The Critical Role of Statistical Analyses in Maximizing Power Gains From Covariate-Adaptive Trial Designs

Abstract

The proposed randomized clinical trial design by Stankiewicz Karita et al 1 addresses the important question of whether the human papillomavirus (HPV) vaccine Gardasil-9 reduces recurrence of anal and vulvar high-grade squamous intraepithelial lesions (HSIL). The target population is unvaccinated adults with a history of anal or vulvar HSIL. The trial is generally well designed to achieve the study objective.We first describe a potential improvement to the trial design, which involves the primary statistical analysis. The protocol's primary analysis involves using a Cox proportional hazards model with time to recurrence as the outcome. No baseline variables are adjusted for in the primary analysis.We recommend that the primary analysis be changed to adjust for the 3 baseline variables (called covariates) used in the covariate-adaptive randomization procedure. Such adjustment is generally recommended in randomized trials that use stratified randomization or covariate-adaptive randomization. [2][3][4] One of the main reasons for such adjustment is to improve power. The authors are correct that using covariate-adaptive randomization can lead to greater power. However, the power gains are only realized if the reduced estimator variance due to covariate-adaptive randomization is accounted for in the analysis. Covariate adjustment can be used to obtain an accurate estimate of this variance, which could be overestimated (leading to power loss) if the covariates were ignored in the analysis. In brief, not adjusting for the covariates would effectively throw away any power gain from covariate-adaptive randomization; it could also result in confidence intervals that are wider than necessary. There are multiple statistical methods available to adjust for baseline covariates in randomized trials with time-to-event outcomes. [5][6][7] The method of Díaz et al 5 avoids making the proportional hazards assumption by focusing on estimation of the restricted mean survival time, ie, the average survival time truncated at a final point such as 36 months. All of the aforementioned methods also adjust for missing data due to loss to follow-up.It is possible to include other baseline variables, in addition to the ones used in the covariateadaptive randomization, in the primary analysis. This may lead to improved precision and power, as long as the new variables explain some of the residual variation that is left after adjustment for the variables in the covariate-adaptive randomization. Previous data sets and clinical knowledge could be used to identify which (if any) variables are likely to add substantial value. An important consideration is to avoid using too many baseline variables because this may lead to poor estimator performance; this is of special concern at smaller sample sizes such as when subpopulations are analyzed. All aspects of the primary analysis need to be prespecified in the study protocol, including the set of variables to be adjusted for and the statistical method to be used.The trial goals include estimation of t...