Adaptive designs for subpopulation analysis optimizing utility functions

Abstract

If the response to treatment depends on genetic biomarkers, it is important to identify predictive biomarkers that define (sub-)populations where the treatment has a positive benefit risk balance. One approach to determine relevant subpopulations are subgroup analyses where the treatment effect is estimated in biomarker positive and biomarker negative groups. Subgroup analyses are challenging because several types of risks are associated with inference on subgroups. On the one hand, by disregarding a relevant subpopulation a treatment option may be missed due to a dilution of the treatment effect in the full population. Furthermore, even if the diluted treatment effect can be demonstrated in an overall population, it is not ethical to treat patients that do not benefit from the treatment when they can be identified in advance. On the other hand, selecting a spurious subpopulation increases the risk to restrict an efficacious treatment to a too narrow fraction of a potential benefiting population. We propose to quantify these risks with utility functions and investigate nonadaptive study designs that allow for inference on subgroups using multiple testing procedures as well as adaptive designs, where subgroups may be selected in an interim analysis. The characteristics of such adaptive and nonadaptive designs are compared for a range of scenarios.

Group Sequential and Confirmatory Adaptive Designs in Clinical Trials

“…11.1.1). It was first proposed in Brannath et al (2009b) who used Bayesian decision tools for the selection rule (see also, Götte et al 2015;Graf et al 2015;Krisam and Kieser 2014). We describe the methodology and designing issues when planning such a design, and give some clinical trial examples where such design can be used.…”

Challenges and methodology in the incorporation of biomarkers in cancer clinical trials

“…Adaptive versions of the aforementioned marker-based designs also have been proposed such as the Bayesian adaptive marker-stratified design (27), the adaptive enrichment design (30,31) and an adaptive version of testing approaches using utility functions (32). Furthermore, a Bayesian prediction model has been proposed to help predict whether a biomarker is truly associated to a clinical outcome using a meta-analytic approach (33).…”

Clinical trial designs incorporating predictive biomarkers

“…An analytical and simulation-based comparison of popular fixed and adaptive biomarker-based designs was performed by Graf et al, with the objective of maximizing expected utility [59]. Specifically, utility functions were developed from both sponsor and public health points of view to quantify the relative gains and risks associated with rejected overall or subgroup hypotheses, accounting for possibly incorrect biomarker-based decisions such as inappropriate enrichment or maker-subgroup type I errors.…”