joineR: joineR

Description Data manipulation functions Plot functions Model fitting functions Note References


The joineR package implements methods for analyzing data from longitudinal studies in which the response from each subject consists of a time-sequence of repeated measurements and a possibly censored time-to-event outcome. The modelling framework for the repeated measurements is the linear model with random effects and/or correlated error structure (Laird and Ware, 1982). The model for the time-to-event outcome is a: Cox proportional hazards model with log-Gaussian frailty (Cox, 1972). A cause-specific hazards model is used when competing risks are present. Stochastic dependence is captured by allowing the Gaussian random effects of the linear model to be correlated with the frailty term of the Cox proportional hazards model. The methodology used to fit the model is described in Henderson et al. (2002) in the case of a single event time, and by Williamson et al. (2008) in the case of competing risks data. Both models exploit the general methodology proposed by Wulfsohn and Tsiatis (1997).

The package offers several types of functions for the analysis of joint data.

Data manipulation functions

There are several functions, including jointdata, sample.jointdata, subset.jointdata, to.balanced, to.unbalanced, and UniqueVariables, which offer the ability to construct a joint model dataset and manipulate it, e.g. take a sample according to a baseline covariate or outcome.

Plot functions

The plot function can be applied to jointdata and vargm (variogram) objects. In addition, points and lines can also be used with jointplot objects.

Model fitting functions

The primary function for fitting a joint model is joint. Standard errors can be estimated using jointSE.


Further details on the package are given in the vignette. To access this, run vignette("joineR").


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.

Williamson PR, Kolamunnage-Dona R, Philipson P, Marson AG. Joint modelling of longitudinal and competing risks data. Stat Med. 2008; 27: 6426-6438.

graemeleehickey/joineR documentation built on May 17, 2019, 8:02 a.m.