A novel estimand to adjust for rescue treatment in randomized clinical trials. (10th February 2021)

• Record Type: Journal Article
• Authors: Michiels, Hege; Sotto, Cristina; Vandebosch, An; Vansteelandt, Stijn
• Is Part Of: Statistics in medicine. Volume 40:Number 9(2021)
• Page Start: 2257
• Page End: 2271
• Publication Date: 2021-02-10
• Subjects: causal inference -- intercurrent events -- mediation -- posttreatment confounding -- treatment discontinuation -- treatment switching; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8901
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : The interpretation of randomized 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 randomized 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 randomized to control. This enables us to disentangle the treatment effect from the effect of rescue medication on a patient's outcome, while tempering 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 propose a novel inverse probability weighting method for estimating this effect in settings where the decision to initiate rescue medication is made at one prespecified time point. This estimator relies on 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 patients with type 2 diabetes mellitus can switch to rescue medication for ethical reasons. Monte Carlo simulations confirm that the proposed estimator is unbiased in moderate sample sizes.