratios less than one are indicative of placebo + MTX being more effective than CZP + MTX. All the risk ratios and their CIs are substantially greater than one, suggesting strong, consistent and statistically significant evidence for the effectiveness of CZP + MTX relative to placebo + MTX in terms of ACR response as quantified by a risk ratio. Absolute risk reductions rather than relative risks are presented in Table 21 of CZP + MTX relative to placebo + MTX. A positive risk reduction for ACR outcomes is suggestive of CZP + MTX being more effective than placebo + MTX; a negative risk reduction for this outcome is suggestive of placebo + MTX being more effective than CZP + MTX. All the risk reductions and their CIs are substantially greater than zero, suggesting strong, consistent and statistically significant evidence for the effectiveness of CZP + MTX relative to placebo + MTX in terms of ACR response as quantified by an absolute risk reduction or risk difference. Figures are forest plots for the relative risks of placebo + MTX vs CZP + MTX for ACR response outcomes. ACR risk ratios less than one are indicative of CZP + MTX being more effective than placebo + MTX; ACR risk ratios greater than one are indicative of placebo + MTX being more effective than CZP + MTX. All the risk ratios and their CIs are substantially less than one, suggesting strong, consistent and statistically significant evidence for the effectiveness of CZP + MTX relative to placebo + MTX in terms of ACR response as quantified by a risk ratio. Figures are forest plots of risk ratios of placebo + MTX vs CZP + MTX for adverse health outcomes (`mortality, `withdrawal due to lack of efficacy at 3 months, `any adverse event, `any serious adverse event, `malignancies (all) and `injection site reactions ). Risk ratios less than one are indicative of CZP + MTX 12

13 being more harmful than placebo + MTX; risk ratios greater than one are indicative of placebo + MTX being less harmful than CZP + MTX. Figure 36 is a forest plot of a (weighted) mean difference of placebo + MTX vs CZP + MTX for a beneficial health outcome other than ACR response: `Van der Heijde mtss change from baseline at 6 months. A positive value is suggestive of CZP + MTX being beneficial compared to placebo + MTX; a negative value is suggestive of placebo + MTX being beneficial compared to CZP + MTX. b. Please state if the results provided in table 20 and 21 and figures refer to patients who were given 200 mg of CZP, 400 mg of CZP or if both groups were analysed together. Only results from the CZP 200 mg plus MTX arms of the RAPID trials were pooled in these analyses. A6 Indirect/mixed treatment comparisons a. Please confirm whether random-effects or fixed effects meta-analysis was used in the indirect analysis. The indirect analysis reported in section 6.6 used a random effects meta-analysis. A random rather than fixed effects analysis was conducted due to the perceived likelihood of heterogeneity. The submission reports the use of the following formula for deriving point estimates of the indirect treatment effect: The variance of the indirect treatment effect was calculated as follows Confidence intervals presented in tables 26-28, 30-32, and were calculated on the basis that the sampling distribution of the indirect treatment effect on a log odds scale was a Normal distribution. b. Please state how do the indirect analysis results compare to evidence from the two head-to-head trials excluded from indirect analysis (table 22, studies: De Filippis 2006 and OPPOSITE). 13

14 As noted in table 22 of the submission, De Filippis et al 2006 [53] and OPPOSITE [56] were studies of IFX + MTX vs ETA + MTX that were excluded from the indirect analysis because they were not central to the comparison of interest and due to the small patient population. Relevant network diagrams are shown in figures 37 and 38 absent these studies. The results from these two studies are presented in Table 8. This may be considered additional to information presented in Tables 25 and 29 of the submission. Note that various cells in this table are blank due to the outcome not being collected or reported in the studies. Table 8: ACR data for two studies excluded from the indirect analysis Study Outcome Treatment Patients with response ITT ACR20 ACR50 ACR70 patients De Filippis et ACR at 3 IFX + MTX al 2006 [53] months ETA + MTX ACR at 6 IFX + MTX months ETA + MTX OPPOSITE ACR at 3 IFX + MTX [56] months ETA + MTX ACR at 6 months IFX + MTX ETA + MTX The indirect analyses of IFX + MTX vs ETA + MTX for 6 month response were included in Tables of the submission and for 3 month response in Tables These are repeated in Table 9 together with new, direct estimates of the effect of IFX + MTX vs ETA + MTX, as summarised as odds ratios. For ACR20 response at 3 months both trials provide data for the analysis and odds ratios were calculated by meta-analytic pooling of the data in Table 8 using Stata. For ACR20 response at 6 months and ACR50 response at 3 months data on each comes from one study only. There is no data from these trials on ACR50 response at 6 months or ACR70 response at 3 or 6 months. Table 9: Direct and indirect comparisons of IFX + MTX vs ETA + MTX (odds ratios, 95% CIs, p-value vs null hypothesis of OR=1) 3 months Direct to 7.69 P=0.07 Indirect to 3.75 P= months Direct to 4.04 P=1.0 ACR 20 ACR 50 ACR to P= to 1.34 P= to 0.8 P<

15 Indirect to 1.39 P= to 1.53 P= to 5.28 P=0.4 There is limited data on ACR response rates available from the OPPOSITE trial and the De Filippis et al 2006 study therefore comparisons of indirect and direct results cannot be made in certain cases. Where direct result can be calculated, 95% confidence intervals are wide and results non-significant due to small patient numbers in these trials. Overall, direct results tend to favour a higher ACR response rate in the infliximab plus MTX treatment groups, whereas indirect comparisons tend to favour a higher ACR response rate in the etanercept plus MTX treatment groups, however results are non-significant by both methods of analysis. Since the submission is concerned with differences in efficacy between CZP and comparators, the inclusion or exclusion of the tenuously indirect evidence supplied by these two small studies in the few outcomes each reports is believed to be immaterial. c. Please clarify how the heterogeneity (I 2 ) was calculated for comparisons within one study. For example in table 26 (p. 84) I 2 for Tocilizumab + MTX versus MTX was 100% when only data from the OPTION trial was used. Heterogeneity (I 2 ) results for comparisons where data came from one study should not have been present in tables 26-28, 30-32, and as the calculation is not meaningful. For the heterogeneity results omitting the I 2 values from single trials, please refer to Table 6 through Table 11 in the answer to question B6c. d. Please identify any software used for indirect comparison and provide details of any code that may have been used. Meta-analysis was conducted using the metan command written for Stata. Indirect analysis was conducted in MS Excel from meta-analysis output produced by Stata according to the formulae quoted and referred to in the answer to clarification question A6a. Specialist software such as WinBugs was not necessary. 15

16 Section B: Clarification of cost-effectiveness analysis B1 Structure of the model a. Priority question: Please provide a table listing all variables used in the model, with variable names, what they do and what values they take, along with the uncertainty estimates (95% CIs or values that define the distribution as appropriate) and the evidence base (reference) justifying these. We have divided our response between deterministic inputs and probabilistic inputs. Deterministic inputs describe the point estimates used in the model in determining deterministic results. Probabilistic inputs describe the CIs, distributions, rationale and sources used in probabilistic analyses. Deterministic inputs The probability of response, defined in terms of ACR >20, >50 and >70 responders for 3 and 6 month response time points are identified in Table 10 and Table 11. The risk values as used in the model define the proportion of cohort subjects within each health state (ACR response category <20, 20-50, 50-70, >70) after the first cycle. All values are obtained from the indirect analysis results reported within section 6.6 of the submission document. Further details on the derivation of the risk values are defined in response to B1d. Model structure The continuation rule applied to both arms of the model is that patients with an inadequate response, defined as ACR <20, discontinue from treatment after the first cycle. Table 10: Risk of ACR response at 3 months Treatment ACR 20 ACR 50 ACR 70 Infliximab + MTX 58.6% 27.0% 19.6% Tocilizumab + MTX 52.3% 18.0% 5.8% Adalimumab 55.3% 25.7% 16.4% Adalimumab + MTX 70.8% 0.0% 0.0% Etanercept 46.2% 21.4% 5.1% Etanercept + MTX 66.4% 61.1% 23.7% Placebo 14.0% 3.2% 1.1% Certolizumab pegol 53.3% 45.1% 11.6% Certolizumab pegol + MTX 71.1% 35.9% 21.6% 16

17 Table 11: Risk of ACR response at 6 months Treatment ACR 20 ACR 50 ACR 70 Infliximab + MTX 48.2% 26.1% 11.3% Rituximab + MTX 58.4% 35.6% 17.6% Tocilizumab + MTX 55.7% 40.9% 35.2% Adalimumab 41.3% 16.2% 11.2% Adalimumab + MTX 61.0% 41.8% 19.7% Etanercept 64.2% 38.1% 16.8% Etanercept + MTX 68.5% 66.4% 30.7% Placebo 13.1% 5.7% 1.0% Certolizumab pegol 55.9% 31.4% 12.3% Certolizumab pegol + MTX 77.2% 49.2% 28.2% Adverse events The broad safety profile of certolizumab pegol versus placebo appears similar to that reported for other biologic TNF-alpha antagonists compared to placebo, as discussed in the submission and in response to clarification questions in A4. The literature on the safety of bdmards is reported in different ways making reliable direct comparison between agents impossible. We thus did not to include any adverse events directly into the model newly presented in section C of this document. This approach is also consistent with the one adopted in a NICE authored model in early RA [2, Health economic appendix]. Costs of adverse events were not incorporated in the model submitted, but there was some impact on QALYs that were assumed to arise from adverse events that differed between treatments. We now view this assumption of differential adverse event effects as inappropriate and inconsistent with the accounting of costs. This issue is remedied in the model presented in section C. Mortality The relative risk of death is expressed in terms of per unit HAQ and was obtained from Wolfe et al [96]. The value of 1.33 is used in calculating the probability of death associated with each health state, as defined by the HAQ score for the given state. Mortality assumptions are as described in response to B1e. Treatment discontinuation The probability of treatment discontinuation is modelled using an exponential distribution, with parameters derived from the mean time on treatment (years). For 17

18 all bdmard treatment, the mean time on treatment is 37 months [107]. A summary of the values as used in the model are presented in Table 12. See also B1c. Table 12: Mean time on treatment (years) for comparators and follow up treatments Treatment Model Assumption Mean time on treatment (years) Adalimumab (comparator) As per BNF Azathioprine 2mg/kg, adjusted for weight distribution (Chen 3.08 (subsequent) et al., 2006) Cyclosporine 3.25mg/kg daily, adjusted for weight distribution 3.08 (subsequent) (Brennan et al., 2006) Etanercept As per BNF (comparator) Hydroxychloroquine 400 mg daily (Brennan et al., 2006) 7.31 (subsequent) Infliximab As per BNF, adjusted for weight distribution 3.08 (comparator) Leflunomide 20 mg daily (Chen et al., 2006 ) 5.98 (subsequent) Penicillamine 500 mg per day (Chen et al., 2006) 4.20 (subsequent) Rituximab As per BNF, repeated courses every 307 days 3.08 (comparator) (Manufacturer submission, 2007) Gold (sodium aurothiomalate) 50 mg weekly for 10 weeks, then fortnightly for 3.91 (subsequent) 20 weeks, monthly thereafter (Clinical opinion, Chris Deighton, personal communications) Sulfasalazine (subsequent) 2.5 mg per day (Chen et al., 2006) Utilities Please see response B2a for a description of the utility modelling, further to the details provided in section of the submission document. Table 13 provides results of a regression analysis used to inform the assumption as to treatment benefit effect, as described in response to item B2a. The mean changes in EQ-5D utility associated with treated patients are obtained from a regression analysis of EQ-5D vs. ACR for both 3 and 6 month data points. The data for this analysis was from the placebo + MTX and 200 mg CZP + MTX arms of the RAPID 1 and RAPID 2 studies. 18

19 Table 13: Regression Analysis EQ-5D versus ACR for response at 3 or 6 months Description Coefficients if response measured at 3 months Coefficients if response measured at 6 months Intercept No ACR ACR50, no ACR ACR20, no ACR No ACR20 * placebo + MTX ACR50, no ACR70 * placebo + MTX ACR20, no ACR50 * placebo + MTX Age Antibody status (Negative) Baseline utility score Number of previous DMARD Disease duration Gender (Male) Treatment: placebo + MTX The mean change in utility for both treatments are calculated based on the regression analysis above and the cohort baseline characteristics, assumed as the pooled data for CZP trials (Table 14). Baseline characteristics Table 14: Assumed characteristics of cohort Source Data Value Age: Mean Age: Standard error N 1821 Gender, n female 1506 % Female 82.7% DMARD: Mean DMARD: Standard error Disease Duration: Mean Disease Duration: Standard error HAQ-DI: Mean HAQ-DI: Standard Error Utility Weight (EQ-5D): Mean Utility Weight (EQ-5D): Standard error Source: pooled data from trials 027 (RAPID 1) and 050 (RAPID 2). Costing The direct costs of RA management by HAQ-DI score is obtained from Kobelt et al 2002 (117) and shown in Table 15. These costs included hospitalizations, surgical interventions, ambulatory and community care and RA medication. The costs are applied at each cycle within the model, based on the HAQ-DI score of each health state at each time point. The costs are obtained in US dollars year 2001, and subsequently inflated (Table 61 of original submission) and converted to pounds using an applied exchange rate of 0.67 to $

