---
title: "Examples for the Usage of the SimNPH Package"
subtitle: "Generate Data, Test and Estimate, Summarise"
author: "Tobias Fellinger"
date: "2025-09-19"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Examples for the Usage of the SimNPH Package}
  %\usepackage[UTF-8]{inputenc}
---




``` r
library(SimDesign)
library(SimNPH)
library(parallel)

cl <- makeCluster(8)
clusterEvalQ(cl, {
  library(SimDesign)
  library(SimNPH)
  library(parallel)
})
```

# A simple scenario with fixed followup

This is a simple example on how to run a simulation of analyses of datasets with
a delayed treatment effect using the max-combo and the log-rank test. The 

## Setting up the Scenarios

Setting up the simulations to be run. `createDesign` creates a `tibble` with
every combination of the parameters. Each line corresponds to one simulation to
be run.

The function `generate_delayed_effect` needs the columns: `n_trt`: number of
patients in the treatment arm, `n_ctrl`: number of patients in the control arm,
`delay`: delay until onset of treatment effect, `hazard_ctrl`: hazard under
control and before onset of treatment effect, `hazart_trt`: hazard under
treatment, `t_max`: maximum time of observation.

An example design `tibble` with all parameters filled out can be created with
`desing_skeleton_delayed_effect`. Use the function to output an example function
call that you can copy and modify as needed, or assign the result to a variable
to obtain a design `tibble` with some default parameters.

By default this will create a simulation design skeleton for simulations of 50
patients in each arm, a constant hazard of 0.2 under control and a hazard of
0.02 under treatment after the effect onset varying from 0 to 10.


``` r
N_sim <- 100

Assumptions <- assumptions_delayed_effect()
#> expand.grid(
#>   delay=m2d(seq(0, 10, by=2)), # delay of 0, 1, ..., 10 months
#>   hazard_ctrl=m2r(24),         # median survival control of 24 months
#>   hazard_trt=m2r(36),          # median survival treatment of 36 months
#>   random_withdrawal=m2r(120)   # median time to random withdrawal 10 years
#> )

Options <- design_fixed_followup()
#> expand.grid(
#>   n_trt=150,         # 150 patients in the treatment arm
#>   n_ctrl=150,        # 150 patients in the control arm
#>   followup=m2d(24),  # followup 2 years
#>   recruitment=m2d(6) # recruitment time 6 months
#> 
#> )

Design <- merge(Assumptions, Options, by=NULL)

knitr::kable(Design)
```



|   delay| hazard_ctrl| hazard_trt| random_withdrawal| n_trt| n_ctrl| followup| recruitment|
|-------:|-----------:|----------:|-----------------:|-----:|------:|--------:|-----------:|
|   0.000|   0.0009489|  0.0006326|         0.0001898|   150|    150|    730.5|     182.625|
|  60.875|   0.0009489|  0.0006326|         0.0001898|   150|    150|    730.5|     182.625|
| 121.750|   0.0009489|  0.0006326|         0.0001898|   150|    150|    730.5|     182.625|
| 182.625|   0.0009489|  0.0006326|         0.0001898|   150|    150|    730.5|     182.625|
| 243.500|   0.0009489|  0.0006326|         0.0001898|   150|    150|    730.5|     182.625|
| 304.375|   0.0009489|  0.0006326|         0.0001898|   150|    150|    730.5|     182.625|



## Defining the 'Generate' funcion

Define the data generating 'generate' function, that beside simulating the time
to event also applies the different censoring processes.


``` r
my_generator <- function(condition, fixed_objects=NULL){
  generate_delayed_effect(condition, fixed_objects) |>
    recruitment_uniform(condition$recruitment) |>
    random_censoring_exp(condition$random_withdrawal) |>
    admin_censoring_time(condition$followup)
} 

```

## Defining the 'Summarise' function

Next, we need to specify a summary function that computes the desired operating
characteristics for each simulation scenario, and each analysis method. In the
example below, we use a summary function that computes the power (as we consider
scenarios under the alternative in the example) of the log-rank test and the
max-combo test across simulated scenarios. For each scenario the function just
averages the number of times the computed p-value is below the significance
level obtain the power.

