outcome_surv_weibull_ph: Weibull survival distribution (proportional hazards...

In psborrow2: Bayesian Dynamic Borrowing Analysis and Simulation

Weibull survival distribution (proportional hazards formulation)

Description

Weibull survival distribution (proportional hazards formulation)

Usage

outcome_surv_weibull_ph(
  time_var,
  cens_var,
  shape_prior,
  baseline_prior,
  weight_var = ""
)

Arguments

time_var	character. Name of time variable column in model matrix
cens_var	character. Name of the censorship variable flag in model matrix
shape_prior	Prior class object for the Weibull shape parameter. Default is prior_exponential(beta = 0.0001).
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 hazard rate. 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 hazard rate 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 hazard rate for the internal control arm.

Value

Object of class OutcomeSurvWeibullPH.

See Also

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

Examples

ws <- outcome_surv_weibull_ph(
  time_var = "time",
  cens_var = "cens",
  shape_prior = prior_exponential(1),
  baseline_prior = prior_normal(0, 1000)
)

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