20 Table 15: Cost by HAQ Score (2001 US dollars) [117] HAQ Range Direct Cost Indirect Cost < 0.6 $1,228 $ <1.1 $3,152 $2, <1.6 $2,091 $3, <2.1 $3,087 $5, < 2.6 $3,401 $8,070 >2.6 $2,697 $8,407 Resourcing and unit costs The parameters for total cost per patient are aggregated by resource and unit costs, as tabulated in tables of the submission document. For the cost of drugs, administration and monitoring we refer to section of the submission. From section : Drug dose and frequency of administration are based on British National Formulary 57 (119) recommendations where possible and are presented in Table 54. The doses of all agents are assumed to be stable with time. For drugs for which the dose is adjusted for patient weight (infliximab, rituximab, azathioprine and cyclosporine), the weight distribution [(Table 16)] of the RA population in the clinical trials of CZP was used to calculate the percentages of patients receiving a specified number of vials. The mean cost of the drug (per administration) was then calculated by combining these percentages with the cost of the drug. `The model takes into account drugs which have an initial dosing schedule followed by a maintenance dosing schedule, which is the case also for CZP. `For drugs which start dosing at week 0 (CZP and infliximab), any dose given at week 26 is assumed to fall into the subsequent six months dosing schedule rather than the first six month schedule. Table 54 (of the submission): Drug dosing schedule Intervention Dose/schedule mg per administration Number of administrations First six months Subsequent six months Certolizumab pegol + MTX 400 mg at week 0, 2 and 4, and 200 mg every 2 weeks thereafter Certolizumab pegol 400 mg at week 0 and every 4 weeks thereafter Infliximab + MTX 3mg/kg weeks 0, 2, 6 then every 8 weeks. Body weight dependent Adalimumab + MTX 40 mg on alternate weeks Etanercept 25 mg twice weekly

21 Etanercept + MTX 25 mg twice weekly Rituximab + MTX Course of 1 g, repeated 2 weeks (course of 2 doses); repeated courses every 307 days Azathioprine 2 mg/kg daily body weight dependent Cyclosporine 3.25 mg/kg daily body weight dependent Gold 50 mg weekly for 10 weeks, then fortnightly for 16 weeks, monthly thereafter Hydroxychloroquine 400 mg daily Leflunomide 20 mg daily Penicillamine 500 mg daily Sulfasalazine 2.5 g daily Palliation Methotrexate 15 mg weekly Methylprednisolone 100 mg co-administered with rituximab SOURCE: (119) Table 16: Weight distribution of Patients Lower Weight Boundary Upper Weight Boundary % Patients Cumulative % Patients 0 0.0% 0.0% % 0.0% % 0.1% % 0.3% % 0.8% % 1.6% % 4.6% % 12.6% % 22.6% % 37.6% % 57.6% % 87.6% % 97.6% % 98.5% % 99.2% % 99.7% % 99.9% % 100.0% % 100.0% Source: pooled data from trials CDP , 027 and 050. From section : For drugs administered by IV infusion (infliximab and rituximab), cost estimates are based on the following assumption: 8% of patients receive treatment in an outpatient setting and 92% of patients receive treatment in a day case setting. This assumption is based on information from NICE guidance for the use of infliximab for the treatment of adults with psoriasis (120). For subcutaneous injections (CZP, adalimumab and etanercept), patients are assumed to receive one hour of nurse time for education, training and assessment. All patients are then assumed to be able to administer the drug themselves at home without further assistance. 21

22 The drug monitoring schedule, including tests and visits, was taken mainly from two sources (97;98). Where estimates were not available, other published literature was consulted. The drug monitoring schedule of tests for CZP and rituximab was assumed to be the same as for etanercept. The drug monitoring schedule of visits for the subcutaneously administered treatments (CZP and adalimumab) was assumed to be the same as that for etanercept. The number of monitoring visits for drugs administered by IV infusion (rituximab) was assumed to be the same as that for infliximab (Table 55, Table 56 and Table 57). For IV treatments, monitoring associated with MTX was assumed to take place during administration visits (Table 58). Table 55 (of the submission): Drug monitoring schedule: pre-treatment tests Intervention Full Blood Count (FBC) Erythrocyte Sedimentatio n Rate (ESR) Biochemical Profile (BCP) Chest X-ray (CXR) Urinalysis Certolizumab pegol Certolizumab pegol + MTX Infliximab + MTX Rituximab + MTX Adalimumab Adalimumab + MTX Etanercept Etanercept + MTX Placebo Placebo + MTX Azathioprine Cyclosporine Gold Hydroxychloroquine Leflunomide Penicillamine Sulfasalazine Palliation Methotrexate SOURCE: (97;98) Table 56 (of the submission): Drug monitoring schedule: tests on treatment, first six months Intervention Full Blood Count (FBC) Erythrocyte Sedimentatio n Rate (ESR) Biochemical Profile (BCP) Chest X-ray (CXR) Urinalysis Certolizumab pegol Certolizumab pegol + MTX Infliximab + MTX Rituximab + MTX Adalimumab Adalimumab + MTX Etanercept Etanercept + MTX Placebo Placebo + MTX Azathioprine Cyclosporine Gold Hydroxychloroquine Leflunomide

