prediction-prostate-surveillance swMATH ID: 23312 Software Authors: Coley, Rebecca Yates; Fisher, Aaron J.; Mamawala, Mufaddal; Carter, Herbert Ballentine; Pienta, Kenneth J.; Zeger, Scott L. Description: A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostate cancer. In this article, we present a Bayesian hierarchical model for predicting a latent health state from longitudinal clinical measurements. Model development is motivated by the need to integrate multiple sources of data to improve clinical decisions about whether to remove or irradiate a patient’s prostate cancer. Existing modeling approaches are extended to accommodate measurement error in cancer state determinations based on biopsied tissue, clinical measurements possibly not missing at random, and informative partial observation of the true state. The proposed model enables estimation of whether an individual’s underlying prostate cancer is aggressive, requiring surgery and/or radiation, or indolent, permitting continued surveillance. These individualized predictions can then be communicated to clinicians and patients to inform decision-making. We demonstrate the model with data from a cohort of low-risk prostate cancer patients at Johns Hopkins University and assess predictive accuracy among a subset for whom true cancer state is observed. Simulation studies confirm model performance and explore the impact of adjusting for informative missingness on true state predictions. R code is provided in an online supplement and at url{http://github.com/rycoley/prediction-prostate-surveillance}. Homepage: https://github.com/rycoley/prediction-prostate-surveillance Source Code: https://github.com/rycoley/prediction-prostate-surveillance Dependencies: R Keywords: latent class analysis; missing data; precision medicine; prostate cancer prognosis; risk classification Related Software: R; BayesDA; PMTK; Stan; Rcpp; rjags; KernSmooth; PRMLT; GitHub; R2jags; JAGS; POMDP Cited in: 4 Documents Standard Articles 1 Publication describing the Software, including 1 Publication in zbMATH Year A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostate cancer. Zbl 1372.62063Coley, Rebecca Yates; Fisher, Aaron J.; Mamawala, Mufaddal; Carter, Herbert Ballentine; Pienta, Kenneth J.; Zeger, Scott L. 2017 all top 5 Cited by 13 Authors 1 Carter, Herbert Ballentine 1 Coley, Rebecca Yates 1 Denton, Brian T. 1 Fisher, Aaron J. 1 Goudie, Robert J. B. 1 Li, Weiyu 1 Mamawala, Mufaddal 1 Manderson, Andrew A. 1 Morgan, Todd M. 1 Pienta, Kenneth J. 1 Saria, Suchi 1 Schulam, Peter 1 Zeger, Scott L. Cited in 4 Serials 1 Biometrics 1 European Journal of Operational Research 1 Journal of Machine Learning Research (JMLR) 1 Statistics and Computing Cited in 4 Fields 3 Statistics (62-XX) 2 Biology and other natural sciences (92-XX) 1 Computer science (68-XX) 1 Operations research, mathematical programming (90-XX) Citations by Year