Abstract:
:This paper reviews Bayesian strategies for monitoring clinical trial data. It focuses on a Bayesian stochastic curtailment method based on the predictive probability of observing a clinically significant outcome at the scheduled end of the study given the observed data. The proposed method is applied to derive efficacy and futility stopping rules in clinical trials with continuous, normally distributed and binary endpoints. The sensitivity of the resulting stopping rules to the choice of prior distributions is examined and guidelines for choosing a prior distribution of the treatment effect are discussed. The Bayesian predictive approach is compared to the frequentist (conditional power) and mixed Bayesian-frequentist (predictive power) approaches. The interim monitoring strategies discussed in the paper are illustrated using examples from a small proof-of-concept study and a large mortality trial.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Dmitrienko A,Wang MDdoi
10.1002/sim.2204subject
Has Abstractpub_date
2006-07-15 00:00:00pages
2178-95issue
13eissn
0277-6715issn
1097-0258journal_volume
25pub_type
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