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) = γ λ (t ^{γ - 1})
exp(β_0 X_i + β_1 X_i x log(t))*

where where Xi is the binary
treatment indicator for individual i, *λ* and *γ* are the
scale and shape parameters for the Weibull baseline hazard, *β_0* is
the log hazard ratio for treatment when t=1 (i.e. when log(t)=0), and
*β_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. *β_0* = -0.5 2. *β_1* =
0.15 3. *λ* = 0.1 4. *γ* = 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)
}

[Package

*casebase* version 0.9.1

Index]