Improving interim decisions in randomized trials by exploiting information on short-term outcomes and prognostic baseline covariates

TL;DR

This paper introduces an improved interim decision method for randomized trials that leverages short-term outcomes and baseline covariates, leading to more efficient decisions and smaller sample sizes without increasing error rates.

Contribution

It proposes a novel interim decision procedure based on conditional power that incorporates baseline covariates and short-term outcomes, enhancing efficiency and enabling earlier decisions.

Findings

Increased efficiency and reduced sample size in trials.

Maintains Type I error rate despite model misspecification.

Allows earlier decisions compared to standard methods.

Abstract

Conditional power calculations are frequently used to guide the decision whether or not to stop a trial for futility or to modify planned sample size. These ignore the information in short-term endpoints and baseline covariates, and thereby do not make fully efficient use of the information in the data. We therefore propose an interim decision procedure based on the conditional power approach which exploits the information contained in baseline covariates and short-term outcomes. We will realise this by considering the estimation of the treatment effect at the interim analysis as a missing data problem. This problem is addressed by employing specific prediction models for the long-term endpoint which enable the incorporation of baseline covariates and multiple short-term endpoints. We show that the proposed procedure leads to an efficiency gain and a reduced sample size, without compromising the Type I error rate of the procedure, even when the adopted prediction models are misspecified. In particular, implementing our proposal in the conditional power approach allows earlier decisions relative to standard approaches, whilst controlling the probability of an incorrect decision. This time gain results in a lower expected number of recruited patients in case of stopping for futility, such that fewer patients receive the futile regimen. We explain how these methods can be used in adaptive designs with unblinded sample size reassessment based on the inverse normal $p$-value combination method to control type I error. We support the proposal by Monte Carlo simulations based on data from a real clinical trial.