Session 8: Enrichment Design for Clinical Trials
Organizer: Jane Qian (Abbvie)
Chair: Jane Qian (Abbvie)
Yijie Zhou (Abbvie) “Enrichment Design with Patient Population Augmentation”
Clinical trials can be enriched on subpopulations that may be more responsive to treatments to improve the chance of trial success. In 2012 FDA issued a draft guidance to facilitate enrichment design, where it pointed out the uncertainty on the subpopulation classification and on the treatment effect outside of the identified subpopulation. We consider a novel design strategy where the identified subpopulation (biomarker-positive) is augmented by some biomarker-negative patients. Specifically, after sufficiently powering biomarker-positive subpopulation we propose to enroll biomarker-negative patients, enough to assess the overall treatment benefit. We derive a weighted statistic for this assessment, correcting for the disproportionality of biomarker-positive and biomarker-negative subpopulations under enriched trial setting. Screening information is utilized for weight determination. This statistic is an unbiased estimate of the overall treatment effect as that in all-comer trials, and is the basis to power for the overall treatment effect. For analysis, testing will be first performed on biomarker-positive subpopulation; only if treatment benefit is established in this subpopulation will overall treatment effect be tested using the weighted statistic. [Joint with Yijie Zhou from AbbVie]
Shu-Chih Su (Merck) “A Population-Enrichment Adaptive Design Strategy for Vaccine Efficacy Trial”
Adaptive design has the flexibility allowing pre-specified modifications to an ongoing trial to mitigate the potential risk associated with the assumptions made at the design stage. It allows studies to include broader target patient population and to evaluate the performance of vaccine/drug across subpopulations simultaneously. Our work is motivated by a Phase III event-driven vaccine efficacy trial. Two target patient populations are being enrolled with the assumption that vaccine efficacy can be demonstrated based on the two patient subpopulations combined. It is recognized due to the heterogeneity of the patient characteristics, the two subpopulations might respond to the vaccine differently. i.e., the vaccine efficacy (VE) in one population could be lower than that in the other. To maximize the probability of demonstrating vaccine efficacy in at least one patient population while taking advantage of combining two populations in one single trial, an adaptive design strategy with potential population enrichment is developed. Specifically, if the observed vaccine efficacy at interim for one subpopulation is not promising to warrant carrying forward, the enrollment in the other population can be enriched. Simulations were conducted to evaluate the operational characteristics of different timing and futility boundaries for interim analysis. This population enrichment design provides a more efficient way as compared to the conventional approaches with several target subpopulations. If executed and planned with caution, it can improve the probability of having a successful trial. [Joint with Ivan S.F. Chan from Merck]
Hui Quan (Sanofi) “Adaptive Patient Population Selection Design in Clinical Trials”
For the success of a new drug development, it is crucial to select the sensitive patient populations. To potentially reduce timeline and cost, we may apply a two-stage adaptive patient population selection design to a therapeutic trial. In such a design, based on early results of the trial, patient population(s) will be selected/determined for the final stage and analysis. Because of this adaptive nature and the multiple between-treatment comparisons for multiple populations, an alpha adjustment is necessary. In this paper, we propose a closed step down testing procedure to assess treatment effects on multiple populations and a weighted combination test to combine data from the two stages after sample size adaptation. Computation/simulation is used to compare the performances of the proposed procedure and the other multiplicity adjustment procedures. A trial simulation is presented to illustrate the application of the methods. [Joint with Dongli Zhou, Pierre Mancini, Yi He and Gary Koch from Sanofi]
