The goal of survParamSim is to perform survival simulation with parametric survival model generated from ‘survreg’ function in ‘survival’ package. In each simulation, coefficients are resampled from variance-covariance matrix of parameter estimates, in order to capture uncertainty in model parameters.

Installation

You can install the package from CRAN.

install.packages("survParamSim")

Alternatively, you can install the development version from GitHub.

# install.packages("devtools")
devtools::install_github("yoshidk6/survParamSim")

Example

This GitHub pages contains function references and vignette. The example below is a sneak peek of example outputs.

First, run survreg to fit parametric survival model:

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)
#> Warning: package 'survParamSim' was built under R version 4.2.3
set.seed(12345)
# 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))

fit.colon <- survreg(Surv(time, status) ~ rx + node4 + depth,
                     data = colon2, dist = "lognormal")

Next, run parametric bootstrap simulation:

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

Calculate survival curves with prediction intervals:

km.pi <- calc_km_pi(sim, trt = "rx", group = c("node4", "depth"))
#> Warning in calc_km_pi(sim, trt = "rx", group = c("node4", "depth")): 339 of 800
#> simulations (#rep * #trt * #group) did not reach median survival time and these
#> are not included for prediction interval calculation. You may want to delay the
#> `censor.dur` in simulation.
km.pi
#> ---- Simulated and observed (if calculated) survival curves ----
#> * Use `extract_medsurv_pi()` to extract prediction intervals of 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: node4 
#>     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)

extract_medsurv_pi(km.pi) # Not implemented for calc_ave_km_pi yet; available for calc_km_pi
#> # A tibble: 32 × 7
#>    node4 depth rx          n description median quantile
#>    <dbl> <dbl> <fct>   <dbl> <chr>        <dbl>    <dbl>
#>  1     0     0 Obs       193 pi_low       1257.   0.0250
#>  2     0     0 Obs       193 pi_med       1895.   0.5   
#>  3     0     0 Obs       193 pi_high      2713.   0.975 
#>  4     0     0 Obs       193 obs          1436   NA     
#>  5     0     0 Lev+5FU   192 pi_low       2429.   0.0250
#>  6     0     0 Lev+5FU   192 pi_med       2716.   0.5   
#>  7     0     0 Lev+5FU   192 pi_high      2908.   0.975 
#>  8     0     0 Lev+5FU   192 obs            NA   NA     
#>  9     0     1 Obs        35 pi_low       1539.   0.0250
#> 10     0     1 Obs        35 pi_med       2558.   0.5   
#> # … with 22 more rows

Calculate hazard ratios with prediction intervals:

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)

extract_hr_pi(hr.pi)
#> # A tibble: 8 × 5
#>   depth rx      description    HR quantile
#>   <dbl> <fct>   <chr>       <dbl>    <dbl>
#> 1     0 Lev+5FU pi_low      0.464   0.0250
#> 2     0 Lev+5FU pi_med      0.624   0.5   
#> 3     0 Lev+5FU pi_high     0.794   0.975 
#> 4     0 Lev+5FU obs         0.590  NA     
#> 5     1 Lev+5FU pi_low      0.233   0.0250
#> 6     1 Lev+5FU pi_med      0.597   0.5   
#> 7     1 Lev+5FU pi_high     1.13    0.975 
#> 8     1 Lev+5FU obs         0.607  NA