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 |

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.

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