Predicting event times in clinical trials when treatment arm is masked

J. Mark Donovan, Michael R. Elliott, Daniel F. Heitjan

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Because power is primarily determined by the number of events in event-based clinical trials, the timing for interim or final analysis of data is often determined based on the accrual of events during the course of the study. Thus, it is of interest to predict early and accurately the time of a landmark interim or terminating event. Existing Bayesian methods may be used to predict the date of the landmark event, based on current enrollment, event, and loss to follow-up, if treatment arms are known. This work extends these methods to the case where the treatment arms are masked by using a parametric mixture model with a known mixture proportion. Posterior simulation using the mixture model is compared with methods assuming a single population. Comparison of the mixture model with the single-population approach shows that with few events, these approaches produce substantially different results and that these results converge as the prediction time is closer to the landmark event. Simulations show that the mixture model with diffuse priors can have better coverage probabilities for the prediction interval than the nonmixture models if a treatment effect is present.

Original languageEnglish (US)
Pages (from-to)343-356
Number of pages14
JournalJournal of Biopharmaceutical Statistics
Volume16
Issue number3
DOIs
StatePublished - May 1 2006

Keywords

  • Bayesian prediction
  • Clinical trials
  • Survival analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)

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