Simulate from the non-longitudinal independent model.
hb_sim_independent(
n_study = 5,
n_group = 3,
n_patient = 100,
n_continuous = 0,
n_binary = 0,
s_alpha = 1,
s_delta = 1,
s_beta = 1,
s_sigma = 1,
alpha = stats::rnorm(n = n_study, mean = 0, sd = s_alpha),
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)
)
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-specific control group mean parameters alpha
.
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 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 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).
Numeric vector of n_study
study-specific
residual standard deviations.
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_hierarchical()
,
hb_sim_mixture()
,
hb_sim_pool()
hb_sim_independent()$data
#> # A tibble: 700 × 4
#> study group patient response
#> <int> <int> <int> <dbl>
#> 1 1 1 1 0.810
#> 2 1 1 2 1.11
#> 3 1 1 3 1.07
#> 4 1 1 4 0.969
#> 5 1 1 5 0.842
#> 6 1 1 6 0.993
#> 7 1 1 7 0.689
#> 8 1 1 8 1.02
#> 9 1 1 9 0.685
#> 10 1 1 10 0.577
#> # … with 690 more rows