Review and Comparison of Computational Approaches for Joint Longitudinal and Time-to-Event Models.

ABSTRACT: Joint models for longitudinal and time-to-event data are useful in situations where an association exists between a longitudinal marker and an event time. These models are typically complicated due to the presence of shared random effects and multiple submodels. As a consequence, software implementation is warranted that is not prohibitively time consuming. While methodological research in this area continues, several statistical software procedures exist to assist in the fitting of some joint models. We review the available implementation for frequentist and Bayesian models in the statistical programming languages R, SAS, and Stata. A description of each procedure is given including estimation techniques, input and data requirements, available options for customization, and some available

SUBMITTER: Furgal AKC 

PROVIDER: S-EPMC7009936 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature