---
title: "Time-varying covariates"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Time-varying covariates}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

```{r, results='hide', echo=F, message=F, warning=F}
library(campsis)
```

This vignette shows how time-varying covariates can be implemented.

### Body weight as a time varying covariate

As a demonstration example, let's implement allometric scaling on the clearance and volume of a 1-compartment PK model:

```{r}
model <- model_suite$nonmem$advan2_trans2
model <- model %>% replace(Equation("CL", "THETA_CL*exp(ETA_CL)*pow(BW/70, 0.75)"))
model <- model %>% replace(Equation("V", "THETA_V*exp(ETA_V)*BW/70"))
```

Assume our drug is given once a month and `BW` varies over time. A time-varying covariate can be added to the dataset as follows:

```{r}
dataset <- Dataset(1) %>%
  add(Bolus(time=0, amount=1000)) %>%
  add(Observations(times=seq(0,30,by=0.1))) %>%
  add(TimeVaryingCovariate("BW", data.frame(TIME=c(0,7,14,21), VALUE=c(80,75,70,65))))
```

The typical profile can be simulated in the following way:

```{r event_bw_as_time_varying_covariate, fig.align='center', fig.height=4, fig.width=8, message=F}
results <- model %>% disable("IIV") %>% simulate(dataset, seed=1, outvars="BW")
gridExtra::grid.arrange(spaghettiPlot(results, "CONC"),
                        spaghettiPlot(results, "BW"), ncol=1)
```

Let's now compare this profile with another typical individual having a constant body weight of 70 kg:

```{r event_bw_as_time_varying_covariate_scenarios, fig.align='center', fig.height=4, fig.width=8, message=F}
scenarios <- Scenarios() %>%
  add(Scenario("BW as time-varying covarite")) %>%
  add(Scenario("Fixed BW", dataset=~.x %>% replace(Covariate("BW", 70))))

results <- model %>% disable("IIV") %>% simulate(dataset, seed=1, outvars="BW", scenarios=scenarios)
gridExtra::grid.arrange(spaghettiPlot(results, "CONC", "SCENARIO"),
                        spaghettiPlot(results, "BW", "SCENARIO"), ncol=1)
```

Finally, time-varying covariates can also be individualized by specifying an ID column:

```{r event_bw_as_time_varying_covariate_by_id, fig.align='center', fig.height=4, fig.width=8, message=F}
dataset <- Dataset(2) %>%
  add(Bolus(time=0, amount=1000)) %>%
  add(Observations(times=seq(0,30,by=0.1))) %>%
  add(TimeVaryingCovariate("BW", data.frame(ID=c(rep(1, 4), rep(2, 4)),
                                            TIME=c(0,7,14,21, 0,7,14,21),
                                            VALUE=c(80,75,70,65, 65,70,75,80))))
results <- model %>% disable("IIV") %>% simulate(dataset, seed=1, outvars="BW")
gridExtra::grid.arrange(spaghettiPlot(results, "CONC", "ID"),
                        spaghettiPlot(results, "BW", "ID"), ncol=1)
```