The results object contains the results of all replications of the corresponding
method for each row of the `Design` object. In this example `results$p` contains
all `N_sim` p-values returned by the `analyse_maxcombo` or `analyse_logrank`
functions respectively. The Summary will contain columns with the rejection rate
and some other summary statistics for both methods.


``` r
alpha <- 0.05

Summarise <- create_summarise_function(
  maxcombo = summarise_test(alpha),
  logrank  = summarise_test(alpha)
)
```

## Putting it all together

Now we put it all together: in Design we give the scenarios defined before. We
want to run 100 replications for each scenario. We want to generate data using
the `generate_delayed_effect` function using the parameters from `Design` and
analyse each replication of each scenario with the two functions
`analyse_logrank` and `analyse_maxcombo`. The output should be summarised with
the `Summarise` function defined before and the simulations should be run in
parallel.


``` r
res <- runSimulation(
  Design,
  replications = N_sim,
  generate = my_generator,
  analyse = list(
    logrank  = analyse_logrank(),
    maxcombo = analyse_maxcombo()
  ),
  summarise = Summarise,
  cl = cl,
  save=FALSE
)
#> 
#> Number of parallel clusters in use: 8
#> 
#> Simulation complete. Total execution time: 10.61s
#> Warning in system2("quarto", "-V", stdout = TRUE, env = paste0("TMPDIR=", : Ausführung von Kommando '"quarto" TMPDIR=C:/Users/Tobias11/AppData/Local/Temp/RtmpGKzPDq/file530456a56ad2
#> -V' ergab Status 1
```

Finally we select the interesting columns from the output. Since all other
parameters are the same for each scenario we just select delay. And we are
interested in the rejection rate of the tests.


``` r
res |> 
  subset(select=c("delay", "maxcombo.rejection_0.05", "logrank.rejection_0.05")) |>
  knitr::kable()
```



|   delay| maxcombo.rejection_0.05| logrank.rejection_0.05|
|-------:|-----------------------:|----------------------:|
|   0.000|                    0.54|                   0.52|
|  60.875|                    0.42|                   0.41|
| 121.750|                    0.45|                   0.34|
| 182.625|                    0.24|                   0.15|
| 243.500|                    0.18|                   0.18|
| 304.375|                    0.21|                   0.18|



# A scenario with an interim analysis

In this scenario we extend the scenario from above to include a fixed followup
as well as an interim analysis after a fixed number of events. For this we will
define additional analyse functions.

First we extend the Design to include a column with the number of events after
which an interim analysis should be done.


``` r
Options <- design_group_sequential()
#> expand.grid(
#>   n_trt=200,          # 200 patients in the treatment arm
#>   n_ctrl=200,         # 200 patients in the control arm
#>   followup=m2d(48),   # maximum followup time 4 years
#>   recruitment=m2d(6), # recruitment time 6 months
#>   interim_events=150, # interim analysis after 150 events
#>   final_events=300    # final analysis after 300 events
#> )

Design <- merge(Assumptions, Options, by=NULL)

knitr::kable(Design)
```



|   delay| hazard_ctrl| hazard_trt| random_withdrawal| n_trt| n_ctrl| followup| recruitment| interim_events| final_events|
|-------:|-----------:|----------:|-----------------:|-----:|------:|--------:|-----------:|--------------:|------------:|
|   0.000|   0.0009489|  0.0006326|         0.0001898|   200|    200|     1461|     182.625|            150|          300|
|  60.875|   0.0009489|  0.0006326|         0.0001898|   200|    200|     1461|     182.625|            150|          300|
| 121.750|   0.0009489|  0.0006326|         0.0001898|   200|    200|     1461|     182.625|            150|          300|
| 182.625|   0.0009489|  0.0006326|         0.0001898|   200|    200|     1461|     182.625|            150|          300|
| 243.500|   0.0009489|  0.0006326|         0.0001898|   200|    200|     1461|     182.625|            150|          300|
| 304.375|   0.0009489|  0.0006326|         0.0001898|   200|    200|     1461|     182.625|            150|          300|



