Semiparametric Bayesian commensurate survival model for post‐market medical device surveillance with non‐exchangeable historical data
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A fully Bayesian semiparametric method for the purpose of attenuating bias and increasing efficiency when jointly modeling time‐to‐event data from two possibly non‐exchangeable sources of information is proposed.Abstract:
Trial investigators often have a primary interest in the estimation of the survival curve in a population for which there exists acceptable historical information from which to borrow strength. However, borrowing strength from a historical trial that is non-exchangeable with the current trial can result in biased conclusions. In this paper we propose a fully Bayesian semiparametric method for the purpose of attenuating bias and increasing efficiency when jointly modeling time-to-event data from two possibly non-exchangeable sources of information. We illustrate the mechanics of our methods by applying them to a pair of post-market surveillance datasets regarding adverse events in persons on dialysis that had either a bare metal or drug-eluting stent implanted during a cardiac revascularization surgery. We finish with a discussion of the advantages and limitations of this approach to evidence synthesis, as well as directions for future work in this area. The paper’s Supplementary Materials offer simulations to show our procedure’s bias, mean squared error, and coverage probability properties in a variety of settings.read more
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Bayesian Survival Analysis.
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Including historical data in the analysis of clinical trials: Is it worth the effort?
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Propensity score-integrated power prior approach for incorporating real-world evidence in single-arm clinical studies.
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References
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Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling
TL;DR: JAGS is a program for Bayesian Graphical modelling which aims for compatibility with Classic BUGS and could eventually be developed as an R package.
Journal ArticleDOI
Generalized Additive Models: An Introduction With R
TL;DR: Robinson, R. (2007). Generalized Additive Models: An Introduction With R.(2007).
Book
Survival Analysis: Techniques for Censored and Truncated Data
TL;DR: Survival analysis:techniques for censored and truncated data, Survival analysis: techniques for censored data analysis, survival analysis, and survival analysis techniques for truncated and uncoded data analysis.
Journal ArticleDOI
The BUGS project: Evolution, critique and future directions
TL;DR: A balanced critical appraisal of the BUGS software is provided, highlighting how various ideas have led to unprecedented flexibility while at the same time producing negative side effects.