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Non-longitudinal hierarchical simulations.

Source: R/hb_sim_hierarchical.R
hb_sim_hierarchical.Rd

Simulate from the non-longitudinal hierarchical model.

Usage

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,
  d_tau = 4,
  prior_tau = "half_t",
  alpha = NULL,
  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 = NULL
)

Arguments

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

Non-negative numeric of length 1. If prior_tau is "half_t", then s_tau is the scale parameter of the Student t prior of tau and analogous to the sigma parameter of the Student-t parameterization given at https://mc-stan.org/docs/functions-reference/unbounded_continuous_distributions.html. # nolint If prior_tau is "uniform", then s_tau is the upper bound of tau. Upper bound on tau if prior_tau is "uniform".

d_tau

Positive numeric of length 1. Degrees of freedom of the Student t prior of tau if prior_tau is "half_t".

prior_tau

Character string, family of the prior of tau. If prior_tau equals "uniform", then the prior on tau is a uniform prior with lower bound 0 and upper bound s_tau. If prior_tau equals "half_t", then the prior on tau is a half Student-t prior with center 0, lower bound 0, scale parameter s_tau, and degrees of freedom d_tau. The scale parameter s_tau is analogous to the sigma parameter of the Student-t parameterization given at https://mc-stan.org/docs/functions-reference/unbounded_continuous_distributions.html. # nolint

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 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).

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.

Value

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.

See also

Other simulate: hb_sim_independent(), hb_sim_mixture(), hb_sim_pool()

Examples

hb_sim_hierarchical()$data
#> # A tibble: 700 × 4
#>    study group patient response
#>    <int> <int>   <int>    <dbl>
#>  1     1     1       1     1.96
#>  2     1     1       2     2.09
#>  3     1     1       3     1.82
#>  4     1     1       4     2.18
#>  5     1     1       5     2.31
#>  6     1     1       6     2.30
#>  7     1     1       7     2.38
#>  8     1     1       8     2.26
#>  9     1     1       9     2.13
#> 10     1     1      10     2.17
#> # ℹ 690 more rows