23 Penicillamine Sulfasalazine Palliation Methotrexate SOURCE: (97;98) Table 57 (of the submission): Drug monitoring schedule: tests on treatment, subsequent six months Intervention Full Blood Count (FBC) Erythrocyte Sedimentatio n Rate (ESR) Biochemical Profile (BCP) Chest X-ray (CXR) Urinalysis Certolizumab pegol Certolizumab pegol + MTX Infliximab + MTX Rituximab + MTX Adalimumab Adalimumab + MTX Etanercept Etanercept + MTX Placebo Placebo + MTX Azathioprine Cyclosporine Gold Hydroxychloroquine Leflunomide Penicillamine Sulfasalazine Palliation Methotrexate SOURCE: (97;98) Table 58 (of the submission): Drug monitoring schedule: visits Intervention First six months Subsequent six months GP visits Outpatient visits GP visits Outpatient visits Certolizumab pegol Certolizumab pegol + MTX Infliximab + MTX Rituximab + MTX Adalimumab Adalimumab + MTX Etanercept Etanercept + MTX Placebo Placebo + MTX Azathioprine Cyclosporine Gold Hydroxychloroquine Leflunomide Penicillamine Sulfasalazine Palliation Methotrexate SOURCE: (97;98) From section : Healthcare staff costs are based on unit costs taken from The Unit Costs of Health and Social Care (121). The cost of an outpatient visit is based on the national average unit cost taken from the NHS Reference Costs (122). Unit costs of tests are taken from Chen et al. (97). The costs of IV infusion are assumed to be 64 for 23

24 administration of IV drug in an outpatient setting and 1334 for administration in a day case setting and are based on information from NICE guidance for the use of infliximab for the treatment of adults with psoriasis (120). Resource unit costs are detailed in Table below. Table 59 (of the submission): Resource unit costs Resource Cost ( ) Description Cost year Healthcare visits and staff time GP surgery consultation (based on an 2008 General Practitioner minute consultation) Nurse (GP Practice) Per hour of client contact 2008 District Nurse Per home visit 2008 Per hour of patient contact (based on 2008 Nurse (In Patient) day ward pay band 5) Per patient-related hour (based on the 2008 Rheumatologist unit cost for consultant) Hospital Pharmacist Per cost of patient-related activities 2008 Per patient-related hour (based on the 2008 Anaesthesiologist unit cost for consultant) Outpatient visit first attendance Consultant led first attendance outpatient face to face visit. Outpatient visit follow-up attendance Consultant led follow-up attendance outpatient face to face visit. Administration of IV drug in outpatient 2008 setting Administration of IV drug in day case setting Tests Full Blood Count Erythrocyte Sedimentation Rate Biochemical Profile Urinalysis Chest X-ray SOURCE: (97; ) Drug unit costs were obtained from the British National Formulary recommendations, BNF 57 (119) (Table 60). All drugs are assumed to be taken continuously. The price per 200 mg of CZP is assumed to be so as to achieve parity with the adalimumab price of per 40 mg in terms of the yearly cost after induction. 24

25 Table 60 (of the submission): Drug unit costs Intervention Route of administration Cost per pack ( ) Presentation Strength (mg) Cost year Certolizumab pegol Subcutaneous 2009 injection * pre-filled syringe 200 Certolizumab pegol + Subcutaneous 2009 MTX injection * pre-filled syringe 200 Infliximab + MTX IV infusion Vial Rituximab + MTX IV infusion Vial Tocilizumab IV infusion Vial Tocilizumab + MTX IV infusion Vial Subcutaneous 2009 Adalimumab injection pre-filled syringe 40 Subcutaneous 2009 Adalimumab + MTX injection pre-filled syringe 40 Subcutaneous 2009 Etanercept injection pre-filled syringe 25 Subcutaneous 2009 Etanercept + MTX injection pre-filled syringe 25 Placebo Placebo + MTX Oral Azathioprine Oral tablets Cyclosporine Oral capsules Gold IM injection amp Hydroxychloroquine Oral tablets Leflunomide Oral tablets Penicillamine Oral tablets tablets (non 2009 Sulfasalazine Oral 9.21 e/c) 500 Palliation Methotrexate Oral tablets Methylprednisolone IV infusion vial SOURCE: (119) * This refers to the cost per pre-filled syringe, since one Cimzia(R) pack contains two pre-filled syringes, with each syringe containing 200 mg The cost year of the model is Inflation factors for 1997 to 2008 are taken from the NHS Pay and Prices Index (121) (Table 61) with inflation for 2008 to 2009 taken as 3.0% pa. This is relevant only where unit costs are taken from a cost year earlier than Table 61 (of the submission): Pay and Prices Index SOURCE: (121) Probabilistic inputs Year Pay and Prices Index The probabilistic inputs for all variables are defined alongside deterministic variables. 25

26 Clinical response Table 17: Risk and lower and upper 95% confidence intervals for all treatments at 3 months Treatment ACR 20 ACR 50 ACR 70 Risk Low 95% CI High 95% CI Risk Low 95% CI High 95% CI Risk Low 95% CI High 95% CI Infliximab + MTX 58.6% 36.8% 77.4% 27.0% 10.1% 54.9% 19.6% 1.3% 82.0% Tocilizumab + MTX 52.3% 31.9% 72.0% 18.0% 8.7% 33.7% 5.8% 2.3% 13.8% Adalimumab 55.3% 35.3% 73.7% 25.7% 10.0% 51.7% 16.4% 3.6% 50.9% Adalimumab + MTX 70.8% 52.0% 84.4% NA NA NA NA NA NA Etanercept 46.2% 30.2% 63.0% 21.4% 9.6% 41.2% 5.1% 1.4% 16.6% Etanercept + MTX 66.4% 32.8% 88.9% 61.1% 30.6% 84.8% 23.7% 3.9% 70.7% Placebo 14.0% NA NA 3.2% NA NA 1.1% NA NA Certolizumab pegol 53.3% 36.5% 69.5% 45.1% 9.8% 86.1% 11.6% 0.7% 70.6% Certolizumab pegol + MTX 71.1% 63.2% 77.9% 35.9% 25.2% 48.2% 21.6% 8.5% 44.8% Table 18: Risk and lower and upper 95% confidence intervals for all treatments at 6 months Treatment ACR 20 ACR 50 ACR 70 Risk Low 95% CI High 95% CI Risk Low 95% CI High 95% CI Risk Low 95% CI High 95% CI Infliximab + MTX 48.2% 32.9% 63.8% 26.1% 16.1% 39.5% 11.3% 7.4% 16.7% Rituximab + MTX 58.4% 35.4% 78.3% 35.6% 15.2% 63.1% 17.6% 4.1% 51.4% Tocilizumab + MTX 55.7% 45.3% 65.6% 40.9% 29.1% 53.8% 35.2% 16.1% 60.7% Adalimumab 41.3% 30.5% 52.9% 16.2% 9.3% 26.8% 11.2% 3.6% 29.9% Adalimumab + MTX 61.0% 43.8% 75.8% 41.8% 29.6% 55.1% 19.7% 10.7% 33.4% Etanercept 64.2% 44.1% 80.3% 38.1% 17.0% 64.9% 16.8% 2.5% 61.5% Etanercept + MTX 68.5% 44.8% 85.4% 66.4% 20.1% 94.0% 30.7% 2.4% 88.8% Placebo 13.1% NA NA 5.7% NA NA 1.0% NA NA Certolizumab pegol 55.9% 37.5% 72.9% 31.4% 13.3% 57.7% 12.3% 0.8% 71.6% Certolizumab pegol + MTX 77.2% 69.1% 83.7% 49.2% 33.5% 65.2% 28.2% 15.3% 46.2% A definition of the PSA component associated with ACR risk categories are defined in subsection From subsection : Point estimate and CIs converted to log odds scale: log odds = ln(risk/(1-risk)) The standard error on log odds scale was calculated as (log odds upper CI log odds lower CI) / (2 x 1.96) where 1.96 is the 97.5th percentile of a standard normal distribution. 26

