Handling misclassified stratification variables in the analysis of randomised trials with continuous outcomes

Abstract

Many trials use stratified randomisation, where participants are randomised within strata defined by one or more baseline covariates. While it is important to adjust for stratification variables in the analysis, the appropriate method of adjustment is unclear when stratification variables are affected by misclassification and hence some participants are randomised in the incorrect stratum. We conducted a simulation study to compare methods of adjusting for stratification variables affected by misclassification in the analysis of continuous outcomes when all or only some stratification errors are discovered, and when the treatment effect or treatment-by-covariate interaction effect is of interest. The data were analysed using linear regression with no adjustment, adjustment for the strata used to perform the randomisation (randomisation strata), adjustment for the strata if all errors are corrected (true strata), and adjustment for the strata after some errors are discovered and corrected (updated strata). The unadjusted model performed poorly in all settings. Adjusting for the true strata was optimal, while the relative performance of adjusting for the randomisation strata or the updated strata varied depending on the setting. As the true strata are unlikely to be known with certainty in practice, we recommend using the updated strata for adjustment and performing subgroup analyses, provided the discovery of errors is unlikely to depend on treatment group, as expected in blinded trials. Greater transparency is needed in the reporting of stratification errors and how they were addressed in the analysis.

Categorisation of continuous covariates for stratified randomisation: How should we adjust?

“…While stratified randomisation is extensively used, practical considerations for trial design and analysis are still being addressed. For example, recent work has demonstrated the importance of adjusting for stratification variables, 9 links between the statistical properties of various estimators under both simple and stratified randomisation, 7,8 methods for addressing misclassified stratification variables, 33 and optimal approaches for creating stratification categories for skewed continuous prognostic variables 34 . This paper adds to this growing literature by demonstrating the advantages of leaving continuous variables used for stratified randomisation as continuous during analysis.…”