## 'Analyse' functions with an interim analysis

The `analyse_group_sequential` function allows to combine two or more analyse
functions to create an analysis function corresponding to a group sequential
design. The arguments are the times or events after which the analyses are done,
the nominal alpha at each stage and the analyse functions to be used at each
stage.


``` r
## O'Brien-Fleming Bounds for GSD with interim analysis at information time 1/2
nominal_alpha <- ldbounds::ldBounds(c(0.5,1))$nom.alpha

clusterExport(cl, "nominal_alpha")

Analyse <-  list(
  logrank_seq  = analyse_group_sequential(
    followup = c(condition$interim_events, condition$final_events),
    followup_type = c("event", "event"),
    alpha = nominal_alpha,
    analyse_functions = analyse_logrank()
  ),
  maxcombo_seq = analyse_group_sequential(
    followup = c(condition$interim_events, condition$final_events),
    followup_type = c("event", "event"),
    alpha = nominal_alpha,
    analyse_functions = analyse_maxcombo()
  )
)
```

## A 'Summarise' function for the more complex scenario

The output of the function created with `analyse_group_sequential` contains
additional columns. `rejected_at_stage` includes the stage at which the null was
first rejected or `Inf` if the null was not rejected, `N_pat` and `N_evt`
contain the number of patients recruited and the number of events observed
before the null was rejected and `followup` contains the time after study start
at which the last analysis was done.

The results object also includes the results returned by each stage in
`results_stages`, but here we only use the overall test-decision.


``` r
Summarise <- create_summarise_function(
  maxcombo_seq = summarise_group_sequential(),
  logrank_seq = summarise_group_sequential()
)
```

## Putting it all together

The call to `runSimulation` looks almost the same as above but now the
additional columns we defined in our `Summarise` functions are included in the
result.


``` r
res <- runSimulation(
  Design,
  replications = N_sim,
  generate = my_generator,
  analyse = Analyse,
  summarise = Summarise,
  cl = cl,
  save=FALSE
)
#> 
#> Number of parallel clusters in use: 8
#> 
#> Simulation complete. Total execution time: 21.83s
#> Warning in system2("quarto", "-V", stdout = TRUE, env = paste0("TMPDIR=", : Ausführung von Kommando '"quarto" TMPDIR=C:/Users/Tobias11/AppData/Local/Temp/RtmpGKzPDq/file5304116837a0
#> -V' ergab Status 1
```

In the case of a group sequential design we are also interested in the average
running time of the study in terms of patients recruited, number of events and
running time of the study.


``` r
res |>
  subset(select=c(
    "delay", 
    "maxcombo_seq.rejection", "logrank_seq.rejection",
    "maxcombo_seq.n_pat", "logrank_seq.n_pat",
    "maxcombo_seq.n_evt", "logrank_seq.n_evt",
    "maxcombo_seq.followup", "logrank_seq.followup"
    )) |>
  knitr::kable()
```



|   delay| maxcombo_seq.rejection| logrank_seq.rejection| maxcombo_seq.n_pat| logrank_seq.n_pat| maxcombo_seq.n_evt| logrank_seq.n_evt| maxcombo_seq.followup| logrank_seq.followup|
|-------:|----------------------:|---------------------:|------------------:|-----------------:|------------------:|-----------------:|---------------------:|--------------------:|
|   0.000|                   0.85|                  0.82|                400|               400|             213.53|            213.07|               1278.40|              1272.17|
|  60.875|                   0.75|                  0.75|                400|               400|             219.45|            219.06|               1300.93|              1298.89|
| 121.750|                   0.72|                  0.72|                400|               400|             225.80|            226.10|               1342.47|              1343.26|
| 182.625|                   0.60|                  0.59|                400|               400|             232.30|            234.03|               1380.38|              1396.26|
| 243.500|                   0.63|                  0.59|                400|               400|             238.15|            240.42|               1410.94|              1428.86|
| 304.375|                   0.61|                  0.53|                400|               400|             240.66|            240.66|               1443.26|              1443.26|