27 `The log odds of ACR 20 response was simulated from a normal distribution with mean equal to the point estimate on the log odds scale and standard error as above. `The log odds of ACR 50 response was simulated as the simulated log odds of ACR 20 plus a simulation from a normal distribution for the difference between ACR50 and ACR20. The mean of this normal distribution was the difference of the point estimates of ACR50 and ACR20 on a log odds scale. The standard error was the square root of the sum of the square of the standard errors of ACR20 and ACR50 on a log odds scale. `The log odds of ACR 70 response was simulated as the simulated log odds of ACR50 plus a simulation from a normal distribution for the difference between ACR70 and ACR50. The mean of this normal distribution was the difference of the point estimates of ACR70 and ACR50 on a log odds scale. The standard error was the square root of the sum of the square of the standard errors of ACR50 and ACR70 on a log odds scale. `Simulated values of ACR20, ACR50 and ACR70 response were converted back to the risk scale: risk = exp(log odds) / [1 + exp(log odds)]. Cost and utility Table 19: Utility and Cost probabilistic values Variable Deterministic Distribution Parameter 1 Parameter 2 Source Value All-Cause 1.33 Log-normal (Sigma)* (Mu)* Wolf et al Mortality (96) Age (years) 52.2 Normal Mean Standard Error CZP trials pooled data Baseline HAQ 1.62 Normal Mean Standard Error CZP trials pooled data Baseline EQ-5D 0.38 Normal Mean Standard Error CZP trials pooled data Number of 2.26 Normal Mean Standard Error CZP trials previous pooled data DMARD Disease 6.56 Normal Mean Standard Error CZP trials Duration pooled data Average Weight 81.4 Sampled from a random percentage of the population Cost by HAQ-DI < 0.6 $1, < 1.1 $3, < 1.6 $2, < 2.1 $3, < 2.6 $3,401 CZP trials pooled data Log-Normal Mean** Mean** Kobelt et al 2002 (117) Log-Normal Mean** Mean** Kobelt et al 2002 (117) Log-Normal Mean** Mean** Kobelt et al 2002 (117) Log-Normal Mean** Mean** Kobelt et al 2002 (117) Log-Normal Mean** Mean** Kobelt et al 2002 (117) 27

28 Log-Normal Mean** Mean** Kobelt et al >2.6 $2, (117) Note: * values are calculated using 95% CI to estimate Standard Deviation (values on Log-scale) ** Assumption that the coefficient of variation is equal to 1 and the standard deviation is equal to the mean. The baseline cohort values are used within the model to simulate the baseline EQ-5D utility of the population, based on the regressions reported in Table 13. b. Priority question: Please clarify the sequence of drugs used in the model and state how this compares with the NICE clinical guidelines. The sequence of drugs used in the model is described in the submission and consistent with NICE clinical guidelines. We refer here to section 4.5 and paragraph 3 of section of the submission. From section 4.5: `Whilst the place of a TNF-α inhibitor (such as CZP) in the treatment of RA is now established in the UK (prescribed as according to NICE guidelines), treatment after anti-tnf-α failure (because of either inefficacy or an adverse event) remains controversial. No head to head data exists to determine which biologic agent is preferable. The options include switching to another TNF-α inhibitor, rituximab, abatacept (not recommended by NICE on health economic grounds) and tocilizumab (which is not currently a treatment option in the UK) [18]. `NICE Guidelines CG79 and TA126 currently recommend rituximab in combination with MTX as an option in patients who have had inadequate response to or are intolerant to other DMARDS including a TNF-α inhibitor. At variance to this, the usual UK clinical practice is to switch a patient onto a second TNF-α inhibitor. From section 7.2.3: `In the base case the drug sequence of the follow-up drugs after failure of the main comparators is sulfasalazine, leflunomide, gold, hydroxychloroquine, azathioprene, cyclosporine and penicillamine. This reflects the drug sequence used by the most recent NICE Assessment Group model, it does however differ slightly in that hydroxychloroquine has been included earlier as advised by UCB clinical experts. The specifics of the drug regimens are summarised in Table 49. The sequence in this submission is summarised in Table

29 Table 20: Modelled drug sequence Treatment line This submission Manufacturer s submission (rituximab) Scenario 1: Return to DMARDs Scenario 2: Sequential use of anti-tnfs (NICE recommended) 1 CZP +/- MTX or comparator Rituximab + MTX Rituximab + MTX 2 Sulfasalazine Leflunomide Adalimumab 3 Leflunomide Gold Infliximab 4 Gold Cyclosporine Leflunomide 5 Hydroxychloroquine MTX / Palliative care Gold 6 Azathioprene Cyclosporine 7 Cyclosporine MTX / Palliative care 8 Penicillamine 9 Palliation c. Priority question: The duration of treatment was unclear in the submission please provide complete details. Please clarify the example calculation in section (p.142) for the probability of treatment discontinuation the hazard appears to be Please explain how this relates to the stated mean of 5 years. The duration of each treatment in the model is as indicated in Table 49 of the submission, within section One year, six month and three month probabilities of treatment discontinuation were calculated approximately from these duration assumptions and used in the transition probabilities. Comparisons between the probabilities assumed and exact six month probabilities calculated assuming an Exponential survival distribution are shown in Table 21 below. Table 21: Drug treatment durations and calculations of discontinuation probabilities [97, 128] Treatment Mean time on treatment (years) Six month discontinuation probability assumed Comparator treatments Adalimumab Etanercept Infliximab Rituximab Methotrexate Subsequent treatments Azathioprine Cyclosporine Hydroxychloroquine Leflunomide Penicillamine Gold (sodium 3.91 aurothiomalate) Sulfasalazine Exact six month discontinuation probability 29

