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The primary goal of randomized trials is to compare the effects of differentinterventions on some outcome of interest. In addition to the treatmentassignment and outcome, data on baseline covariates, such as demographiccharacteristics or biomarker measurements, are typically collected.Incorporating such auxiliary covariates in the analysis of randomized trialscan increase power, but questions remain about how to preserve type I errorwhen incorporating such covariates in a flexible way, particularly when thenumber of randomized units is small. Using the Young Citizens study, acluster-randomized trial of an educational intervention to promote HIVawareness, we compare several methods to evaluate intervention effects whenbaseline covariates are incorporated adaptively. To ascertain the validity ofthe methods shown in small samples, extensive simulation studies wereconducted. We demonstrate that randomization inference preserves type I errorunder model selection while tests based on asymptotic theory may yield invalidresults. We also demonstrate that covariate adjustment generally increasespower, except at extremely small sample sizes using liberal selectionprocedures. Although shown within the context of HIV prevention research, ourconclusions have important implications for maximizing efficiency androbustness in randomized trials with small samples across disciplines.

Flexible covariate-adjusted exact tests of randomized treatment effects with application to a trial of HIV education