Simulate from the non-longitudinal hierarchical model.

```
hb_sim_hierarchical(
n_study = 5,
n_group = 3,
n_patient = 100,
n_continuous = 0,
n_binary = 0,
s_delta = 1,
s_beta = 1,
s_sigma = 1,
s_mu = 1,
s_tau = 1,
alpha = stats::rnorm(n = n_study, mean = mu, sd = tau),
delta = stats::rnorm(n = n_group - 1, mean = 0, sd = s_delta),
beta = stats::rnorm(n = n_study * (n_continuous + n_binary), mean = 0, sd = s_delta),
sigma = stats::runif(n = n_study, min = 0, max = s_sigma),
mu = stats::rnorm(n = 1, mean = 0, sd = s_mu),
tau = stats::runif(n = 1, min = 0, max = s_tau)
)
```

- n_study
Number of studies to simulate.

- n_group
Number of groups (e.g. study arms) to simulate per study.

- n_patient
Number of patients to simulate per study per group.

- n_continuous
Number of continuous covariates to simulate (all from independent standard normal distributions).

- n_binary
Number of binary covariates to simulate (all from independent Bernoulli distributions with p = 0.5).

- s_delta
Numeric of length 1, prior standard deviation of the study-by-group effect parameters

`delta`

.- s_beta
Numeric of length 1, prior standard deviation of the fixed effects

`beta`

.- s_sigma
Numeric of length 1, prior upper bound of the residual standard deviations.

- s_mu
Numeric of length 1, prior standard deviation of

`mu`

.- s_tau
Numeric of length 1, Upper bound on

`tau`

.- alpha
Numeric vector of length 1 for the pooled and mixture models and length

`n_study`

for the independent and hierarchical models.`alpha`

is the vector of control group mean parameters.`alpha`

enters the model by multiplying with`$matrices$x_alpha`

(see the return value). The control group in the data is the one with the`group`

column equal to 1.- delta
Numeric vector of length

`n_group - 1`

of treatment effect parameters.`delta`

enters the model by multiplying with`$matrices$x_delta`

(see the return value). The control (non-treatment) group in the data is the one with the`group`

column equal to 1.- beta
Numeric vector of

`n_study * (n_continuous + n_binary)`

fixed effect parameters. Within each study, the first`n_continuous`

betas are for the continuous covariates, and the rest are for the binary covariates. All the`beta`

s for one study appear before all the`beta`

s for the next study, and studies are arranged in increasing order of the sorted unique values in`$data$study`

in the output.`betas`

enters the model by multiplying with`$matrices$x_alpha`

(see the return value).- sigma
Numeric vector of

`n_study`

study-specific residual standard deviations.- mu
Numeric of length 1, mean of the control group means

`alpha`

.- tau
Numeric of length 1, standard deviation of the control group means

`alpha`

.

A list with the following elements:

`data`

: tidy long-form dataset with the patient-level data. one row per patient and indicator columns for the study, group (e.g. treatment arm), and patient ID. The`response`

columns is the patient response. The other columns are baseline covariates. The control group is the one with the`group`

column equal to 1, and the current study (non-historical) is the one with the maximum value of the`study`

column. Only the current study has any non-control-group patients, the historical studies have only the control group.`parameters`

: named list of model parameter values. See the model specification vignette for details.`matrices`

: A named list of model matrices. See the model specification vignette for details.

Other simulate:
`hb_sim_independent()`

,
`hb_sim_mixture()`

,
`hb_sim_pool()`

```
hb_sim_hierarchical()$data
#> # A tibble: 700 × 4
#> study group patient response
#> <int> <int> <int> <dbl>
#> 1 1 1 1 1.36
#> 2 1 1 2 0.737
#> 3 1 1 3 1.64
#> 4 1 1 4 1.04
#> 5 1 1 5 1.40
#> 6 1 1 6 1.48
#> 7 1 1 7 0.861
#> 8 1 1 8 2.30
#> 9 1 1 9 0.576
#> 10 1 1 10 0.534
#> # … with 690 more rows
```