https://github.com/jamesmurray7/gmvjoint
Generalised joint models of survival and multivariate longitudinal data
https://github.com/jamesmurray7/gmvjoint
glmm joint-models longitudinal mixed-models model prediction survival survival-analysis
Last synced: 12 days ago
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Generalised joint models of survival and multivariate longitudinal data
- Host: GitHub
- URL: https://github.com/jamesmurray7/gmvjoint
- Owner: jamesmurray7
- Created: 2022-11-21T17:22:08.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-10-06T10:55:09.000Z (over 1 year ago)
- Last Synced: 2025-12-09T09:26:44.537Z (3 months ago)
- Topics: glmm, joint-models, longitudinal, mixed-models, model, prediction, survival, survival-analysis
- Language: R
- Homepage:
- Size: 26.8 MB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# `gmvjoint`
## What is `gmvjoint`?
* "**g**eneralised"
* **m**ulti**v**ariate
* **Joint** models.
`gmvjoint` allows the user to fit joint models of survival and multivariate longitudinal data, where the
longitudinal sub-models are specified by generalised linear mixed models (GLMMs). The joint models
are fit via maximum likelihood using an approximate EM algorithm first proposed by Bernhardt *et
al*. (2015). The GLMMs are specified using the same syntax as for package `glmmTMB` (Brooks *et
al*., 2017). The joint models themselves are then the flexible extensions to those in e.g.
Wulfsohn and Tsiatis (1997). The user is able to simulate data under many different response
types.
Currently, six families can be fit: Gaussian; Poisson; binomial; Gamma; negative binomial; and generalised Poisson.
## Installation
You can install the latest 'official' release from CRAN in the usual way:
```r
install.packages('gmvjoint')
```
or the latest development version using `devtools`:
```r
devtools::install_github('jamesmurray7/gmvjoint')
```
MacOS users may be interested in [swapping their BLAS library](https://cran.r-project.org/bin/macosx/RMacOSX-FAQ.html#Which-BLAS-is-used-and-how-can-it-be-changed_003f) to one which provides an optimal BLAS implementation for Mac hardware (`vecLib`).
## Example
To fit a joint model, we first need to specify the longitudinal and survival sub-models.
The longitudinal sub-model **must** be a list which contains the specification of the longitudinal process along with its random effects structure
in the same syntax as a [glmmTMB](https://cran.r-project.org/package=glmmTMB) model (which itself is the same as the widely-used `lme4`).
As an example, suppose we want to fit a trivariate model on the oft-used PBC data, with a linear time-drug interaction term on albumin, a spline term on
(logged) serum bilirubin and a linear fit on spiders, we specify
```r
data(PBC)
PBC <- subset(PBC, select = c('id', 'survtime', 'status', 'drug', 'time',
'serBilir', 'albumin', 'spiders'))
PBC <- na.omit(PBC)
long.formulas <- list(
albumin ~ drug * time + (1 + time|id),
log(serBilir) ~ drug * splines::ns(time, 3) + (1 + splines::ns(time, 3)|id),
spiders ~ drug * time + (1|id)
)
```
where we note interactions and spline-time fits are possible.
The survival sub-model must be set-up using `Surv()` from the [survival](https://cran.r-project.org/package=survival) package e.g.
```r
surv.formula <- Surv(survtime, status) ~ drug
```
Currently interaction terms in the survival sub-model specification are unsupported.
Now we can do the joint model call through the main workhorse function `joint`. This notably take a *list* of family arguments which **must** match-up in the desired order as the longitudinal process
list. We then fit our joint model via
```r
fit <- joint(long.formulas = long.formulas, surv.formula = surv.formula, data = PBC,
family = list("gaussian", "gaussian", "binomial"))
summary(fit)
```
where extra control arguments are documented in `?joint`. For certain families, we could additionally supply `disp.formulas` which specify the dispersion model for the corresponding longitudinal process. Numerous S3 methods exist for the class of object `joint` creates: `summary()`, `logLik()`, `fixef()`, `ranef()`, `fitted()`, `resid()`, and `vcov()`. LaTeX-ready tables can also be generated by S3 method `xtable()`. Data can be simulated under a host of different parameter set-ups using the `simData()` function.
We bridge from a set of joint model parameter estimates to a prognostic one by dynamic predictions `dynPred`. We can assess discriminatory capabilities of the `joint()` model fit by the `ROC` function, too.
## To-do list
Currently the largest limitation exists with the relatively strict data structure necessary and the corresponding calls to the `joint` function. The below lists these (known) limitations and plans for relaxing.
* Longitudinal information: The longitudinal time argument **must** be named `time` and the subject identifier (which we 'split' random effects by) `id`. Unsure if I will ever change these; I think a little more user pre-processing is no bad thing, when alternative would be a more crowded call to `joint`, which I wouldn't be a fan of.
* Misc.: data must be balanced (i.e. no `NA` values); this will be fixed in a future update. For now I don't think this is the biggest issue, and recommend using `na.omit` for example. Additionally, the id variable __must__ increment by no more than one. That is, `data$id=1,1,1,2,2,2,3,3,3` is fine, but `data$id=1,1,1,1,3,3,3,4,4` is not. This is due to how data matrices are created internally and will be fixed in the future.
Update June 2023: Since calls and data are passed straight to `glmmTB`, possible to assign these unique indices `1:n`, nothing currently done with this, though.
* Pretty-ing of progress bars using cli package (maybe...).
Note I'm a PhD student, and the S3 methods (and some functions themselves) have largely arisen out of things I needed, or thought would be a good idea at some point!
## References
Bernhardt PW, Zhang D and Wang HJ. A fast EM Algorithm for Fitting Joint Models of a Binary
Response to Multiple Longitudinal Covariates Subject to Detection Limits.
*Computational Statistics and Data Analysis* 2015; **85**; 37--53
Mollie E. Brooks, Kasper Kristensen, Koen J. van Benthem, Arni Magnusson, Casper W. Berg, Anders
Nielsen, Hans J. Skaug, Martin Maechler and Benjamin M. Bolker (2017). glmmTMB Balances Speed and
Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling.
*The R Journal*, **9(2)**, 378-400.
Murray, J and Philipson P. A fast approximate EM algorithm for joint models of survival and
multivariate longitudinal data. *Computational Statistics and Data Analysis* 2022
Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data
measured with error. *Biometrics.* 1997; **53(1)**, 330-339.