Ventricular arrhythmias: risk stratification and approach to therapy after the Cardiac Arrhythmia Suppression Trial (CAST)

Assessing treatment effect heterogeneity in clinical trials with blocked binary outcomes

This paper addresses treatment effect heterogeneity (also referred to, more compactly, as 'treatment heterogeneity') in the context of a controlled clinical trial with binary endpoints. Treatment heterogeneity, variation in the true (causal) individual treatment effects, is explored using the concept of the potential outcome. This framework supposes the existance of latent responses for each subject corresponding to each possible treatment. In the context of a binary endpoint, treatment heterogeniety may be represented by the parameter, pi2, the probability that an individual would have a failure on the experimental treatment, if received, and would have a success on control, if received. Previous research derived bounds for pi2 based on matched pairs data. The present research extends this method to the blocked data context. Estimates (and their variances) and confidence intervals for the bounds are derived. We apply the new method to data from a renal disease clinical trial. In this example, bounds based on the blocked data are narrower than the corresponding bounds based only on the marginal success proportions. Some remaining challenges (including the possibility of further reducing bound widths) are discussed.

Direct effect on validity of response run-in selection in clinical trials

A run-in is a period prior to randomization during which potential participants who have met all entry criteria for a randomized clinical trial are assigned the same regimen, either the control (possibly placebo) or the experimental treatment. Typically, the intention is to exclude from the subsequent study (i.e., randomization) some segment of this cohort, based on their experiences during the run-in period. Selecting patients based on the run-in thereby forces differential representation of certain subpopulations relative to others. Previous studies have addressed the potential for a run-in to jeopardize validity through unintended mechanisms. While these concerns are valid, they leave open the possibility that modifications to the design of the run-in might be able to preserve validity, even if patient selection is based on the results of the run-in. As such, we address the potential for selecting patients based on response during a run-in period to jeopardize validity directly, through its intended effect of overrepresenting (relative to the screened population) some segments in the randomized portion of the trial and underrepresenting others.