FlexVarJM

Estimate Joint Models with subject-specific variance

https://github.com/leoniecourcoul/flexvarjm

Repository

Estimate Joint Models with subject-specific variance

README.Rmd

---
output: github_document
editor_options: 
  markdown: 
    wrap: 72
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# FlexVarJM



[![R-CMD-check](https://github.com/LeonieCourcoul/FlexVarJM/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/LeonieCourcoul/FlexVarJM/actions/workflows/R-CMD-check.yaml)


The goal of FlexVarJM is to estimate joint model with subject-specific
time-dependent variability.

The global function is 'lsjm'. It handles to estimate joint model with a
marker which has a subject-specific time-dependent variability and
competing events with the possibility to take into account the left
truncation. For more information you can read the corresponding article
: 

## Installation

You can install the development version of FlexVarJM from
[GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("LeonieCourcoul/FlexVarJM")
```

## Example

# Estimation

This is an example in a simulated dataset where \code{binary} is a
binary variable.

$$y_i(t_{ij}) = \color{blue}\tilde{y}_i(t_{ij}) \color{black} + \epsilon_{ij} = \beta_0 + b_{0i} + (\beta_1 + b_{1i})t_{ij} + \beta_2 * binary_i + \epsilon_{ij} $$

For the first risk, k = 1, we estimate the following risk function :

$$ \lambda_{i1}(t) = \lambda_{01}(t)\exp(\gamma_{11}*binary_i + \color{blue}\alpha_{11}\tilde{y}_i(t) + \color{red}\alpha_{\sigma 1} \sigma_i(t) \color{black}) $$
And for the second risk, k = 2 :
$$ \lambda_{i2}(t) = \lambda_{02}(t)\exp(\color{blue}\alpha_{21}\tilde{y_i}(t) + \color{blue}\alpha_{22}\tilde{y}'_i(t) + \color{red}\alpha_{\sigma 2} \sigma_i(t) \color{black}) $$

where :

-   $\epsilon_{i}(t_{ij}) \sim \mathcal{N}(0, \color{red}\sigma_i^2\color{black})$
    with
    $\color{red}\log(\sigma_i(t_{ij})) = \mu_0 + \tau_{0i} + (\mu_1 + \tau_{1i})\times t_{ij} + \mu_2 * binary_i$

-   with $b_i=\left(b_{0i},b_{1i}\right)^{\top}$ and
    $\tau_i=\left(\tau_{0i},\tau_{1i}\right)^{\top}$ assuming that the
    two sets of random effects $b_i$ and $\tau_i$ are not independent:
    $$(b_i, \tau_i)^\top \sim N(0, \Sigma)$$

-   $\lambda_{0k}(t) = \kappa_k^2 t^{\kappa_k^2-1}e^{\zeta_{0k}}$ :
    Weibull function

-   $\tilde{y}'_i(t)$ is the current slope of the marker $y$

``` r
example <- lsjm(formFixed = y~visit+binary,
                      formRandom = ~ visit,
                      formGroup = ~ID,
                      formSurv = Surv(time, event ==1 ) ~ binary,
                      timeVar = "visit",
                      data.long = Data_toy,
                      variability_hetero = TRUE,
                      formFixedVar =~visit+binary,
                      formRandomVar =~visit,
                      correlated_re = TRUE,
                      sharedtype = c("current value", "variability"),
                      hazard_baseline = "Weibull",
                      competing_risk = TRUE,
                      formSurv_CR = Surv(time, event ==2 ) ~ 1,
                      hazard_baseline_CR = "Weibull",
                      sharedtype_CR = c("slope", "variability"),
                      formSlopeFixed =~1,
                      formSlopeRandom = ~1,
                      indices_beta_slope = c(2), 
                      S1 = 500,
                      S2 = 1000,
                      nproc = 5,
                      Comp.Rcpp = TRUE
                      )
                      
summary(example)
```

You can access to the table of estimations and standard deviation with :

``` r
example$table.res
```

The computing time is given by :

``` r
example$time.compute
```

The output of the marqLevAlg algorithm is in :

``` r
example$result
```

Finally, some elements of control are in :

``` r
example$control
```

# Goodness-of-fit

You can check the goodness-of-fit of the longitudinal submodel and of
the survival submodel by computing the predicted random effects :

``` r
goodness <- goodness_of_fit(example, graph = T)
```

# Predictions

You can compute the probability for (new) individual(s) to have event 1
or 2 between time s and time s+t years given that he did not experience
any event before time s, its trajectory of marker until time s ans the
set of estimated parameters. To have a 'IC%' confidence interval, the
predictions are computed 'nb.draws' time and the percentiles of the
predictions are computed. For example, for individuals 1 and 3 to
experiment the event 1 at time 1.5, 2, and 3, given their measurements until time 1 :

``` r
newdata <- Data_toy[which(Data_toy$ID %in% c(1,3)),]
predyn(newdata,example,1, c(1.5,2,3), event = 1, IC = 95, nb.draws = 500, graph = TRUE)
```

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