30 A sensitivity analysis using the exact probabilities is shown in Table 22. The results of the analysis indicate minimal change in Incremental cost-effectiveness ratios. Table 22: Sensitivity analysis for exact probabilities, ICER Base case Sensitivity analysis Scenario A Scenario B Scenario A Scenario B CZP + MTX vs each combination therapy Adalimumab + MTX CZP dominates CZP dominates 34,779 Etanercept + MTX * ETA dominates 202,577 * ETA dominates Infliximab + MTX CZP CZP dominates CZP dominates dominates CZP dominates Rituximab + MTX ,047 52,889 CZP vs each monotherapy Adalimumab ,835 42,044 Etanercept * * 82,962 * 6,724 * * Comparator was more costly and more effective than CZP +/- MTX d. Priority question: Please provide details (and an example) of how response rates in table 50 were derived from the results of indirect comparisons in tables We will extend the description of this modelling step as provided on page 121 in section of the submission. The absolute effectiveness of MTX in terms of ACR response was estimated from aggregating response data from the MTX arms of combination therapy studies at each of three and six months. These studies used for this aggregation were the same as those presented in section 6.6. The absolute effectiveness of placebo was estimated in the same way from the placebo arms of monotherapy studies presented in section 6.6. The values that were used for monotherapy comparisons were the averages of the three and six month response data, whereas for combination comparisons, the timepoint stratification was maintained. The results of this analysis were summarised in Table 51 of the submission. The absolute effectiveness of each comparator treatment was derived from the absolute effects as described above (risks) and the relative indirect effects through the following steps: Absolute risks for MTX or placebo converted to odds: odds = risk/(1-risk) Odds of response for each treatment calculated by applying odds ratio derived in indirect comparisons (section 6.6) 30

31 Risk of response for each treatment converted from odds: risk = odds/(1+odds) For example, the risk of ACR20 for MTX at six months was (339/1398 from Table 51 of the submission). This converts to an odds of for MTX (= /( )). The odds ratio for CZP + MTX vs MTX (ACR20 response at 6 months) was from Table 26 of the submission, with a 95% CI of 6.99 to The odds for CZP + MTX are therefore x = The risk for CZP + MTX is therefore (=3.3836/( )) or 77.2%, as shown in The model has an input of %, which is the exact assumption adopted. Table 50 of the submission presents the risk as 77.3%. In responding to question B1a we note that the assumption is 77.2% to one decimal place of a percentage value. The bounds of the 95% CI for the risk were calculated by applying the bounds of the 95% CI of the odds ratio. o The lower bound for the 95% CI of the risk was 69.1%, calculated as x 6.99 / ( x 6.99). o The upper bound was 83.6%, calculated as x / ( x 15.98). The model has inputs of % and % respectively. Using the odds scale meant that transformation and back-transformation calculations became necessary. However, the scale used also ensured that risks and confidence intervals were within a 0-100% range and were consistent with indirect analyses of relative treatment effect presented in the clinical effectiveness section. e. Priority question: Please explain the relationship between HAQ-DI and mortality as in the model UK mortality rates were adjusted for HAQ-DI (p. 121). Please clarify if any RA adjusted mortality rates could have been used instead? In response to item 4 of the Clarification of requests for clarification document sent by NICE/ERG, we can confirm that the model does assume a linear relationship 31

32 between HAQ and increased mortality risk. The relationship between HAQ-DI and mortality was modelled as follows where x: age in years i: model state (ie line of treatment) : unisex one year mortality probability at age x (years) : HAQ score at age x (years) in model state i RR: relative risk of death per unit increase in HAQ-DI. This is taken as 1.33, Note that based on a 35 year cohort study of 3501 RA patients in Canada [96]. This variable is included in the PSA with the published 95% CI of to 1.610, assuming a lognormal distribution. is an average of the gender specific mortality functions, weighted by the proportion of cohort alive at age x of either gender: where `pm is the proportion of the cohort assumed to be male An alternative approach implied in this question would have been to derive a life table for RA patients contingent on age and, possibly, gender. However such an approach would have ignored, or dealt crudely with, the relationship between HAQ- DI and mortality, which is associated with line of treatment and age in the model. Therefore such an approach was rejected. Regarding the apparent inconsistency in the referencing of research cited on page 121 of the submission, there is in fact no inconsistency. To clarify: the research published at the end of the Brennan reference was used as the source of data, rather than the model prepared by Brennan (the Brennan reference was a University of Sheffield technical report that contained a model and a validation of a HAQ mapping that was later published by Bansback et al. [130]). 32

33 f. Standard practice when developing a Markov model is that all cycles are the same length. Under certain circumstances, it may be methodologically appropriate to vary cycle length if warranted by the nature of the condition or treatment under consideration. However this might typically take the form of shorter cycle lengths during treatment, followed by longer cycles during follow-up. The CZP model cycle lengths begin at six months in cycle one, then last for three months in the second and third cycles before reverting back to six (p. 122). Please clarify why has the cycle length been changed in this way? Please explain how were the discount rates and all transition probabilities adjusted for the differing cycle lengths. Please provide methodological justification for the approach with appropriate references. The spreadsheet model allows the user to select whether response should be assessed at 3 months or 6 months for the purposes of continuation of treatment with certolizumab pegol or comparator, as demonstrated in the presentation of sensitivity analyses (as shown in Tables 67 and 68 of the submission). The length of the timesteps in the model are altered as in Table 23 as a calculation mechanism for achieving this. Table 23: Length of timesteps in the model, dependent on response timepoint selected Timestep 3 months response 6 months response 1 3 months 6 months months 3 months months 3 months 4 and thereafter 6 months 6 months Transition probabilities and discount factors are adjusted accordingly. See for example the following spreadsheet ranges: CC15:CF18 of Markov_C Column CB of Markov_C Column EN and EO of Markov_C CB15:CD18 of Markov_O Column CA of Markov_O 33

