outcome_cont_normal: Normal Outcome Distribution

In psborrow2: Bayesian Dynamic Borrowing Analysis and Simulation

Normal Outcome Distribution

Description

Normal Outcome Distribution

Usage

outcome_cont_normal(
  continuous_var,
  baseline_prior,
  std_dev_prior,
  weight_var = ""
)

Arguments

continuous_var	character. Name of continuous 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.
std_dev_prior	Prior. Object of class Prior specifying prior distribution for the standard deviation of the outcome distribution (i.e. "sigma").
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 intercept of the linear model. 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 intercept 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 intercept for the internal control arm.

Value

Object of class OutcomeContinuousNormal.

See Also

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

Examples

norm <- outcome_cont_normal(
  continuous_var = "tumor_size",
  baseline_prior = prior_normal(0, 100),
  std_dev_prior = prior_half_cauchy(1, 5)
)

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