Sample size calculation and blinded recalculation for analysis of covariance models with multiple random covariates

TL;DR

This paper introduces a method for re-estimating sample size during ANCOVA trials with multiple covariates, addressing planning challenges and reducing risks of incorrect assumptions, without unblinding data.

Contribution

It proposes a novel sample size re-estimation procedure for ANCOVA models with multiple covariates, improving trial planning and robustness.

Findings

Re-estimation method provides reliable results in various settings.

The procedure does not require unblinding of data.

Simulations confirm effectiveness across different designs.

Abstract

When testing for superiority in a parallel-group setting with a continuous outcome, adjusting for covariates (e.g., baseline measurements) is usually recommended, in order to reduce bias and increase power. For this purpose, the analysis of covariance (ANCOVA) is frequently used, and recently, several exact and approximate sample size calculation procedures have been proposed. However, in case of multiple covariates, the planning might pose some practical challenges and surprising pitfalls, which have not been recognized so far. Moreover, since a considerable number of parameters have to be specified in advance, the risk of making erroneous initial assumptions, leading to substantially over- or underpowered studies, is increased. Therefore, we propose a method, which allows for re-estimating the sample size at a prespecified time point during the course of the trial. Extensive simulations for a broad range of settings, including unbalanced designs, confirm that the proposed method provides reliable results in many practically relevant situations. An advantage of the reassessment procedure is that it does not require unblinding of the data. In order to facilitate the application of the proposed method, we provide some R code and discuss a real-life data example.