34 Column EL and EM of Markov_O g. Please explain why were rates of discontinuation not extrapolated beyond the trial period (p. 123). It seems unlikely that no further patients will discontinue treatment beyond the trial time horizon. Please provide an analysis of the results if treatment discontinuation rates are extrapolated from the trial data. There are two sources of discontinuation in the model: Discontinuation due to lack of efficacy: ACR20 response not achieved at the response timepoint, which in the base case is 6 months Discontinuation for other reasons after the response timepoint Since discontinuation due to lack of efficacy only applies at one timepoint, there is no extrapolation. The six month discontinuation probabilities used in the model throughout its time horizon are the assumed values given in Table 21 in response to question B1c. Discontinuation rates observed in trials were not used. Consequently, there was no extrapolation of trial-based discontinuation rates. Discontinuation for other reasons after the response timepoint was estimated using observational rather than trial data, as described in response to B1c. h. On p. 111 CZP monotherapy was described as CZP monotherapy (400 mg at weeks 0, 2, 4 then 200 mg every 2 weeks thereafter) whereas table 47 stated 400 mg is received every 4 weeks. Please clarify this apparent contradiction and state the dose regime(s) in the relevant trial (FAST4WARD) and employed in the economic analysis. The actual dose regimen used in the FAST4WARD study and the economic analysis is CZP 400 mg s.c. every 4 weeks, as per Table 47 of the submission. i. Please supply further detail regarding the statement (p 116) on drug sequence as advised by UCB clinical experts. Drug sequencing is discussed in response to B1b. j. It was stated that Patients enter the model after an inadequate response to MTX (p. 17/ p. 118). Please clarify what was meant by inadequate response. 34

35 The model uses data as reported by trial publications identified by the systematic review. The systematic review inclusion criteria required that patients in each trial had inadequate response to prior DMARD therapy, including MTX (as stated in appendix of submission). Inadequate response to MTX was defined according to each individual trial s methodology and in many cases the precise definition was not reported. B2 Utility mapping methodology a. Priority question: Please explain more fully how utility values were obtained and provide any detail of the mapping algorithm and the data used. Was there an opportunity to use data from other (non- CZP) anti- TNF studies? Weaknesses of models previously submitted to NICE Previous models submitted to NICE have modelled utility based on HAQ scores converted via a mapping exercise to EQ-5D, NICE s preferred measure of preferencebased utility [127]. NICE has highlighted this as a weakness of model design because: `HAQ fails to capture the psychological and pain elements of quality of life associated with RA. In addition, the Committee noted that the HAQ scoring system may be an insensitive measure of small changes in health-related quality of life and may have a non-linear relationship to utility scores. The Committee noted that HAQ had been used as a basis for calculating utility across all economics models, and while noting its limitations, accepted that it was the best means of estimating utility for the purposes of economic analysis given the available data [paragraph of 30] The unavailability of data includes the previous absence of EQ-5D data. NICE made recommendations for further research, which included: `Evidence should be collected on the impact of TNF-alpha inhibitors on disease progression, joint replacement, mortality and quality of life, as assessed using a generic preference-based utility instrument (for example, the EQ-5D) [paragraph 6.3 of 30]. UCB s approach: to use directly measured EQ-5D data as far as possible rather than HAQ 35

36 UCB have heeded NICE s advice, collected EQ-5D in its trials and used this data directly in the cost-utility model presented. Utility is primarily modelled as directly measured by EQ-5D, with extrapolation beyond the time at which employed trial data was measured. How was utility modelled? The valuation of health effects is considered in section of the submission. Nevertheless, there appear to be some possible misconceptions about the way in which utility was modelled (point 6 of NICE/ERG s response to UCB s questions about this clarification document). We therefore extend the description here. A schematic of the utility model is provided in Figure 1 below. Figure 1: Utility modelling schematic Treatment effect dependent on clinical response (double arrow reflects approximate range of ACR responses) 0.6 Utility % of benefit achieved at 4 weeks Treatment benefit whilst on first-line treatment Decline in utility with subsequent treatment 0 Start 4 weeks 3/6 months Move from 1st-line to subsequent tx Timepoint ENTRY INTO THE MODEL Patients enter the model with a baseline utility of This value is the average EQ- 5D utility of patients in the 027 (RAPID 1) and 050 (RAPID 2) studies. No mapping algorithm was necessary for this component of the model. TREATMENT BENEFIT EFFECT The model assumes an initial benefit from treatment. This benefit is dependent on ACR response and other factors and is quantified via a regression analysis on EQ-5D (Table 13). The benefit is quantified in terms of EQ-5D from analyses of patients 36

37 directly measured EQ-5D values. No mapping algorithm was necessary for this component of the model. For a given level of ACR response, the same treatment benefit is assumed for certolizumab pegol as it is for the comparator treatment. There is no assumption that the benefit to patients given certolizumab pegol is any better (or worse) than those given comparator interventions, other than what is implied from ACR response rates which may differ between treatments. It is assumed that 80% of the benefit at 6 months occurs by week 4, based on UCB trial data. To reiterate, HAQ is not used in this component of the model. TREATMENT BENEFIT WHILST ON FIRST LINE TREATMENT The originally submitted model assumed that utility decreased by per year (or per year if response had been assessed at 3 months). Further scrutiny of this assumption and statistical analysis of trial data has taken place since the submission was made and the revised model presented in section C adopts an assumption of an improvement of per year (or per year if response had been assessed at 3 months). UCB analysis ECO46e1 found that the mean (standard error) change in HAQ-DI in the year from week 24 to week 76 in the 027 study in completers with at least an ACR20 response on certolizumab pegol at week 24 was (0.0259), as measured in 132 patients [110]. Using the Bansback conversion factor, this equated to a utility change of An equivalent procedure was used to identify the assumption following 3 month response of , based on the ECO46e2 analysis [111]. Whilst the value used in the model ( or ) was derived from HAQ scores and a HAQ to EQ-5D conversion function, it is utility rather than HAQ which is being assessed at each timepoint in the model. DECLINE IN UTILITY WITH SUBSEQUENT TREATMENT The model assumes that patients will ultimately discontinue treatment with certolizumab pegol or comparator and move to follow-on treatments. It was assumed in the originally submitted model that this would lead to a decrease in utility of per year (or per year if response had been assessed at 3 months). To be externally consistent with NICE s recommendations following TA126 and to be 37

