Title
The utility of Bayesian predictive probabilities for interim monitoring of clinical trials
Abbreviated Journal Title
Clin. Trials
Keywords
SEQUENTIAL TEST DESIGNS; 2-STAGE DESIGNS; CANCER; POWER; SIZE; Medicine, Research & Experimental
Abstract
Background Bayesian predictive probabilities can be used for interim monitoring of clinical trials to estimate the probability of observing a statistically significant treatment effect if the trial were to continue to its predefined maximum sample size. Purpose We explore settings in which Bayesian predictive probabilities are advantageous for interim monitoring compared to Bayesian posterior probabilities, p-values, conditional power, or group sequential methods. Results For interim analyses that address prediction hypotheses, such as futility monitoring and efficacy monitoring with lagged outcomes, only predictive probabilities properly account for the amount of data remaining to be observed in a clinical trial and have the flexibility to incorporate additional information via auxiliary variables. Limitations Computational burdens limit the feasibility of predictive probabilities in many clinical trial settings. The specification of prior distributions brings additional challenges for regulatory approval. Conclusions The use of Bayesian predictive probabilities enables the choice of logical interim stopping rules that closely align with the clinical decision-making process.
Journal Title
Clinical Trials
Volume
11
Issue/Number
4
Publication Date
1-1-2014
Document Type
Article; Proceedings Paper
Language
English
First Page
485
Last Page
493
WOS Identifier
ISSN
1740-7745
Recommended Citation
"The utility of Bayesian predictive probabilities for interim monitoring of clinical trials" (2014). Faculty Bibliography 2010s. 6049.
https://stars.library.ucf.edu/facultybib2010/6049
Comments
Authors: contact us about adding a copy of your work at STARS@ucf.edu