outcome_surv_pem {psborrow2} | R Documentation |
Piecewise exponential survival distribution
Description
Piecewise exponential survival distribution
Usage
outcome_surv_pem(
time_var,
cens_var,
baseline_prior,
weight_var = "",
cut_points
)
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 |
|
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
|
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()
: thebaseline_prior
for Bayesian Dynamic Borrowing refers to the log hazard rate of the external control arm. -
Full borrowing or No borrowing using
borrowing_full()
orborrowing_none()
: thebaseline_prior
for these borrowing methods refers to the log hazard rate for the internal control arm.
Value
Object of class OutcomeSurvPEM
.
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)
)