Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM
| Autor: | Jane-Ling Wang, Cong Xu, Pantelis Z. Hadjipantelis |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
Computer science Proportional hazards model Statistics b-splines em algorithm Statistical model Random effects model 01 natural sciences B-splines EM algorithm multiplicative random effects semi-parametric models transformation model Semiparametric model HA1-4737 010104 statistics & probability Standard error Component (UML) Expectation–maximization algorithm Covariate Econometrics 0101 mathematics Statistics Probability and Uncertainty Software |
| Zdroj: | Journal of Statistical Software, Vol 93, Iss 1, Pp 1-29 (2020) Journal of Statistical Software; Vol 93 (2020); 1-29 |
| ISSN: | 1548-7660 |
| Popis: | This paper is devoted to the R package JSM which performs joint statistical modeling of survival and longitudinal data. In biomedical studies it has been increasingly common to collect both baseline and longitudinal covariates along with a possibly censored survival time. Instead of analyzing the survival and longitudinal outcomes separately, joint modeling approaches have attracted substantive attention in the recent literature and have been shown to correct biases from separate modeling approaches and enhance information. Most existing approaches adopt a linear mixed effects model for the longitudinal component and the Cox proportional hazards model for the survival component. We extend the Cox model to a more general class of transformation models for the survival process, where the baseline hazard function is completely unspecified leading to semiparametric survival models. We also offer a non-parametric multiplicative random effects model for the longitudinal process in JSM in addition to the linear mixed effects model. In this paper, we present the joint modeling framework that is implemented in JSM, as well as the standard error estimation methods, and illustrate the package with two real data examples: a liver cirrhosis data and a Mayo Clinic primary biliary cirrhosis data. |
| Databáze: | OpenAIRE |
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