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Predicting event times in clinical trials when randomization is masked and blocked

Pharmaceutical Research Institute, Bristol-Myers Squibb Company, Pennington, NJ 08534, USA, mark.donovan{at}bms.com

Michael R. Elliott

Department of Biostatistics, University of Michigan School of Public Health and Survey Methodology Program, University of Michigan Institute for Social Research, USA

Daniel F. Heitjan

Center for Clinical Epidemology and Biostatistics, Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, USA

Background Timing for interim or final analysis of data in an event-based trial is often determined by the accrual of events during the study. Existing Bayesian methods may be used to predict the date of the landmark event using observed enrollment, event, and loss times when treatment arm information is masked.

Purpose For event-based trials with a blocked randomization, knowledge of blocks in which patients are enrolled can provide additional information to improve predictions versus models that only assume a known treatment allocation proportion. We therefore propose to incorporate blocking information into existing methods for prediction.

Methods We derive a latent variable (LV) extension of a mixture model used in Donovan JM, Elliott MR, Heitjan DF. Predicting Event Times in Clinical Trials When Treatment Arm is Masked. J Biopharmaceut Stat 2006; 16:343—56 to incorporate block randomization (constrained LV) for predicting the landmark event and compare this model with (a) methods where blocking information is ignored (unconstrained LV), and (b) methods assuming a single population (SP).

Results Comparison of the constrained and unconstrained LV models in our application shows that the constrained LV model has narrower prediction intervals. Simulation studies show that the constrained LV model can have better coverage probabilities for the prediction intervals than SP models if a treatment effect is present, and prediction intervals from the constrained LV model are narrower than those for the unconstrained LV model. These differences varied by block size and prediction time.

Limitations We have limited focus to the exponential model for events. Coverage for the LV models may be somewhat reduced if no treatment effect is present.

Conclusions Extra information provided by knowledge of blocking can be used to decrease prediction interval width versus the unconstrained LV model, while providing better coverage properties than the SP model if a treatment effect is present. Clinical Trials 2007; 4: 481—490. http://ctj.sagepub.com

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