Practical Issues in Design of Biomarker-driven Adaptive Designs

Transcription

1 7 th Annual Bayesian Biostatistics and Bioinformatics Conference Houston, Texas: February 13, 2014 Session IV: Biomarker-driven Adaptive Designs Practical Issues in Design of Biomarker-driven Adaptive Designs Brenda Gaydos, Ph.D. Research Fellow

2 Overview Practical issues and approaches illustrated with case studies Classes of approaches taken 5 cases studies spanning: Neuroscience Oncology Muscular Skeletal 2

3 For this Presentation Tailored Therapeutics (definition) Designs with multiple subpopulations Designs limited to a subpopulation (enriched) Designs where population may or may not be identified by a predictive marker Cased Studies Completed None have been positive 3

4 Study Types Subgroup analysis post design Develop hypothesis & strength of evidence Adapt the hypothesis if trials in parallel a-priori subgroup hypothesis Enrichment design All comers: Multiple Comparison (MC) procedures Stratified: Drop a population Identified marker but no identified threshold Adapt hypothesis 4

5 Subgroup Analysis Post Design Majority of cases: Hypothesis Generating Case Study in Neuroscience Alternative approach if trials in parallel Lock the first study assess subpopulations or secondary hypothesis Adapt the analysis in subsequent studies Primary analysis changed to subpopulation not statistically significant with/without change 5

6 a-priori subgroup hypothesis: Enrichment Designs Restricted to the subpopulation of interest May never know if there is benefit in broader population Restrict population to only those a-priori believed to benefit, or Including biomarker negative patients to increase confidence that the marker is valid Practical Issue: Availability of a validated assay 6

7 Case Study: Enrichment Design Neuroscience Phase 2 secondary subgroup analysis based on disease severity Strength of evidence difficult to assess Population narrowed in phase 3 Phase 3 studies were not statistically significance 7

8 Implementation Advantages of the All-comers Design Conduct a prospective analysis utilizing archived samples if assay not developed Predictive marker may be a-priori selected but the marker positive threshold not known Enrollment is simplified (Ideally requires larger sample size) Information available on broader population for subgroup identification analysis Can utilize adaptive analyses or multiple comparison procedures to arrive at confirmatory evidence 8

9 a-priori subgroup hypothesis: Multiple Comparison Approach Millen, Dmitrienko, Ruberg, Shen (2012). A statistical Framework for Decision Making in Confirmatory Multipopulation Tailoring Clinical Trials. DIJ 46(6) A-priori identified subpopulations All comers Size the trial to have high likelihood of power for MC assuming the expected average proportion of the subgroup in the general population Recommend the use of estimation approaches to assess Influence of subpopulation if ONLY the overall population is significant Qualitative Interaction if both the overall and the subgroup is significant 9

10 Bayesian Estimation Approaches in MC Approach IF Only the overall population is significant: Assess the Influence of the marker positive Posterior Pr (Effect (M-) > e) where e could be any clinically meaningful effect IF Both the overall population & marker positive population is significant Assess the quantitative interaction for added label information of greater improvement in subgroup Posterior Pr (Effect (M+)/Effect(M-) > r) where r is lower bound on ratio of improvement that would be clinically important 10

11 Case Study: All Comers MC Approach Neuroscience Small positive PoC on overall population Two additional phase 2 studies Active controlled study failed Large study utilizing all comers MC negative Meta-analytic evidence strong for marker positive subgroup Phase 3 stopped at interim for futility 11

12 Case Study: Drop subgroups Oncology 6 tumor types in 1 protocol Site specific to tumor type No pooling information across tumor types No control arm 40% savings in operational costs due to shared protocol Endpoint: overall response rate Design Min 15 to max 50 per group Futility analysis within group at n=15 and every 10 patients 12

13 Case Study (continued) Futility rule Binomial, with beta prior Informative prior used indicative of greater likelihood of a low response rate Stop a tumor type for futility if Posterior Pr (Response Rate < 10% ) > 0.95 Subsequent interims may not be done if response rate such that futility cannot be achieved Threshold was determined via simulations across a grid of potential true responses Study stopped for futility 13

14 Marker Positive Threshold Unknown Jiang, Freidlin, Simon (2007). Biomarker-Adaptive Threshold Design: a Procedure for Evaluating Treatment with Possible Biomarker-Defined subset Effect. Journal National Cancer Institute 99: , 2007 General Approach Pre-determine portion of the data that will be used to identify the subgroup (usually first 50%) Pre-determine approach that will be used to control the Type 1 error rate for: Effect in overall population Effect in identified subgroup 14

15 Case Study in Muscular Skeletal Literature indicates a biomarker measured at baseline MAY help identify which patients are more likely to respond to new therapy Lower biomarker value implies increased likelihood of response Patients with a level < X are considered low relative to normal Unknown if biomarker will be predictive If predictive, threshold unknown 15

16 Phase 2 study Case Study Continued Primary measure, continuous at 24 weeks Biomarker continuous, available at baseline Study stratified by baseline severity, and biomarker level X Enrollment enriched to insure approximately 50% of subjects have level < X ~ 80 per arm randomized Study objectives Assess effect of therapy in overall population If not significant, in subpopulation & estimate threshold value for biomarker 16

17 Approach Type 1 error rate controlled at 1-sided 0.10 Overall population tested first at alpha 0.04 Subgroup tested at alpha 0.06 if overall population not significant Power for overall procedure approximately 80% Test of the overall population: ANCOVA 17

18 Test of an effect in subpopulation Utilize a cut-point model to calculate the log partial likelihood ratio statistic Patient with biomarker value above the cutpoint does not contribute to the log likelihood Let S((b)) be the log partial likelihood ratio statistic for cutpoint b Repeat for all values of b Let S*=max{S(b)} Compute null distribution of S* by permuting the treatment labels If S* significant, declare treatment is significant for a subpopulation Permutation p-value is given by 1 + # permutations where ts < 1 + # permutations S * 18

19 Estimation of threshold value Point estimate b^ Value of b that maximized the partial likelihood CI for b Estimate the distribution function of b^ via bootstrap Create a bootstrap sample from the observed data and estimate b, say b^^ The empirical distribution function of b^^ can be used to estimate the distribution function of b^ C.I. of b can be calculated using the percentiles of the empirical distribution function Study stopped at interim 19

20 Summary Remarks Increasing use of Tailoring designs but. Cultural Barriers Belief that studies without interim analyses are more acceptable (even in phase 2) More upfront planning is needed More upfront strategy decision making is needed ( we will know it when we see it ) Operational Barriers Timing of assay development Assay turn around time for stratifying, enriching, adapting Timely validation of predictive markers Analytical Challenges Multiplicity Challenges Models linking predictive markers to clinical outcome Clinical interpretations (e.g. marker positive thresholds) 20