Bayesian imperfect information analysis for clinical recurrent data

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

Acknowledgments

The authors wish to express their sincere thanks to Professor Shaw Wang for the helpful English language copy editing and to the reviewers for comments on an early draft of this paper. This work is supported by the Jen-Ai Hospital and Chung-Shan Medical University of Taiwan (CSMU-JAH-103-01).

Disclosure

The authors report no conflicts of interest in this work.