Using principal stratification in analysis of clinical trials

TL;DR

This paper provides a comprehensive overview of principal stratification in clinical trial analysis, discussing its assumptions, methods, and applications with illustrative examples and code.

Contribution

It unifies various approaches to principal stratification, clarifies their assumptions, and demonstrates their application in clinical trials with practical examples.

Findings

Principal stratification offers a structured framework for handling intercurrent events.

Strong assumptions are required for effect estimation within PS, impacting validity.

The paper illustrates methods with real clinical trial data and provides implementation code.

Abstract

The ICH E9(R1) addendum (2019) proposed principal stratification (PS) as one of five strategies for dealing with intercurrent events. Therefore, understanding the strengths, limitations, and assumptions of PS is important for the broad community of clinical trialists. Many approaches have been developed under the general framework of PS in different areas of research, including experimental and observational studies. These diverse applications have utilized a diverse set of tools and assumptions. Thus, need exists to present these approaches in a unifying manner. The goal of this tutorial is threefold. First, we provide a coherent and unifying description of PS. Second, we emphasize that estimation of effects within PS relies on strong assumptions and we thoroughly examine the consequences of these assumptions to understand in which situations certain assumptions are reasonable. Finally, we provide an overview of a variety of key methods for PS analysis and use a real clinical trial example to illustrate them. Examples of code for implementation of some of these approaches are given in supplemental materials.