simdat {casebase} | R Documentation |

This simulated data is and description is taken verbatim from the
`simsurv`

.

```
simdat
```

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

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

See `simsurv`

vignette:
https://cran.r-project.org/package=simsurv/vignettes/simsurv_usage.html

Sam Brilleman (2019). simsurv: Simulate Survival Data. R package version 0.2.3. https://CRAN.R-project.org/package=simsurv

```
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 0.10.3 Index]