```
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(ggplot2)
library(survival)
library(survParamSim)
set.seed(12345)
```

`survreg()`

functionBefore running a parametric survival simulation, you need to fit a model to your data using `survreg()`

function of `survival`

package.

In this vignette, we will be using `colon`

dataset available in `survival`

package, where the treatment effect of adjuvant Levamisole+5-FU for colon cancer over placebo is evaluated.

First, we load the data and do some data wrangling.

```
# ref for dataset https://vincentarelbundock.github.io/Rdatasets/doc/survival/colon.html
colon2 <-
as_tibble(colon) %>%
# recurrence only and not including Lev alone arm
filter(rx != "Lev",
etype == 1) %>%
# Same definition as Lin et al, 1994
mutate(rx = factor(rx, levels = c("Obs", "Lev+5FU")),
depth = as.numeric(extent <= 2))
```

Generating Kaplan-Meier curves for visually checking the data.

The second plot is looking at how many censoring we have over time.

Looks like we have a fairly uniform censoring between 1800 to 3000 days.

```
survfit.colon <- survfit(Surv(time, status) ~ rx, data = colon2)
survminer::ggsurvplot(survfit.colon)
```

```
survfit.colon.censor <- survfit(Surv(time, 1-status) ~ rx, data = colon2)
survminer::ggsurvplot(survfit.colon.censor)
```

Next we fit a lognormal parametric model for the data.

Here we are using `node4`

and `depth`

as additional covariates in addition to treatment (`rx`

).

You can see that all of the factor has strong association with the outcome.

```
fit.colon <- survreg(Surv(time, status) ~ rx + node4 + depth,
data = colon2, dist = "lognormal")
summary(fit.colon)
#>
#> Call:
#> survreg(formula = Surv(time, status) ~ rx + node4 + depth, data = colon2,
#> dist = "lognormal")
#> Value Std. Error z p
#> (Intercept) 7.5103 0.1343 55.92 < 2e-16
#> rxLev+5FU 0.7606 0.1677 4.54 5.7e-06
#> node4 -1.3474 0.1816 -7.42 1.2e-13
#> depth 1.1243 0.2661 4.22 2.4e-05
#> Log(scale) 0.6040 0.0461 13.10 < 2e-16
#>
#> Scale= 1.83
#>
#> Log Normal distribution
#> Loglik(model)= -2561.7 Loglik(intercept only)= -2607.6
#> Chisq= 91.8 on 3 degrees of freedom, p= 9e-20
#> Number of Newton-Raphson Iterations: 4
#> n= 619
```

`surv_param_sim()`

is the main function of the package that takes `survreg`

object as described above.

It also require you to supply `newdata`

, which is required even if it is not new - i.e. the same data was used for both `survreg()`

and `surv_param_sim()`

.

What it does is: 1. Re-sample all the coefficients in the parametric survival model from variance-covariance matrix for `n.rep`

times. 2. Perform survival time for all subjects in `newdata`

with the corresponding covariates, using one of the resampled coefficients. Also generate censoring time according to `censor.dur`

(if not NULL), and replace the simulated survival time above if censoring time is earlier. 4. Repeat the steps 2. for `n.rep`

times.

```
sim <-
surv_param_sim(object = fit.colon, newdata = colon2,
# Simulate censoring according to the plot above
censor.dur = c(1800, 3000),
# Simulate only 100 times to make the example go fast
n.rep = 100)
```

After the simulation is performed, you can either extract raw simulation results or further calculate Kaplan-Meier estimates or hazard ratio of treatment effect, as you see when you type `sim`

in the console.

```
sim
#> ---- Simulated survival data with the following model ----
#> survreg(formula = Surv(time, status) ~ rx + node4 + depth, data = colon2,
#> dist = "lognormal")
#>
#> * Use `extract_sim()` function to extract individual simulated survivals
#> * Use `calc_km_pi()` function to get survival curves and median survival time
#> * Use `calc_hr_pi()` function to get hazard ratio
#>
#> * Settings:
#> #simulations: 100
#> #subjects: 619 (without NA in model variables)
```

To calculate survival curves for each simulated dataset, `calc_km_pi()`

can be used on the simulated object above.

```
km.pi <- calc_km_pi(sim, trt = "rx")
km.pi
#> ---- Simulated and observed (if calculated) survival curves ----
#> * Use `extract_median_surv()` to extract median survival times
#> * Use `extract_km_pi()` to extract prediction intervals of K-M curves
#> * Use `plot_km_pi()` to draw survival curves
#>
#> * Settings:
#> trt: rx
#> group: (NULL)
#> pi.range: 0.95
#> calc.obs: TRUE
```

Similar to the raw simulated object, you can have a few options for further processing - one of them is plotting prediction intervals with `plot_km_pi()`

function.

```
km.pi
#> ---- Simulated and observed (if calculated) survival curves ----
#> * Use `extract_median_surv()` to extract median survival times
#> * Use `extract_km_pi()` to extract prediction intervals of K-M curves
#> * Use `plot_km_pi()` to draw survival curves
#>
#> * Settings:
#> trt: rx
#> group: (NULL)
#> pi.range: 0.95
#> calc.obs: TRUE
plot_km_pi(km.pi) +
theme(legend.position = "bottom") +
labs(y = "Recurrence free rate") +
expand_limits(y = 0)
```

Or providing median survival summary table with `extract_median_surv()`

function.

```
extract_median_surv(km.pi)
#> # A tibble: 8 x 5
#> rx n description median quantile
#> <fct> <dbl> <chr> <dbl> <dbl>
#> 1 Obs 315 pi_low 1108. 0.025
#> 2 Obs 315 pi_med 1538. 0.5
#> 3 Obs 315 pi_high 2113. 0.975
#> 4 Obs 315 obs 1236 NA
#> 5 Lev+5FU 304 pi_low 2126. 0.025
#> 6 Lev+5FU 304 pi_med 2697. 0.5
#> 7 Lev+5FU 304 pi_high 2927. 0.975
#> 8 Lev+5FU 304 obs NA NA
```

Plot can also be made for subgroups.

You can see that prediction interval is wide for (depth: 1 & nodes4: 1) group, mainly due to small number of subjects

To calculate prediction intervals of HRs, `calc_hr_pi()`

can be used on the simulated object above. Here I only generated subgroups based on “depth”, since the very small N in (depth: 1 & nodes4: 1) can cause issue with calculating HRs.

```
hr.pi <- calc_hr_pi(sim, trt = "rx", group = c("depth"))
hr.pi
#> ---- Simulated and observed (if calculated) hazard ratio ----
#> * Use `extract_hr_pi()` to extract prediction intervals and observed HR
#> * Use `extract_hr()` to extract individual simulated HRs
#> * Use `plot_hr_pi()` to draw histogram of predicted HR
#>
#> * Settings:
#> trt: rx
#> (Lev+5FU as test trt, Obs as control)
#> group: depth
#> pi.range: 0.95
#> calc.obs: TRUE
plot_hr_pi(hr.pi)
```

You can also extract prediction intervals and observed HR with `extract_hr_pi()`

function.