jmpost 
The goal of the `jmpost` package is to fit joint models involving:
1. a parametric time-to-event sub-model,
2. a nonlinear (or linear) mixed-effect sub-model, describing
individual time profiles (*i.e.* trajectories) for a continuous
marker,
3. a link function (*a.k.a.* association term).
More specifically, the model implemented in this package utilizes a
modelling framework described previously **\[1-3\]** to link overall
survival to tumour size data in oncology clinical trials.
**\[1\]** [Tardivon *et al.* Association between tumour size kinetics
and survival in patients with urothelial carcinoma treated with
atezolizumab: Implications for patient follow-up. *Clin Pharm Ther*,
2019](https://doi.org/10.1002/cpt.1450).
**\[2\]** [Kerioui *et al.* Bayesian inference using Hamiltonian
Monte-Carlo algorithm for nonlinear joint modelling in the context of
cancer immunotherapy. *Stat in Med*,
2020](https://doi.org/10.1002/sim.8756).
**\[3\]** [Kerioui *et al.* Modelling the association between biomarkers
and clinical outcome: An introduction to nonlinear joint models. *Br J
Clin Pharm*, 2022](https://doi.org/10.1111/bcp.15200).
The models are implemented in [STAN](https://mc-stan.org/), and the
package provides a flexible user interface. Please reach out to us via
issues or email (see the `DESCRIPTION` file) if you have comments or
questions or would like to get involved in the ongoing development,
thank you!
## Installation
**GitHub**
You can install the current development version from GitHub with:
``` r
if (!require("remotes")) {
install.packages("remotes")
}
remotes::install_github("genentech/jmpost")
```
Please note that this package requires
[`cmdstanr`](https://mc-stan.org/cmdstanr/).
**CRAN**
This package has not been published to CRAN yet.
## Getting Started
See also the [model
fitting](https://genentech.github.io/jmpost/main/articles/model_fitting.html)
vignette for more details. Here we present a very basic example here.
First we simulate a data set. In practice you want to follow a similar
structure of the input data and use `DataJoint()` to bring it into the
right format.
``` r
library(jmpost)
#> Registered S3 methods overwritten by 'ggpp':
#> method from
#> heightDetails.titleGrob ggplot2
#> widthDetails.titleGrob ggplot2
set.seed(321)
sim_data <- SimJointData(
design = list(
SimGroup(50, "Arm-A", "Study-X"),
SimGroup(50, "Arm-B", "Study-X")
),
longitudinal = SimLongitudinalRandomSlope(
times = c(1, 50, 100, 150, 200, 250, 300),
),
survival = SimSurvivalWeibullPH(
lambda = 1 / 300,
gamma = 0.97
)
)
#> INFO: 1 subject did not die before max(times)
joint_data <- DataJoint(
subject = DataSubject(
data = sim_data@survival,
subject = "subject",
arm = "arm",
study = "study"
),
survival = DataSurvival(
data = sim_data@survival,
formula = Surv(time, event) ~ cov_cat + cov_cont
),
longitudinal = DataLongitudinal(
data = sim_data@longitudinal,
formula = sld ~ time,
threshold = 5
)
)
```
Then we specify the joint model, here we use a Generalized Stein-Fojo
model for the longitudinal part, and a Weibull proportional hazards
model for the survival part. The longitudinal model impacts the hazard
via a term for the derivative and another term for the time-to-growth.
``` r
joint_model <- JointModel(
longitudinal = LongitudinalGSF(),
survival = SurvivalWeibullPH(),
link = Link(
linkDSLD(),
linkTTG()
)
)
```
Finally we can sample the parameters via MCMC from the underlying Stan
model. Note that in a real application you will choose more warm up and
sampling iterations.
``` r
mcmc_results <- sampleStanModel(
joint_model,
data = joint_data,
iter_sampling = 100,
iter_warmup = 100,
chains = 1,
parallel_chains = 1
)
```
## Citing `jmpost`
To cite `jmpost` please see
[here](https://genentech.github.io/jmpost/main/authors.html#citation).