Principal Stratum Strategy: Potential Role in Drug Development

TL;DR

This paper discusses the principal stratum strategy in drug development, emphasizing its potential to improve causal inference in the presence of intercurrent events, and advocates for its integration with the ICH E9(R1) estimand framework.

Contribution

It introduces the principal stratum strategy as a method to address intercurrent events, illustrating its application and discussing assumptions and sensitivity analyses in drug development.

Findings

Principal stratum strategy clarifies causal effects post-intercurrent events.

Integration with ICH E9(R1) enhances transparency and appropriateness of analyses.

Most assumptions are unverifiable and require scientific justification.

Abstract

A randomized trial allows estimation of the causal effect of an intervention compared to a control in the overall population and in subpopulations defined by baseline characteristics. Often, however, clinical questions also arise regarding the treatment effect in subpopulations of patients, which would experience clinical or disease related events post-randomization. Events that occur after treatment initiation and potentially affect the interpretation or the existence of the measurements are called {\it intercurrent events} in the ICH E9(R1) guideline. If the intercurrent event is a consequence of treatment, randomization alone is no longer sufficient to meaningfully estimate the treatment effect. Analyses comparing the subgroups of patients without the intercurrent events for intervention and control will not estimate a causal effect. This is well known, but post-hoc analyses of this kind are commonly performed in drug development. An alternative approach is the principal stratum strategy, which classifies subjects according to their potential occurrence of an intercurrent event on both study arms. We illustrate with examples that questions formulated through principal strata occur naturally in drug development and argue that approaching these questions with the ICH E9(R1) estimand framework has the potential to lead to more transparent assumptions as well as more adequate analyses and conclusions. In addition, we provide an overview of assumptions required for estimation of effects in principal strata. Most of these assumptions are unverifiable and should hence be based on solid scientific understanding. Sensitivity analyses are needed to assess robustness of conclusions.