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Identifying subgroups, which respond differently to a treatment, both interms of efficacy and safety, is an important part of drug development. Awell-known challenge in exploratory subgroup analyses is the small sample sizein the considered subgroups, which is usually too low to allow for definitecomparisons. In early phase trials this problem is further exaggerated, becauselimited or no clinical prior information on the drug and plausible subgroups isavailable. We evaluate novel strategies for treatment effect estimation inthese settings in a simulation study motivated by real clinical trialsituations. We compare several approaches to estimate treatment effects forselected subgroups, employing model averaging, resampling and Lasso regressionmethods. Two subgroup identification approaches are employed, one based oncategorization of covariates and the other based on splines. Our results showthat naive estimation of the treatment effect, which ignores that a selectionhas taken place, leads to bias and overoptimistic conclusions. For theconsidered simulation scenarios virtually all evaluated novel methods providemore adequate estimates of the treatment effect for selected subgroups, interms of bias, MSE and confidence interval coverage.

Comparing Approaches to Treatment Effect Estimation for Subgroups in Clinical Trials