Efficacy Data in regulatory settings, DSBS January, May 2013

1. Efficacy Data in regulatory settings, DSBS January, May 2013 H. Lundbeck A/S

2. Outline Part 1: Objectives and Endpoints in test strategies Part 2: • Integrated Data Analysis: Purpose, Requirements, Terminology • Methodology for Pooled and Meta Analysis • Applications to filing of Vortioxetine

3. Part 1: Endpoints in RCTs • Secondary Endpoints are Increasingly important • for differentiation of products • highly competitive markets • demands from authorities • Publishing on clinicaltrials.gov

4. Definitions of Endpoints in RCTs: ’Good old Days’: Primary, Secondary and Exploratory Now: Primary: More or less as before Secondary: Key Secondaries Other Secondaries Exploratory: perhaps bigger than before

5. Regulatory view Primary Endpoint: Multiplicity control in case of e.g. several doses Key Secondary Endpoints should be under proper multiplicity control together with the primary and can potentially be included in labelling text and promotional material. Will normally require significant primary Other Secondary Endpoints can (normally) not be included in labelling text but have to go on ’www.clinicaltrials.gov’ Exploratory endpoints can (normally) not be included in labelling text but does not have to go on ’www.clinicaltrials.gov’ - Unclear whether secondary analyses have to go on .gov

6. Authority Requirements to protocols and SAPs (EMA+FDA) • Clinical formulation of objectives • Clear correspondence between objectives and endpoints • Testing Strategy • Primary and Key secondaries should be selected based on ‘Objectives’ • Only one endpoint per objective. No redundancy • Only one analysis method (population) per endpoint

7. Objectives and Endpoints Objective Endpoint Analysis Methodology Similar for other objectives. Select one row within each objective Often a mix is seen in protocols

8. Primary Analysis Objective Endpoint Analysis Methodology : Secondary analysis method of primary endpoint adressing primary objective Can not be used as key secondary

9. Key Secondary Analysis I Objective Endpoint Analysis Methodology

10. Key Secondary Analysis II Objective Endpoint Analysis Methodology

11. Example: Depression Study Primary Objective: Evaluate the efficacy of LuAA21004 compared to Placebo on depressive symptoms (in patients with MDD). Key Secondary Objectives: Evaluate the efficacy of LuAA21004 compared to Placebo on • Global Status • Functioning • Anxiety Assessments/endpoints MADRS, HAM-D (Response,Remission) adressing objective CGI-S, CGI-I (Response, Remission) SDS, work/family/social/total HAM-A, HAM-D Subscale

12. Hierarchical Testing MADRS Depression Global Status CGI-I Functionality work/social/family SDS HAM-A Anxiety One endpoint per objective Two doses: alfa=2.5% in each sequence

13. Primary Objective Objective Endpoint Analysis Methodology OC LOCF MADRS MMRM HAM-D Non-par Depression Response Remission Response/Remission considered redundant, not a separate objective. However, special interest in EU

14. Response and Remission Response and Remission: • attractive for profiling • attractive for pricing • difficult to formulate as separate objective EMA: Particular Clinical Relevance + Redundant FDA: Arbitrary and Inadequate Definition + Redundant

15. EMA: Responders MADRS MADRS 50% Response CGI-I MADRS Remission SDS ”Branching”, overall α>5% Confirming Clinical Relevance HAM-A • proceed as long as p<0.05

16. Number of Key Secondary Endpoints • no formal requirement or limitations • limited through non-redundancy within and between objectives • ’Rule of thumb’: 4-5 tests within each dose • chose hierarchi according importance and ’hit-likelihood’ • status of non-tested endpoints can be unclear

17. Testing Strategy • How to report p-values outside testing strategy or after stop within sequence ? • Phrasing ’Statistical Significance’ should be reserved for results from testing strategy Tip: Phrasing ’seperated from placebo’has been introduced in accepted Lundbeck publications and in filing documents. Other possible phrasings: Nominal significance Nominal p<0.05 Nominal evidence Potential significance

