Covariates are implemented using the `new_covariate()`

function, wrapped in a named list. For example:

```
<- list(
covariates "WT" = new_covariate(value = 70),
"SCR" = new_covariate(value = 120)
)
```

The names in the covariate list-object should correspond
**exactly** with the names of the covariates in the
model.

Time-varing covariates, such as creatinine values can be implemented
easily as well. They just require the additional `times`

argument:

```
<- list(
covariates "WT" = new_covariate(value = 70),
"CR" = new_covariate(
value = c(0.8, 1, 1.2),
times = c(0, 48, 72)
) )
```

By default, `PKPDsim`

assumes that you want to interpolate
(linearly) between measurements of the time-varying covariates. If you
prefer to implement the covariate using
*last-observation-carried-forward* (in other words a step
function), specify the `method = "LOCF"`

argument to
`new_covariate()`

.

A table of covariates can be supplied to `sim()`

with
covariate values per individual. It can handle both static and
time-varying covariates. A covariate table could look like this:

```
<- data.frame(
cov_table id = c(1, 1, 2, 3),
WT = c(40, 45, 50, 60),
SCR = c(50, 150, 90, 110),
t = c(0, 5, 0, 0)
)
```

The `id`

and `t`

(time) columns can be omitted
when only static covariates are to be used. Again, make sure that the
headers used for the covariates match *exactly* with the
covariate names specified in the model definition.

A full example for a model with (simulated) covariates for a patient population:

```
<- list(
parameters CL = 1,
V = 10,
KA = 0.5
)<- 50
n_ind <- data.frame(
cov_table 'id' = 1:n_ind,
'WT' = rnorm(n_ind, mean = 70, sd = 5)
)
<- new_ode_model(
model code = '
CLi = CL * pow((WT/70), 0.75)
Vi = V * (WT/70)
dAdt[1] = -KA*A[1]
dAdt[2] = KA*A[1] -(CLi/Vi)*A[2]
',
declare_variables = c('CLi', 'Vi'),
covariates = c('WT'),
dose = list(cmt = 1),
obs = list(cmt = 2, scale = 'V * (WT/70)')
)
<- new_regimen(
regimen amt = 30,
n = 4,
type = 'bolus',
interval = 12
)
<- sim(
dat ode = model,
parameters = parameters,
t_obs = c(0.5, 2, 4, 8, 12, 16, 24),
n_ind = n_ind,
regimen = regimen,
covariates_table = cov_table,
output_include = list(covariates = TRUE)
)
```

`## Simulating 50 individuals.`

*Note: PKPDsim does not handle missing covariate values. If you do
have missing covariate data, probably the best approach would be to
impute the values manually before simulation, e.g. based on the mean
observed / known value, or the correlation between the
covariates.*