psborrow2 (version 0.0.4.0)

outcome_surv_pem: Piecewise exponential survival distribution

Description

Piecewise exponential survival distribution

Usage

outcome_surv_pem(
  time_var,
  cens_var,
  baseline_prior,
  weight_var = "",
  cut_points
)

Value

Object of class OutcomeSurvPEM.

Arguments

time_var

character. Name of time variable column in model matrix

cens_var

character. Name of the censorship variable flag in model matrix

baseline_prior

Prior. Object of class Prior specifying prior distribution for each cut point. See Details for more information.

weight_var

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

cut_points

numeric. Vector of internal cut points for the piecewise exponential model. Note: the choice of cut points will impact the amount of borrowing between arms when dynamic borrowing methods are selected. It is recommended to choose cut points that contain an equal number of events within each interval. Please include only internal cut points in the vector. For instance, for cut points of [0, 15], (15, 20], (20, Inf), the vector should be c(15, 20). If you pass cut-points beyond the follow-up of the data, you will receive an informative warning when calling create_analysis_object() and these cut points will be ignored.

Details

Baseline Prior

The baseline_prior argument specifies the prior distribution for the baseline log hazard rate within each cutpoint. Currently, there is no option to consider different baseline priors within each cut point. 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.

See Also

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

Examples

es <- outcome_surv_pem(
  time_var = "time",
  cens_var = "cens",
  baseline_prior = prior_normal(0, 1000),
  cut_points = c(10, 15, 30)
)