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
h_i(t) = \gamma \lambda (t ^{\gamma - 1})
exp(\beta_0 X_i + \beta_1 X_i x log(t))
where where Xi is the binary
treatment indicator for individual i, \lambda
and \gamma
are the
scale and shape parameters for the Weibull baseline hazard, \beta_0
is
the log hazard ratio for treatment when t=1 (i.e. when log(t)=0), and
\beta_1
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. \beta_0
= -0.5 2. \beta_1
=
0.15 3. \lambda
= 0.1 4. \gamma
= 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)
}