Nonlinear joint models for individual dynamic prediction of risk of death using Hamiltonian Monte Carlo: application to metastatic prostate cancer.

ABSTRACT:

Background

Joint models of longitudinal and time-to-event data are increasingly used to perform individual dynamic prediction of a risk of event. However the difficulty to perform inference in nonlinear models and to calculate the distribution of individual parameters has long limited this approach to linear mixed-effect models for the longitudinal part. Here we use a Bayesian algorithm and a nonlinear joint model to calculate individual dynamic predictions. We apply this approach to predict the risk of death in metastatic castration-resistant prostate cancer (mCRPC) patients with frequent Prostate-Specific Antigen (PSA) measurements.

Methods

A joint model is built using a large population of 400 mCRPC patients where PSA kinetics is described by a biexponential function and

SUBMITTER: Desmee S 

PROVIDER: S-EPMC5513366 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature