Difference in difference + outcome regression + external control borrowing

Usage

DID_EC_OR(
  data,
  outcome_col_name,
  trial_status_col_name,
  treatment_col_name,
  covariates_col_name,
  T_cross,
  model_form_mu0_ext = "",
  model_form_mu0_rct = "",
  model_form_mu1_rct = "",
  Bootstrap = FALSE,
  R = 500,
  bootstrap_CI_type = "bca",
  alpha = 0.05,
  quiet = TRUE
)

Arguments

data: A data frame containing all subject-level data.
outcome_col_name: Character vector of outcome column names.
trial_status_col_name: Name of the trial status column.
treatment_col_name: Name of the treatment column.
covariates_col_name: Character vector of covariate column names.
T_cross: Integer crossover time point.
model_form_mu0_ext: Formula string(s) for the external control outcome model.
model_form_mu0_rct: Formula string(s) for the RCT control outcome model.
model_form_mu1_rct: Formula string(s) for the RCT treatment outcome model.
Bootstrap: Logical. Whether to use bootstrap inference.
R: Number of bootstrap replicates.
bootstrap_CI_type: Type of bootstrap CI (e.g. "bca", "perc").
alpha: Significance level.
quiet: Logical. If TRUE, suppress printed output.

Value

tau and standard deviation

Examples

if (FALSE) { # \dontrun{
model_form_mu <- c(
  "y1 ~ x1 + x2",
  "y2 ~ x1 + x2",
  "y3 ~ x1 + x2",
  "y4 ~ x1 + x2"
)
res1 <- DID_EC_OR(
  outcome = Y, trial_status = S, treatment = A, covariates = X,
  long_term_marker = c(F, T, T, T),
  model_form_mu0_ext = model_form_mu,
  model_form_mu0_rct = model_form_mu,
  model_form_mu1_rct = model_form_mu
)
} # }