View source: R/simfix2simPWSurv.R

simfix2simPWSurv | R Documentation |

`simfix2simPWSurv()`

converts failure rates and dropout rates entered in the simpler
format for `sim_fixed_n()`

to that used for `simtrial::sim_pw_surv()`

.
The `fail_rate`

argument for `sim_fixed_n()`

requires enrollment rates, failure rates
hazard ratios and dropout rates by strata for a 2-arm trial, `simtrial::sim_pw_surv()`

is in a more flexible but less obvious but more flexible format.
Since `sim_fixed_n()`

automatically analyzes data and `simtrial::sim_pw_surv()`

just produces
a simulation dataset, the latter provides additional options to analyze or otherwise evaluate
individual simulations in ways that `sim_fixed_n()`

does not.

```
simfix2simPWSurv(
fail_rate = tibble(Stratum = "All", duration = c(3, 100), fail_rate = log(2)/c(9, 18),
hr = c(0.9, 0.6), dropoutRate = rep(0.001, 2))
)
```

`fail_rate` |
Piecewise constant control group failure rates, hazard ratio for experimental vs control, and dropout rates by stratum and time period. |

A `list`

of two `tibble`

components formatted for `simtrial::sim_pw_surv()`

:
`fail_rate`

and `dropoutRates`

.

```
library(tidyr)
library(dplyr)
library(tibble)
# example 1
# Convert standard input
simfix2simPWSurv()
# Stratified example
fail_rate <- tibble(Stratum = c(rep("Low", 3),rep("High", 3)),
duration = rep(c(4, 10, 100), 2),
fail_rate = c(.04, .1, .06,
.08,.16,.12),
hr = c(1.5, .5, 2/3,
2, 10/16, 10/12),
dropoutRate =.01)
x <- simfix2simPWSurv(fail_rate)
# Do a single simulation with the above rates
# Enroll 300 patients in ~12 months at constant rate
sim <- sim_pw_surv(n = 300,
strata = tibble(Stratum = c("Low","High"), p = c(.6, .4)),
enroll_rate = tibble(duration = 12, rate = 300 / 12),
fail_rate = x$fail_rate,
dropoutRates = x$dropoutRates)
# Cut after 200 events and do a stratified logrank test
dat <- sim %>%
cut_data_by_event(200) %>% # cut data
counting_process(arm = "Experimental") %>% # convert format for wlr
wlr(rg = tibble(rho=0,gamma=0)) # stratified logrank
```

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