joineRML: joineRML

In joineRML: Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes

joineRML

Description

joineRML is an extension of the joineR package for fitting joint models of time-to-event data and multivariate longitudinal data. The model fitted in joineRML is an extension of the Wulfsohn and Tsiatis (1997) and Henderson et al. (2000) models, which is comprised on (K+1)-sub-models: a Cox proportional hazards regression model (Cox, 1972) and a K-variate linear mixed-effects model - a direct extension of the Laird and Ware (1982) regression model. The model is fitted using a Monte Carlo Expectation-Maximization (MCEM) algorithm, which closely follows the methodology presented by Lin et al. (2002).

References

Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics. 1997; 53(1): 330-339.

Henderson R, Diggle PJ, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics. 2000; 1(4): 465-480.

Cox DR. Regression models and life-tables. J R Stat Soc Ser B Stat Methodol. 1972; 34(2): 187-220.

Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982; 38(4): 963-974.

joineRML documentation built on Jan. 22, 2023, 1:18 a.m.