Abstract
Adaptive enrichment designs involve rules for restricting enrollment to a subset of the population during the course of an ongoing trial. This can be used to target those who benefit from the experimental treatment. Trial characteristics such as the accrual rate and the prognostic value of baseline variables are typically unknown when a trial is being planned; these values are typically assumed based on information available before the trial starts. Because of the added complexity in adaptive enrichment designs compared to standard designs, it may be of special concern how sensitive the trial performance is to deviations from assumptions. Through simulation studies, we evaluate the sensitivity of Type I error, power, expected sample size, and trial duration to different design characteristics. Our simulation distributions mimic features of data from the Alzheimer's Disease Neuroimaging Initiative cohort study, and involve two subpopulations based on a genetic marker. We investigate the impact of the following design characteristics: the accrual rate, the time from enrollment to measurement of a short-term outcome and the primary outcome, and the prognostic value of baseline variables and short-term outcomes. To leverage prognostic information in baseline variables and short-term outcomes, we use a semiparametric, locally efficient estimator, and investigate its strengths and limitations compared to standard estimators. We apply information-based monitoring, and evaluate how accurately information can be estimated in an ongoing trial.