outcome_bin_logistic: Bernoulli distribution with logit parametrization

In psborrow2: Bayesian Dynamic Borrowing Analysis and Simulation

Bernoulli distribution with logit parametrization

Description

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.

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.

Value

Object of class OutcomeBinaryLogistic.

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)
)

psborrow2 documentation built on April 4, 2025, 12:37 a.m.