Clarifying identification and estimation of treatment effects in the Sequential Parallel Comparison Design

TL;DR

This paper uses causal inference to clarify the assumptions and limitations of the Sequential Parallel Comparison Design in clinical trials, highlighting issues with conventional estimators and the impact of placebo response misclassification.

Contribution

It provides a formal causal inference framework to identify treatment effects in SPCD trials and reveals the limitations of traditional estimators under certain assumptions.

Findings

Conventional SPCD estimators often do not target meaningful treatment effects.

Placebo response misclassification significantly biases second stage estimates.

Causal inference tools clarify the assumptions needed for valid treatment effect identification.

Abstract

Sequential parallel comparison design (SPCD) clinical trials aim to adjust active treatment effect estimates for placebo response to minimize the impact of placebo responders on the estimates. This is potentially accomplished using a two stage design by measuring treatment effects among all participants during the first stage, then classifying some placebo arm participants as placebo non-responders who will be re-randomized in the second stage. In this paper, we use causal inference tools to clarify under what assumptions treatment effects can be identified in SPCD trials and what effects the conventional estimators target at each stage of the SPCD trial. We further illustrate the highly influential impact of placebo response misclassification on the second stage estimate. We conclude that the conventional SPCD estimators do not target meaningful treatment effects.