INTRODUCTION
REGRESSION
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.
Stratified randomisation is a popular approach for randomly assigning participants to treatment groups in clinical trials.It is widely recognised that analytic methods should reflect the trial design and hence adjustment should be made for the stratification variables in the analysis of trials that use stratified randomisation.When a stratification variable is affected by measurement error, some participants may be misclassified and hence randomised in the incorrect stratum. Such errors, referred to hereafter as Several previous studies have explored the impact of stratification errors on treatment effect estimation using regression models. Ke et al.The aims of the current article are: (1) to explore the impact of stratification errors on the correlation between sample means induced by stratification; and (2) 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. In Sections 