Bayesian additive decision trees of biomarker by treatment interactions for predictive biomarkers detection and subgroup identification

Transcription

1 Bayesian additive decision trees of biomarker by treatment interactions for predictive biomarkers detection and subgroup identification Wei Zheng Sanofi-Aventis US Comprehend Info and Tech

2 Talk outlines Introduction and background Methods and models Numerical simulations and a real example Conclusions and discussions

3 Biomarker and subgroup identification in clinical development Tailored therapies Precision Personalized medicine medicine Pharmacogenomics Evidence-based medicine Biomarker identification Subgroup identification Other

4 Biomarker and subgroup identification: Scenario 1 Translational Research Subgroup identification Clinical studies Well-understood biomarkers

5 Biomarker and subgroup identification: Scenario 2 P value: Not Again! Clinical studies Treatment Control findings

6 Biomarker and subgroup identification: Scenario 3 Subgroup analyses Health authorities Is the treatment effect homogeneous? Biomarker identification Subgroup identification New clinical studies

7 Example: Zaltrap (Aflibercept)

8 Example: Zaltrap (Aflibercept) Nov 15, 2012 CHMP assessment report Feb 05, 2013 Approval in EU Feb 23, 2017 Label updated per biomarker program findings Thus, the EMA has requested to the applicant company to analyse plasma and tissue samples from the available trials, with the primary aim to identify biomarkers to allow better selection of the population likely to experience a beneficial effect following treatment with aflibercept. CHMP in combination with irinotecan/5 fluorouracil/folinic acid (FOLFIRI) chemotherapy in adults with metastatic colorectal cancer (mcrc) that is resistant to or has progressed after an oxaliplatin containing regimen Exploratory biomarker analyses were undertaken in the VELOUR trial including analyses of RAS mutational status in 482 of 1226 patients (n= 240 aflibercept; 242 placebo) These data are exploratory and the statistical interaction test was nonsignificant (lack of evidence for heterogeneity in treatment effect between the RAS wild type and RAS mutant subgroups). Section 5.1

9 Symbols and assumptions Randomized, placebo-controlled studies

10 Modeling predictive effect A patient with biomarkers of and receiving treatment : prognostic effect : predictive effect

11 Modeling treatment effect How to estimate? Parametric approach, Non-parametric approach Decision tree based approach

12 Decision tree based subgroup identification ~ 0.2 Tree construction prognostic effect not controlled Tree construction one directional

13 Prognostic effect Prognostic effect not controlled Potential variance inflation depending on the shape of

14 One directional construction Implementation 0.2 Not optimal Computation

15 Objectives a tree based algorithm Controls prognostic effects Has mechanisms of revising the tree structure to achieve better fitting

16 Additive tree models : prediction function of tree A Prognostic effect controlled

17 Additive tree models Integer programming Time consuming 0.2 Optimal Tree Alternatives? Bayesian Trees!

18 Decision tree vector representation : 2 3 : : 6 7 :

19 Bayesian specifications,,,, ~,,,,,

20 Prior specifications (BART (Chipman 2010)), : ~ 0, 1/16 ~ 3

21 MCMC sampling (Chipman 1998, 2010) Gibbs sampling each tree is updated once a time Tree sampling Split a terminal node Prune two terminal nodes Revision mechanism Change a split rule Switch the split rules of two nodes Treatment effect sampled using conjugate priors

22 Biomarker predictive score Biomarker predictive score : posterior probability that the biomarker is a split variable in the interaction tree

23 Subgroup identification max min 1,, ;,,,

24 Simulations ~ 0, 1 20, , 600 1: 1 Predictive scores: UL: univariate linear interaction model rvti: revised virtual twin method Subgroup identification: rvts: revised virtual twin method

25 Simulations

26 Predictive scores

27 Subgroup identification probability of identifying a subgroup

28 Subgroup identification sensitivity and specificity

29 A real example Oncology study 1:2 randomization ratio ~ 300 patients 20 biomarkers, normalized Endpoint: % change from baseline in tumor size

30 Predictive scores (PM UL rvti)

31 Subgroup identification (rvts PM)

32 Subgroup identification

33 Conclusions and discussions Bayesian interaction trees Best used in randomized studies Need reasonable sample size or large effect size

34 Conclusions and discussions Potential selection bias A bit slow in computation Highly correlated data could be hard

35 Thank you!