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Bayesian imperfect information analysis for clinical recurrent data

Authors Chang C, Chang C

Received 30 April 2014

Accepted for publication 12 August 2014

Published 19 December 2014 Volume 2015:11 Pages 17—26

DOI https://doi.org/10.2147/TCRM.S67011

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 3

Editor who approved publication: Professor Garry Walsh


Chih-Kuang Chang,1 Chi-Chang Chang2

1Department of Cardiology, Jen-Ai Hospital, Dali District, Taichung, Taiwan; 2School of Medical Informatics, Chung Shan Medical University, Information Technology Office of Chung Shan Medical University Hospital, Taichung, Taiwan

Abstract: In medical research, clinical practice must often be undertaken with imperfect information from limited resources. This study applied Bayesian imperfect information-value analysis to realistic situations to produce likelihood functions and posterior distributions, to a clinical decision-making problem for recurrent events. In this study, three kinds of failure models are considered, and our methods illustrated with an analysis of imperfect information from a trial of immunotherapy in the treatment of chronic granulomatous disease. In addition, we present evidence toward a better understanding of the differing behaviors along with concomitant variables. Based on the results of simulations, the imperfect information value of the concomitant variables was evaluated and different realistic situations were compared to see which could yield more accurate results for medical decision-making.

Keywords: Bayesian value-of-information, recurrent events, chronic granulomatous disease

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