hb_sim_mixture {historicalborrow}

Non-longitudinal mixture simulations.

Description

Simulate from the non-longitudinal mixture model.

Usage

hb_sim_mixture(
  n_study = 5,
  n_group = 3,
  n_patient = 100,
  n_continuous = 0,
  n_binary = 0,
  s_delta = 1,
  s_beta = 1,
  s_sigma = 1,
  m_omega = 0,
  s_omega = 1,
  p_omega = 1/n_study,
  alpha = omega[pi],
  delta = stats::rnorm(n = n_group - 1, mean = 0, sd = s_delta),
  beta = stats::rnorm(n = n_continuous + n_binary, mean = 0, sd = s_delta),
  sigma = stats::runif(n = 1, min = 0, max = s_sigma),
  pi = sample.int(n = n_study, size = 1, prob = p_omega),
  omega = stats::rnorm(n = n_study, mean = m_omega, sd = s_omega)
)

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.

m_omega

Numeric of length 1 or n_study, prior control group mean of each study. If length n_study, then the last element corresponds to the current study, and the others are for historical studies.

s_omega

Numeric of length 1 or n_study, prior control group standard deviation of each study. If length n_study, the the last element corresponds to the current study, and the others are for historical studies.

p_omega

Numeric of length n_study, prior mixture proportion of each study. If length n_study, then the last element corresponds to the current study, and the others are for historical studies.

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_continuous + n_binary fixed effect parameters. The first n_continuous betas are for the continuous covariates, and the rest are for the binary covariates. 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.

pi

Integer of length 1, index of the mixture component randomly chosen for alpha.

omega

Numeric of length n_study, Candidate placebo mean parameters drawn from each of the mixture components.

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_hierarchical(), hb_sim_independent(), hb_sim_pool()

Examples

hb_sim_mixture()$data