joineRfits: Study specific joint model fits using the joineR package
In mesudell/joineRmeta: Joint Modelling for Meta-Analytic (Multi-Study) Data

Description Usage Format Details See Also

A dataset containing a list of the model fits for joint models fitted to the data for each study in the simdat2 dataset using the joineR package. Further details of model fits supplied below.

1	joineRfits

A list of 6 objects:

joineRfit1: an object of class joint, the result of using the joint function to fit a joint model to the data from the first study in the simdat2 dataset.
joineRfit1SE: an object of class data.frame, the result of applying the function jointSE to the joint model fit joineRfit1.
joineRfit2: an object of class joint, the result of using the joint function to fit a joint model to the data from the second study in the simdat2 dataset.
joineRfit2SE: an object of class data.frame, the result of applying the function jointSE to the joint model fit joineRfit2.
joineRfit3: an object of class joint, the result of using the joint function to fit a joint model to the data from the third study in the simdat2 dataset.
joineRfit3SE: an object of class data.frame, the result of applying the function jointSE to the joint model fit joineRfit3.

These are the results of fitting a joint model using the joineR package separately to the data from the first three studies present in the simdat2 dataset. This data has three levels, namely the longitudinal measurements at level 1, nested within individuals (level 2) who are themselves nested within studies (level 3). The joint models fitted to each study's data had the same format. The longitudinal sub-model contained a fixed intercept, time and treatment assignment term, and random intercept and slope. The survival sub-model contained a fixed treatment assignment term. A proportional association structure was used to link the sub-models. More formally, the longitudinal sub-model had the following format:

Y_{kij} = β_{10} + β_{11}time + β_{12}treat + b^{(2)}_{0ki} + b^{(2)}_{1ki}time + ε_{kij}

Where Y represents the continuous longitudinal outcome, fixed effect coefficients are represented by β, random effects coefficients by b and the measurement error by ε. For the random effects the superscript of 2 indicates that these are individual level, or level 2 random effects. This means they take can take a unique value for each individual in the dataset. The longitudinal time variable is represented by time, and the treatment assignment variable (a binary factor) is represented by treat.

The survival sub-model had format:

λ_{ki}(t) = λ_{0}(t)exp(β_{21}treat + α(b^{(2)}_{0ki} + b^{(2)}_{1ki}time))

In the above equation, λ_{ki}(t) represents the survival time of the individual i in study k, and λ_{0}(t) represents the baseline hazard. The fixed effect coefficient is represented by β_{21}. The association parameter quantifying the link between the sub-models is represented by α. Again treat represents the binary factor treatment assignment variable, and b^{(2)}_{0ki} and b^{(2)}_{1ki} are the zero mean random effects shared from the longitudinal sub-model.

We differentiate between the fixed effect coefficients in the longitudinal and the survival sub-models by varying the first number present in the subscript of the fixed effect, which takes a 1 for coefficients from the longitudinal sub-model and a 2 for coefficients from the survival sub-model.

These fits have been provided in this package for use with the package vignette, see the vignette for more information.

jointdata, joint, jointSE

mesudell/joineRmeta documentation built on Jan. 24, 2020, 6:06 p.m.