Optimizing trial design in pharmacogenetics research: comparing a fixed parallel group, group sequential, and adaptive selection design on sample size requirements

Abstract

Two-stage clinical trial designs may be efficient in pharmacogenetics research when there is some but inconclusive evidence of effect modification by a genomic marker. Two-stage designs allow to stop early for efficacy or futility and can offer the additional opportunity to enrich the study population to a specific patient subgroup after an interim analysis. This study compared sample size requirements for fixed parallel group, group sequential, and adaptive selection designs with equal overall power and control of the family-wise type I error rate. The designs were evaluated across scenarios that defined the effect sizes in the marker positive and marker negative subgroups and the prevalence of marker positive patients in the overall study population. Effect sizes were chosen to reflect realistic planning scenarios, where at least some effect is present in the marker negative subgroup. In addition, scenarios were considered in which the assumed 'true' subgroup effects (i.e., the postulated effects) differed from those hypothesized at the planning stage. As expected, both two-stage designs generally required fewer patients than a fixed parallel group design, and the advantage increased as the difference between subgroups increased. The adaptive selection design added little further reduction in sample size, as compared with the group sequential design, when the postulated effect sizes were equal to those hypothesized at the planning stage. However, when the postulated effects deviated strongly in favor of enrichment, the comparative advantage of the adaptive selection design increased, which precisely reflects the adaptive nature of the design.

Adaptive designs for subpopulation analysis optimizing utility functions

“…If we assume that the cost to treat a patient in (where the treatment is not efficacious) is equal to the gain to treat a patient in S (where the treatment is efficacious), the utility assigned to the event that is rejected when the treatment is only efficacious in S , is given by . This corresponds to in 2013.…”

“…Thus, can only be rejected if also a minimum efficacy in is observed. For a given prior, prevalence, and parameters and τ we optimized the consistency boundary c together with α 0 and r to maximize the utility function 2013. We determined the optimal design parameters by simulating the expected utility over a grid of the parameters and r ranging from 0 to 1 in steps of 0.01.…”

“…It has been shown that adaptive designs may lead to superior statistical power compared to fixed sample designs, where power is usually defined as the power to reject at least one false null hypothesis (Wang et al, 2009; Boessen et al, 2013). In a setting where multiple hypotheses are tested, however, this may not be the only operating characteristic of interest.…”

Tailoring treatments using treatment effect modification

“…there is a beneficial average effect), the second study period (the period following the interim analysis) can be used to enrich the patient sample to explore heterogeneity between pre‐specified clinically important patient subgroups. We recognize that this contrasts with the more usual approach of focusing on a single promising subgroup after interim . Here we actually reverse the usual approach; we start with a subgroup where we expect treatment to be most beneficial and in the second stage (after interim) explore consistency of this treatment effect across important subgroups.…”

“…equally sized subgroups), ensuring appropriate power and type 1 error rates. One attractive idea is to incorporate interaction tests using adaptive trial designs . For example, consider an RCT of a particular treatment, conducted within a homogenous group of patients.…”