18. Part II Integrated Data Analyses in Regulatory Setting

19. Integrated Data Analyses When a clinical development program enters registration phase a need for integrated analyses arises: • Regulatory requirements • Questions during approval phase • Profiling after approval

20. Terminology Integrated Data Analysis Pooled Analysis Meta Analysis

21. Terminology Definitions of Meta-Analysis: FDA: Meta-analysis refers to the analysis of analyses...the statistical analysis of a large collection of analysis results from individual studies for the purpose of integrating the findings. (Glass, 1976) Examples of related terms used in literature include: analysis of combined data, combined analysis, analysis of pooled data, and pooled analysis. No matter what term is used, the objective is to use appropriately sound methods when formulating an integrated analysis. ICH E9 + EMA: The formal evaluation of the quantitative evidence from two or more trials bearing on the same question. This most commonly involves the statistical combination of summary statistics from the various trials, but the term is sometimes also used to refer to the combination of the raw data.

22. Meta-analysis Definition (google) • Statistical solutions Software:Meta-analysis is a statistical technique in which the results of two or more studies are mathematically combined in order to improve the reliability of the results. Studies chosen for inclusion in a meta-analysis must be sufficiently similar in a number of characteristics in order to accurately combine their results. When the treatment effect (or effect size) is consistent from one study to the next, meta-analysis can be used to identify this common effect.  When the effect varies from one study to the next, meta-analysis may be used to identify the reason for the variation • Wikipaedia: In statisitics, a meta-analysis refers to methods focused on contrasting and combining results from different studies, in the hope of identifying patterns among study results, sources of disagreement among those results, or other interesting relationships that may come to light in the context of multiple studies

23. Terminology Integrated Data Analysis Meta Analysis Pooled Analysis Meta Analysis

24. Terminology Integrated Data Analysis Pooled Analysis Pooled Analysis Meta Analysis

25. Terminology in this presentation Integrated Data Analysis Pooled Analysis Meta Analysis

26. Terminology Meta Analysis (AD): A specific statistical methodology based on summary statistics or aggregate data from each trial (AD Meta Analysis) Pooled Analysis (IPD): Statistical analysis based on data pooling at individual patient data level, that is, combination of raw data. (IPD Meta Analysis)

27. Pre-requisities for Integrated analyses Similarity of Studies with respect to • Clinical endpoints • Study designs • Populations

28. FDA: ISE requirements • Integrated summary demonstating substantial eveidence of effectivenes • Evidence to support recommended dosing in labelling • Analyses in subgroups: Sex, age, race • Dosing in specific subgroups - So, actually no specific demand for integrated analysis, could just be side by side presentation

29. EMA: Points to consider on Meta-Analysis • Not a requirement • Cannot normally serve a primary • Cannot save individual negative studies • Needs prespecification

30. EMA: Pre-requisits for acceptance of results from Meta-analysis as pivotal evidence Pre-specification • Statistical Methodology • Arguments for Inclusion and exclusion of studies • Plan for evaluation of robustness of results: subgroup, subsets of studies etc.. • Populations

31. EMA: Accepted Purposes of Meta-Analysesfor supportive evidence • Preciseestimate of treatmenteffects • Confirmeffect in subgroups • Secondaryoutcomesrequiring more power • Evaluatedose-response • Evaluateconflictingstudyresults - similar to FDA ISE

32. Pooled Analysis

33. Properties of Pooled Analysis Intuitively attractive using individual patient data Flexibility in having original data (subgroups, outliers etc.) Complex statistical modelling possible/necessary Assumptions on variability, baseline dependence, sites etc. Heterogeneity not straightforward Risk of getting meaningless comparisons Design and convergence issues when using MMRM Not really recommended by FDA?: Correspondence in Relation to AA21004: ’pooling on patient level is in general not recommended’

34. AA21004 Data Package for MDD • 8 Studies for Major Depressive Disorder • Differences between Studies: • Duration, 6-week 8-week • Doses: 1, 2.5, 5, 10, 15 ,20 • Primary endpoint: MADRS, HAMD-24 • Method for primary (ANCOVA LOCF, MMRM, nominal/window) • Test Strategy (step-down/alfa-split) • Differences in key secondaries: SDS, Response, CGI • Region • Results

