R: Hierarchical model MCMC

hb_mcmc_hierarchical {historicalborrow}

R Documentation

Hierarchical model MCMC

Description

Run the hierarchical model with MCMC.

Usage

hb_mcmc_hierarchical(
  data,
  response = "response",
  study = "study",
  study_reference = max(data[[study]]),
  group = "group",
  group_reference = min(data[[group]]),
  patient = "patient",
  covariates = grep("^covariate", colnames(data), value = TRUE),
  s_delta = 30,
  s_beta = 30,
  s_sigma = 30,
  s_mu = 30,
  s_tau = 30,
  n_chains = 4,
  n_adapt = 2000,
  n_warmup = 4000,
  n_iterations = 20000,
  quiet = TRUE
)

Arguments

`data`	Tidy data frame with one row per patient, indicator columns for the response variable, study, group, and patient, and covariates. All columns must be atomic vectors (e.g. not lists). The data for the mixture and simple models should have just one study, and the others should have data from more than one study. The simple model can be used to get the historical data components of `m_omega` and `s_omega` for the mixture model.
`response`	Character of length 1, name of the column in `data` with the response/outcome variable. `data[[response]]` must be a continuous variable, and it should be the change from baseline of a clinical endpoint of interest, as opposed to just the raw response. Treatment differences are computed directly from this scale, please supply change from baseline unless you are absolutely certain that treatment differences computed directly from this quantity are clinically meaningful.
`study`	Character of length 1, name of the column in `data` with the study ID.
`study_reference`	Atomic of length 1, element of the `study` column that indicates the current study. (The other studies are historical studies.)
`group`	Character of length 1, name of the column in `data` with the group ID.
`group_reference`	Atomic of length 1, element of the `group` column that indicates the control group. (The other groups may be treatment groups.)
`patient`	Character of length 1, name of the column in `data` with the patient ID.
`covariates`	Character vector of column names in `data` with the columns with baseline covariates. These can be continuous, categorical, or binary. Regardless, `historicalborrow` derives the appropriate model matrix.
`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`	Positive numeric of length 1, hyperparameter: prior standard deviation of the mean `mu` of the study-level control means `alpha`.
`s_tau`	Positive numeric of length 1, hyperparameter: uniform prior upper bound of the standard deviation `tau` of the study-level control means `alpha`.
`n_chains`	Number of MCMC chains to run.
`n_adapt`	Number of adaptation iterations to run.
`n_warmup`	Number of warmup iterations per chain to run.
`n_iterations`	Number of saved MCMC iterations per chain to run.
`quiet`	Logical of length 1, `TRUE` to suppress R console output.

Value

A tidy data frame of parameter samples from the posterior distribution. Columns .chain, .iteration, and .draw have the meanings documented in the posterior package.

Examples

if (!identical(Sys.getenv("HB_TEST", unset = ""), "")) {
data <- hb_sim_hierarchical(n_continuous = 2)$data
hb_mcmc_hierarchical(
  data,
  n_chains = 1,
  n_adapt = 100,
  n_warmup = 50,
  n_iterations = 50
)
}