Bernoulli distribution with logit parametrization

Bernoulli distribution with logit parametrization

Usage

outcome_bin_logistic(binary_var, baseline_prior, weight_var = "")

Arguments

binary_var

character. Name of binary (1/0 or TRUE/FALSE) outcome variable in the model matrix

baseline_prior

Prior. Object of class Prior specifying prior distribution for the baseline outcome. See Details for more information.

weight_var

character. Optional name of variable in model matrix for weighting the log likelihood.

Value

Object of class OutcomeBinaryLogistic.

Details

Baseline Prior

The baseline_prior argument specifies the prior distribution for the baseline log odds. The interpretation of the baseline_prior differs slightly between borrowing methods selected.

• Dynamic borrowing using borrowing_hierarchical_commensurate(): the baseline_prior for Bayesian Dynamic Borrowing refers to the log odds of the external control arm.

• Full borrowing or No borrowing using borrowing_full() or borrowing_none(): the baseline_prior for these borrowing methods refers to the log odds for the internal control arm.

See also

Other outcome models: outcome_cont_normal(), outcome_surv_exponential(), outcome_surv_pem(), outcome_surv_weibull_ph()

Examples

lg <- outcome_bin_logistic(
  binary_var = "response",
  baseline_prior = prior_normal(0, 1000)
)