35. AA21004/Vortioxetine Studies for MDD

36. Methodology for Pooled Analysis • Example: Standard ANCOVA model • MADRS, LOCF, Week 8 • Alternatives (SAS): • model MADRS_DL = MADRS_BL ARMCD; (Naive) • model MADRS_DL = MADRS_BL ARMCD STUDY; • model MADRS_DL = MADRS_BL ARMCD SITEID(STUDY); • model MADRS_DL = MADRS_BL*STUDY ARMCD SITEID(STUDY); • Further Modelling: Random STUDY*ARMCD; Random Treatment*Study Effect • Repeated group=study; Heterogeneous Variability

37. Example: Treatment versus Placebo TTreatment Arm only in one Study - substantial difference in estimate

38. Interpretation of Pooled Methodology Misleading Estimates MMRM design and convergence problems Modelling does not seem to account for all study differences A lot of effort can be done to make the pooled analysis do what the meta-analysis seems to do automatically Seems not to be the best choice for AA21, but was used for small subgroups

39. Meta Analysis

40. Properties of Meta Analysis Analysis of analyses Original data not needed (survey setting not so relevant for AA21) Only relevant comparisons are retained Works on any treatment estimate (+/-SE) logistic regression, Cox, ANCOVA, SES Well-established method for heterogeneity Less powerfull ? Pairwise Comparisons mainly

41. Methodology for Meta Analysis Trials: i=1,…,k Fixed Effects Modelling: ai = true treatment effect in trial i âi= estimated treatment effect in trial i vi = variance of âi wi = 1/vi, weights Estimated effect: â = Σwiâi/Σwi Variance of estimate: 1/Σwi Test H0: ai=0:(Σwiâi)2/Σwi ~ϰ2(1) ref: Encyclopaedia of Biostats. page 2570-2578 Der Simonian (1976)

42. Methodology for Meta Analysis with heterogeneity: Random Effects Test for heterogeneity of effects Q = Σwi(âi – â)2~ϰ2(k-1) I2=max(0,100*(Q-k)/Q) I2 describes the percentage oftotal variation across studies that is due to heterogeneityrather than chance (ref: Higgins, 2007) >50% considered problematic Random effects in case of Heterogeneity: ai ~ N(a*,σ2), σ2 estimated using Q(ref: Der Simonian) wi*= 1/(vi+σ2) â*= Σwi*âi/Σwi* Variance of estimate: 1/Σwi* Test: (Σwi*âi)2/Σwi* ~ ϰ2(1)

43. Meta Analyses in SAS PROC MEANS;

44. Plan for Meta Analyses on AA21004 for regulatory purposes • ‘Prespecification’ in separate SAP • To be shown in 2.7.3 • Applied for sub-groups: gender, baseline severity • Pooled analyses for small subgroups • - not all studies finalised at planning stage

45. Preliminary Meta Analysis without two non-finalised studies. Differences to Placebo Removed for confidentiality reasons Dose Response ? 10 better than 5 ? Fixed or Random ?

46. Meta Analysis with all studies Removed for confidentiality reasons Dose Response ? 15 mg ?

47. Meta Analysis SummaryDifferences to Placebo Removed for confidentiality reasons

48. Pooled versus Meta Analysis of AA21004 • Severe heterogeneity complicates interpretations • Confounding with Region US/Non-US • For both analysis types it is mandatory that interpretations involving comparions across treament arms take the individual study results into account. • The random effects model has less power in the presence of heterogeneity but estimated treament differences change only slightly. Does not solve all heterogeneity problems. • Random effects not feasible in pooled MMRM, but gets close to Meta results for LOCF • Neither method completely satisfactory • Mixed treatment comparison (MTC) meta-analysis allows several treatments (doses) to be compared in a single analysis while utilising direct and indirect evidence

49. Meta Analysis in the filing documents • Need to downplay due to severeheterogeneity • Demonstrate Region issue US/Non-US • Resultsacrosssubgroups: age, bmi, gender, severity • Argumentation for dose

50. Planned talk at 8 January 2013 …… 4 Months Later ……..