simdat {casebase} | R Documentation |
Simulated data under Weibull model with Time-Dependent Treatment Effect
Description
This simulated data is and description is taken verbatim from the
simsurv
.
Usage
simdat
Format
A dataframe with 1000 observations and 4 variables:
- id
patient id
- eventtime
time of event
- status
event indicator (1 = event, 0 = censored)
- trt
binary treatment indicator
Details
Simulated data under a standard Weibull survival model that incorporates a time-dependent treatment effect (i.e. non-proportional hazards). For the time-dependent effect we included a single binary covariate (e.g. a treatment indicator) with a protective effect (i.e. a negative log hazard ratio), but we will allow the effect of the covariate to diminish over time. The data generating model will be
where where Xi is the binary
treatment indicator for individual i, and
are the
scale and shape parameters for the Weibull baseline hazard,
is
the log hazard ratio for treatment when t=1 (i.e. when log(t)=0), and
quantifies the amount by which the log hazard ratio for
treatment changes for each one unit increase in log(t). Here we are assuming
the time-dependent effect is induced by interacting the log hazard ratio with
log time. The true parameters are 1.
= -0.5 2.
=
0.15 3.
= 0.1 4.
= 1.5
Source
See simsurv
vignette:
https://cran.r-project.org/package=simsurv/vignettes/simsurv_usage.html
References
Sam Brilleman (2019). simsurv: Simulate Survival Data. R package version 0.2.3. https://CRAN.R-project.org/package=simsurv
Examples
if (requireNamespace("splines", quietly = TRUE)) {
library(splines)
data("simdat")
mod_cb <- casebase::fitSmoothHazard(status ~ trt + ns(log(eventtime),
df = 3) +
trt:ns(log(eventtime),df=1),
time = "eventtime",
data = simdat,
ratio = 1)
}