Comparing Approaches to Treatment Effect Estimation for Subgroups in Clinical Trials

TL;DR

This paper evaluates novel statistical methods for estimating treatment effects in small subgroups within clinical trials, addressing bias and overoptimism issues in early-phase studies through simulation comparisons.

Contribution

It introduces and compares new approaches like model averaging, resampling, and Lasso regression for subgroup treatment effect estimation in limited sample scenarios.

Findings

Novel methods reduce bias compared to naive estimates

All evaluated approaches improve confidence interval coverage

Methods perform well across various simulation scenarios

Abstract

Identifying subgroups, which respond differently to a treatment, both in terms of efficacy and safety, is an important part of drug development. A well-known challenge in exploratory subgroup analyses is the small sample size in the considered subgroups, which is usually too low to allow for definite comparisons. In early phase trials this problem is further exaggerated, because limited or no clinical prior information on the drug and plausible subgroups is available. We evaluate novel strategies for treatment effect estimation in these settings in a simulation study motivated by real clinical trial situations. We compare several approaches to estimate treatment effects for selected subgroups, employing model averaging, resampling and Lasso regression methods. Two subgroup identification approaches are employed, one based on categorization of covariates and the other based on splines. Our results show that naive estimation of the treatment effect, which ignores that a selection has taken place, leads to bias and overoptimistic conclusions. For the considered simulation scenarios virtually all evaluated novel methods provide more adequate estimates of the treatment effect for selected subgroups, in terms of bias, MSE and confidence interval coverage.