Guidelines for estimating causal effects in pragmatic randomized trials

TL;DR

This paper provides comprehensive guidelines for estimating causal effects in pragmatic randomized trials, addressing challenges like non-adherence and emphasizing both intention-to-treat and per-protocol effects for better clinical decision-making.

Contribution

It introduces new, detailed methods for validly estimating both intention-to-treat and per-protocol effects in pragmatic trials with diverse populations and real-world settings.

Findings

Guidelines improve causal inference accuracy in pragmatic trials.

Methods account for non-adherence and heterogeneity.

Enhanced interpretability of trial results for stakeholders.

Abstract

Pragmatic randomized trials are designed to provide evidence for clinical decision-making rather than regulatory approval. Common features of these trials include the inclusion of heterogeneous or diverse patient populations in a wide range of care settings, the use of active treatment strategies as comparators, unblinded treatment assignment, and the study of long-term, clinically relevant outcomes. These features can greatly increase the usefulness of the trial results for patients, clinicians, and other stakeholders. However, these features also introduce an increased risk of non-adherence, which reduces the value of the intention-to-treat effect as a patient-centered measure of causal effect. In these settings, the per-protocol effect provides useful complementary information for decision making. Unfortunately, there is little guidance for valid estimation of the per-protocol effect. Here, we present our full guidelines for analyses of pragmatic trials that will result in more informative causal inferences for both the intention-to-treat effect and the per-protocol effect.