Frequentist rules for regulatory approval of subgroups in phase III trials: A fresh look at an old problem

Abstract

Background The number of Phase III trials that include a biomarker in design and analysis has increased due to interest in personalised medicine. For genetic mutations and other predictive biomarkers, the trial sample comprises two subgroups, one of which, say [Formula: see text] is known or suspected to achieve a larger treatment effect than the other [Formula: see text]. Despite treatment effect heterogeneity, trials often draw patients from both subgroups, since the lower responding [Formula: see text] subgroup may also gain benefit from the intervention. In this case, regulators/commissioners must decide what constitutes sufficient evidence to approve the drug in the [Formula: see text] population. Methods and Results Assuming trial analysis can be completed using generalised linear models, we define and evaluate three frequentist decision rules for approval. For rule one, the significance of the average treatment effect in [Formula: see text] should exceed a pre-defined minimum value, say [Formula: see text]. For rule two, the data from the low-responding group [Formula: see text] should increase statistical significance. For rule three, the subgroup-treatment interaction should be non-significant, using type I error chosen to ensure that estimated difference between the two subgroup effects is acceptable. Rules are evaluated based on conditional power, given that there is an overall significant treatment effect. We show how different rules perform according to the distribution of patients across the two subgroups and when analyses include additional (stratification) covariates in the analysis, thereby conferring correlation between subgroup effects. Conclusions When additional conditions are required for approval of a new treatment in a lower response subgroup, easily applied rules based on minimum effect sizes and relaxed interaction tests are available. Choice of rule is influenced by the proportion of patients sampled from the two subgroups but less so by the correlation between subgroup effects.

Estimands and Complex Innovative Designs

“…To inform this decision, the efficacy in BM− is crucial. 37 Whether or not defined in the study protocol, the estimand for BM− may therefore be important. Its definition will typically follow directly from those of the other estimands, i.e., will mainly differ by the ”population” attribute.…”

“…While the trial sponsor may be interested in showing efficacy in AC (or BM+), regulators and payers may ask whether a proven efficacy in AC should lead to approval and reimbursement in the full population or, say, only in BM+. To inform this decision, the efficacy in BM− is crucial 37 . Whether or not defined in the study protocol, the estimand for BM− may therefore be important.…”