Planning a method for covariate adjustment in individually-randomised trials: a practical guide

TL;DR

This paper compares three covariate adjustment methods—direct adjustment, standardisation, and inverse-probability-of-treatment weighting—in randomized trials, providing guidance on their selection based on trial context and statistical properties.

Contribution

It offers a practical framework for choosing among covariate adjustment methods in randomized trials, highlighting their similarities, differences, and appropriate applications.

Findings

All three methods are asymptotically efficient.

Mis-specification of covariate functions reduces efficiency.

Convergence issues vary among methods, with IPTW being most robust.

Abstract

Background: It has long been advised to account for baseline covariates in the analysis of confirmatory randomised trials, with the main statistical justifications being that this increases power and, when a randomisation scheme balanced covariates, permits a valid estimate of experimental error. There are various methods available to account for covariates but it is not clear how to choose among them. Methods: Taking the perspective of writing a statistical analysis plan, we consider how to choose between the three most promising broad approaches: direct adjustment, standardisation and inverse-probability-of-treatment weighting. Results: The three approaches are similar in being asymptotically efficient, in losing efficiency with mis-specified covariate functions, and in handling designed balance. If a marginal estimand is targeted (for example, a risk difference or survival difference), then direct adjustment should be avoided because it involves fitting non-standard models that are subject to convergence issues. Convergence is most likely with IPTW. Robust standard errors used by IPTW are anti-conservative at small sample sizes. All approaches can use similar methods to handle missing covariate data. With missing outcome data, each method has its own way to estimate a treatment effect in the all-randomised population. We illustrate some issues in a reanalysis of GetTested, a randomised trial designed to assess the effectiveness of an electonic sexually-transmitted-infection testing and results service. Conclusions: No single approach is always best: the choice will depend on the trial context. We encourage trialists to consider all three methods more routinely.