Group sequential methods for interim monitoring of randomized clinical trials with time-lagged outcome

TL;DR

This paper develops a group sequential framework for interim analysis in clinical trials with time-lagged outcomes, improving early stopping decisions by efficiently using all available data including censored observations.

Contribution

It introduces a novel semiparametric approach for treatment effect estimation that accounts for censoring and covariates, enhancing early stopping decisions in clinical trials.

Findings

Estimates incorporating censoring lead to stronger evidence for early stopping.

Test statistics have independent increments, enabling standard software use.

Proposed methods outperform standard approaches in simulation studies.

Abstract

The primary analysis in two-arm clinical trials usually involves inference on a scalar treatment effect parameter; e.g., depending on the outcome, the difference of treatment-specific means, risk difference, risk ratio, or odds ratio. Most clinical trials are monitored for the possibility of early stopping. Because ordinarily the outcome on any given subject can be ascertained only after some time lag, at the time of an interim analysis, among the subjects already enrolled, the outcome is known for only a subset and is effectively censored for those who have not been enrolled sufficiently long for it to be observed. Typically, the interim analysis is based only on the data from subjects for whom the outcome has been ascertained. A goal of an interim analysis is to stop the trial as soon as the evidence is strong enough to do so, suggesting that the analysis ideally should make the most efficient use of all available data, thus including information on censoring as well as other baseline and time-dependent covariates in a principled way. A general group sequential framework is proposed for clinical trials with a time-lagged outcome. Treatment effect estimators that take account of censoring and incorporate covariate information at an interim analysis are derived using semiparametric theory and are demonstrated to lead to stronger evidence for early stopping than standard approaches. The associated test statistics are shown to have the independent increments structure, so that standard software can be used to obtain stopping boundaries.