A novel estimand to adjust for rescue treatment in clinical trials

TL;DR

This paper introduces a new estimand to accurately measure treatment effects in clinical trials affected by rescue medication, avoiding complex extrapolations and enabling clearer interpretation of results.

Contribution

It proposes a novel estimand and inverse probability weighting method to disentangle treatment effects from rescue medication effects in clinical trials.

Findings

Estimator is unbiased in moderate samples

Method effectively separates treatment and rescue effects

Sensitivity analysis addresses untestable assumptions

Abstract

The interpretation of randomised clinical trial results is often complicated by intercurrent events. For instance, rescue medication is sometimes given to patients in response to worsening of their disease, either in addition to the randomised treatment or in its place. The use of such medication complicates the interpretation of the intention-to-treat analysis. In view of this, we propose a novel estimand defined as the intention-to-treat effect that would have been observed, had patients on the active arm been switched to rescue medication if and only if they would have been switched when randomised to control. This enables us to disentangle the treatment effect from the effect of rescue medication on a patient's outcome, while avoiding the strong extrapolations that are typically needed when inferring what the intention-to-treat effect would have been in the absence of rescue medication. We develop an inverse probability weighting method to estimate this estimand under specific untestable assumptions, in view of which we propose a sensitivity analysis. We use the method for the analysis of a clinical trial conducted by Janssen Pharmaceuticals, in which chronically ill patients can switch to rescue medication for ethical reasons. Monte Carlo simulations confirm that the proposed estimator is unbiased in moderate sample sizes.