38 internally consistent with the new assumptions relating to treatment benefit whilst on first line treatment, this was revised to a HAQ increase of per year, which equated to a utility decrease of per year (revised results presented in section C). This assumption applies to both arms of the model, to palliative care and earlier treatment lines alike. CAPS AND COLLARS The model assumes that utility in this population can neither be greater than 0.72 (cap) nor less than 0.09 (collar). The cap and collar are calculated from the following: the mean baseline EQ-5D value directly measured in patients was 0.38 the mean baseline HAQ value directly measured in patients was 1.62 the Bansback conversion factor (change in utility vs change in HAQ) is (see response to B2b) HAQ values fall between 0 and 3. Where is HAQ used in the model? HAQ is used in very few places in the model. Modelling of utility is predominantly conducted on the basis of EQ-5D values directly measured in patients. The exceptions to this are: assumptions relating to the change in quality of life on continuation of treatment (described above) probability of mortality (see B1e) costing of RA management (see Table 15 of this clarification and [117]) It is only necessary to calculate HAQ in the model in order to then calculate probabilities of mortality and the costing of RA management. Possible misconceptions by the NICE/ERG NICE/ERG made the following statement in answering a question about the requirements for the clarification response. `Our understanding is that the model (MS page 125) anchors the relationship between HAQ and utility by adopting the value 1.62 for HAQ to be equivalent to utility 0.38; other utilities are then related to HAQ by using the Brennan (ref 98) 38

39 value of 0.1 HAQ change equivalent to utility change, so that max utility is 0.72 (0.38 +[0.34 * ] ) and min utility is 0.09 (0.38 [ 1.38*0.2102] ). `A] we would like to get a feel for the data that underpin the calculated anchor value; as it stands the MS merely presents the numbers and calls them means from trials. `B] Since the trials provided HAQ scores and EQ-5D scores for (presumably) a large number of patients it should be possible to represent how the modeled utilities match those calculated using the model procedure starting with HAQ score. For example, say a patient in a trial has a HAQ score of 1.3, the model calculates a utility of ( [0.2102*0.32]) = 0.447; how does this compare with the observed value for this patient? We would like to see the relationship presented graphically rather than go hunting for it in trial reports. We believe this statement suggests that NICE/ERG may have misunderstood the mechanics of the model. In sentence one, the utility cap and collar value stated is correct. But it is potentially misleading to view the HAQ value of 1.62 as being `adopted as equivalent to a utility of 0.38 they were simply the baseline mean values. Similarly it may be misleading to view the HAQ values as being `anchored to utility. Utility is modelled from direct patient measurements in the majority of the model, but this has not been possible for some important characteristics: mortality and RA management costing. For these aspects of the model and for setting the assumption relating to change in utility with continuation of treatment, a simple conversion was necessary. Sentence two has the premise that HAQ scores are primarily measured in the trial and followed in the model. This is not the case. The baseline utility value in the model was directly measured in patients via EQ-5D. Treatment benefit was assessed in terms of EQ-5D change vs treatment, ACR response and other factors. We trust that the graphic provided above (Figure 1) and explanation provided here correct any possible misunderstanding. Model setting: Improvement in QoL measure The model provides the functionality to assess quality of life primarily in terms of HAQ or primarily in terms of EQ-5D. If the HAQ setting is chosen, the baseline value, treatment benefit and other factors in the model are all assessed in terms of HAQ 39

40 and then converted to utility [130]. This setting provides a model of RA that is consistent with but suffers the same criticisms levelled at previous models submitted to NICE: that HAQ is an inadequate measure of quality of life and that mappings from such instruments to utility are an inadequate substitute for the direct measurement of EQ-5D utility. The results presented assume the primary quality of life measure is EQ-5D. The HAQ setting is superfluous to the submission and its results invalid, given the availability of output that employs directly measured EQ-5D utility. b. Priority question: If EQ-5D measured in the studies was not used directly in the model; please compare the EQ-5D data from the studies with estimates obtained using a mapping algorithm. EQ-5D values measured in studies were directly used in the model. To obtain utility values from HAQ-DI in the few areas that this was required, simple linear regressions were used (ΔEQ5D ΔHAQ). These regressions were based on results from a study by Bansback and colleagues, published as an appendix to a ScHARR technical report [98] and later published in full [130]. The regression coefficients did not appear in the full publication; the model reported in depth [98] appears to match the model reported in less depth [130]. The regressions have been used by Brennan and colleagues in a study to evaluate the cost effectiveness of TNF inhibitors over conventional DMARD therapy [98]. Bansback et al. examined patients with RA participating in 2 studies in the UK (n = 151) and Canada (n = 319) who completed the HAQ, EQ-5D, and SF-36 [130]. Models were developed of the relationship between the HAQ and SF-6D and EQ-5D using regression analyses. Although this method of calculation of preference-based utility may not reflect all aspects of the HAQ-DI it provides an alternative approach to allow use of health-state utility values where data from clinical studies is insufficient or was not collected. B3 Model assumptions a. Priority question: Please explain and provide details regarding the assumptions and derivation of the long term HAQ progression whilst on treatment. Please clarify if the HAQ progression rate of whilst on treatment (p. 129) is just for the trial period or for the lifetime horizon. 40

41 Please see responses to items B2a and the analysis results presented in section C, in which a revised assumption is employed. b. Priority question: Please clarify if CZP will be available for home injection for all users (p. 131) as it was earlier stated that home injection is not currently possible (p. 5). Please clarify if modelling assumed home injection is achieved by all patients. Please clarify the cost implications if not all patients achieve self injection. CZP will be available as a subcutaneous injection that after appropriate training can be administered by the patient in the home setting without the aid of medical support. This is identical to current standard therapies such as etanercept and adalimumab. An autoinjector device is in preparation for patient who would prefer this route. In summary all patients will be able to self inject. If a patient is unable to use CZP they will similarly be unable to use etanercept or adalimumab and should be considered for a secondary care infusion treatment such as infliximab. A device has been supplied to NICE as part of this submission, illustrated in Figure 2. Figure 2: Illustrations of the syringe device for CZP 41