Modeling and prediction of subject accrual and event times in clinical trials: a systematic review.
BACKGROUND
Modeling and prediction of subject accrual and event times in clinical trials has been a topic of considerable interest for important practical reasons. It has implications not only at the initial planning stage of a trial but also on its ongoing monitoring.


OBJECTIVE
To provide a systematic view of the recent research in the field of modeling and prediction of subject accrual and event times in clinical trials.


METHODS
Two classes of methods for modeling and prediction of subject accrual are reviewed, namely, one that uses the Brownian motion and the other uses the Poisson process. Extensions of the accrual models in multicenter clinical trials are also discussed. Trials with survival endpoints require proper joint modeling of subject accrual and event/lost-to-follow-up (LTFU) times, the latter of which can be modeled either parametrically (e.g., exponential and Weibull) or nonparametrically.


RESULTS
Flexible stochastic models are better suited when modeling real trials that does not follow constant underlying enrollment rate. The accrual model generally improves as center-specific information is accounted for in multicenter trials. The choice between parametric and nonparametric event models can depend on confidence on the underlying event rates.


CONCLUSIONS
All methods reviewed in event modeling assume noninformative censoring, which cannot be tested.


CONCLUSIONS
We recommend using proper stochastic accrual models, in combination with flexible event time models when applicable, for modeling and prediction of subject enrollment and event times in clinical trials.
Pfizer Inc., New York, NY 10017, USA. xiaoxi.zhang@pfizer.com
Xiaoxi
X
Zhang
Qi
Q
Long
D002986:Clinical Trials as Topic / Q000379:methods*; D003627:Data Interpretation, Statistical; D023381:Endpoint Determination / Q000379:methods*; D059012:Lost to Follow-Up; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000379:methods; D018579:Patient Selection; D016012:Poisson Distribution; D018709:Statistics, Nonparametric; D013269:Stochastic Processes; D016019:Survival Analysis; D013997:Time Factors