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)
)Number of studies to simulate.
Number of groups (e.g. study arms) to simulate per study.
Number of patients to simulate per study per group.
Number of continuous covariates to simulate (all from independent standard normal distributions).
Number of binary covariates to simulate (all from independent Bernoulli distributions with p = 0.5).
Numeric of length 1, prior standard deviation
of the study-by-group effect parameters delta.
Numeric of length 1, prior standard deviation
of the fixed effects beta.
Numeric of length 1, prior upper bound of the residual standard deviations.
Numeric of length 1,
prior standard deviation of mu.
Numeric of length 1,
Upper bound on tau.
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.
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.
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 betas for one study
appear before all the betas 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).
Numeric vector of n_study study-specific
residual standard deviations.
Numeric of length 1,
mean of the control group means alpha.
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