Covariates"
In campsis: Generic PK/PD Simulation Platform Campsis

if (campsis::on_cran()) {
  cat(
    "This vignette was not built on CRAN. Please check out the online version [here](https://calvagone.github.io/campsis.doc/articles/v03_covariates.html)."
  )
  knitr::knit_exit()
}

library(campsis)

Add a body weight covariate into the model

Let's use a simple 1-compartment model to illustrate how covariates are managed by Campsis.

model <- model_suite$nonmem$advan1_trans2

For this example, we're going to add allometric scaling on the clearance parameter.

model <- model %>%
  replace(Equation("CL", "THETA_CL*exp(ETA_CL)*pow(BW/70, 0.75)"))
model

We will infuse 1000 mg with a rate of 200 mg/hour into the central compartment and observe for a day. The corresponding dataset is as follows:

dataset <- Dataset() %>%
  add(Infusion(time = 0, amount = 1000, rate = 200)) %>%
  add(Observations(times = seq(0, 24, by = 0.5)))

To visualize clearly the effect of the covariates, we will disable the inter-individual variability on the model.

model <- model %>%
  disable("IIV")

Constant body weight

Let's define a constant covariate into the dataset. This is done as follows.

ds <- dataset %>%
  set_subjects(5) %>%
  add(Covariate("BW", 70))

All simulated subjects will be exactly the same, as IIV was removed.

results <- simulate(model = model, dataset = ds)
spaghetti_plot(results, "CONC")

Fix body weight values (1/subject)

Let's now define 1 body weight per subject. This is done as follows.

ds <- dataset %>%
  set_subjects(5) %>%
  add(Covariate("BW", c(50, 60, 70, 80, 90)))

Simulated subjects should now be different.

results <- simulate(model = model, dataset = ds)
spaghetti_plot(results, "CONC")

Uniform distribution

Let's say now that the body weight is a uniform distribution. This can be implemented as follows:

ds <- dataset %>%
  set_subjects(40) %>%
  add(Covariate("BW", UniformDistribution(min = 50, max = 90)))

Simulated weights will then be sampled from a uniform distribution with a min value of 50 and a max value of 90.

results <- simulate(
  model = model,
  dataset = ds,
  outvars = c("CONC", "BW"),
  seed = 1
)
gridExtra::grid.arrange(
  spaghetti_plot(results, "BW"),
  spaghetti_plot(results, "CONC"),
  nrow = 1
)

Normal distribution

Let's say now that the body weight is a normal distribution. This can be implemented as follows:

ds <- dataset %>%
  set_subjects(40) %>%
  add(Covariate("BW", NormalDistribution(mean = 70, sd = 10)))

Simulated weights will then be sampled from a normal distribution with a mean of 70 and a standard deviation of 10.

results <- simulate(
  model = model,
  dataset = ds,
  outvars = c("CONC", "BW"),
  seed = 1
)
gridExtra::grid.arrange(
  spaghetti_plot(results, "BW"),
  spaghetti_plot(results, "CONC"),
  nrow = 1
)

Log-normal distribution

Say now that the body weight is a log-normal distribution. This can be implemented as follows:

ds <- dataset %>%
  set_subjects(40) %>%
  add(Covariate("BW", LogNormalDistribution(meanlog = log(70), sdlog = 0.2)))

Simulated weights will then be sampled from a log-normal distribution with a median of 70 and a coefficient of variation of 20%.

results <- simulate(
  model = model,
  dataset = ds,
  outvars = c("CONC", "BW"),
  seed = 1
)
gridExtra::grid.arrange(
  spaghetti_plot(results, "BW"),
  spaghetti_plot(results, "CONC"),
  nrow = 1
)

Bootstrap

Body weight can also be bootstrapped from a real dataset. Let's create a fictive one:

bootstrap <- data.frame(ID = c(1, 2, 3, 4, 5), BW = c(89, 54, 60, 75, 77))

ds <- dataset %>%
  set_subjects(10) %>%
  add(Covariate(
    "BW",
    BootstrapDistribution(
      data = bootstrap$BW,
      replacement = TRUE,
      random = TRUE
    )
  ))

Simulated weights will then be sampled from a log-normal distribution with a median of 70 and a coefficient of variation of 20%.

results <- simulate(
  model = model,
  dataset = ds,
  outvars = c("CONC", "BW"),
  seed = 2
)
gridExtra::grid.arrange(
  spaghetti_plot(results, "BW"),
  spaghetti_plot(results, "CONC"),
  nrow = 1
)