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In this article, we present a Bayesian hierarchical model for predicting alatent health state from longitudinal clinical measurements. Model developmentis motivated by the need to integrate multiple sources of data to improveclinical decisions about whether to remove or irradiate a patient's prostatecancer. Existing modeling approaches are extended to accommodate measurementerror in cancer state determinations based on biopsied tissue, clinicalmeasurements possibly not missing at random, and informative partialobservation of the true state. The proposed model enables estimation of whetheran individual's underlying prostate cancer is aggressive, requiring surgeryand/or radiation, or indolent, permitting continued surveillance. Theseindividualized predictions can then be communicated to clinicians and patientsto inform decision-making. We demonstrate the model with data from a cohort oflow risk prostate cancer patients at Johns Hopkins University and assesspredictive accuracy among a subset for whom true cancer state is observed.Simulation studies confirm model performance and explore the impact ofadjusting for informative missingness on true state predictions. R code andsimulated data available athttps://github.com/rycoley/prediction-prostate-surveillance.

A Bayesian Hierarchical Model for Prediction of Latent Health States  from Multiple Data Sources with Application to Active Surveillance of  Prostate Cancer