# Estimation

## Calculating the true values of the summary statistics

To evaluate the performance of an estimator, we first compute the values of some
true summary statistics to which the estimates will be compared. The most
relevant true summary statistics can be computed by a convenience function for
each scenario. Just pipe the Design data.frame to the function and the values of
the statistics are added as columns.


``` r
Options <- design_fixed_followup()
#> expand.grid(
#>   n_trt=150,         # 150 patients in the treatment arm
#>   n_ctrl=150,        # 150 patients in the control arm
#>   followup=m2d(24),  # followup 2 years
#>   recruitment=m2d(6) # recruitment time 6 months
#> 
#> )

Design <- merge(Assumptions, Options, by=NULL)

Design <- Design |> 
  true_summary_statistics_delayed_effect(cutoff_stats = 20)

knitr::kable(Design)
```



|   delay| hazard_ctrl| hazard_trt| random_withdrawal| n_trt| n_ctrl| followup| recruitment| median_survival_trt| median_survival_ctrl| rmst_trt_20| rmst_ctrl_20|   gAHR_20|    AHR_20|  AHRoc_20| AHRoc_robust_20|
|-------:|-----------:|----------:|-----------------:|-----:|------:|--------:|-----------:|-------------------:|--------------------:|-----------:|------------:|---------:|---------:|---------:|---------------:|
|   0.000|   0.0009489|  0.0006326|         0.0001898|   150|    150|    730.5|     182.625|           1095.7500|                730.5|    19.87402|     19.81142| 0.6666667| 0.6666667| 0.6666667|       0.6666667|
|  60.875|   0.0009489|  0.0006326|         0.0001898|   150|    150|    730.5|     182.625|           1065.3125|                730.5|    19.81142|     19.81142| 1.0000000| 1.0000000| 1.0000000|       1.0000000|
| 121.750|   0.0009489|  0.0006326|         0.0001898|   150|    150|    730.5|     182.625|           1034.8750|                730.5|    19.81142|     19.81142| 1.0000000| 1.0000000| 1.0000000|       1.0000000|
| 182.625|   0.0009489|  0.0006326|         0.0001898|   150|    150|    730.5|     182.625|           1004.4375|                730.5|    19.81142|     19.81142| 1.0000000| 1.0000000| 1.0000000|       1.0000000|
| 243.500|   0.0009489|  0.0006326|         0.0001898|   150|    150|    730.5|     182.625|            974.0000|                730.5|    19.81142|     19.81142| 1.0000000| 1.0000000| 1.0000000|       1.0000000|
| 304.375|   0.0009489|  0.0006326|         0.0001898|   150|    150|    730.5|     182.625|            943.5625|                730.5|    19.81142|     19.81142| 1.0000000| 1.0000000| 1.0000000|       1.0000000|



## Defining the `Summarise` function

In the Summarise function the true value against which the estimator should be
compared has to be specified. If coverage and average width of the confidence
intervals should be estimated, the CI bounds should also be specified.

The arguments to the functions are left un-evaluated and are later evaluated in
the `results` and `condition` datasets respectively. So any expressions using
variables from results can be used for the estimated value and the CI bounds and
expressions using variables from condition can be used in the argument for the
real value.

In this case we want to compare the hazard ratio estimated by the Cox model to
the geometric average hazard ratio as well as to the hazard ratio after onset of
treatment, calculated from the two respective columns of the Design data.frame.

Note that one name can be used twice to summarise the output of one analysis
method two times, like in this case, comparing it to two different summary
statistics.


``` r
Summarise <- create_summarise_function(
    coxph=summarise_estimator(hr, gAHR_20, hr_lower, hr_upper, name="gAHR"),
    coxph=summarise_estimator(hr, hazard_trt/hazard_ctrl, hr_lower, hr_upper, name="HR")
  )
```

## Putting it all together


``` r
Analyse <- list(
  coxph = analyse_coxph()
)

res <- runSimulation(
  Design,
  replications = N_sim,
  generate = my_generator,
  analyse = Analyse,
  summarise = Summarise,
  cl = cl,
  save=FALSE
)
#> 
#> Number of parallel clusters in use: 8
#> 
#> Simulation complete. Total execution time: 0.77s
#> Warning in system2("quarto", "-V", stdout = TRUE, env = paste0("TMPDIR=", : Ausführung von Kommando '"quarto" TMPDIR=C:/Users/Tobias11/AppData/Local/Temp/RtmpGKzPDq/file530433727d3d
#> -V' ergab Status 1
```


``` r
res |>
  subset(select=c(
    "delay", "coxph.HR.bias", "coxph.gAHR.bias", "coxph.HR.mse", 
    "coxph.gAHR.mse", "coxph.HR.coverage", "coxph.gAHR.coverage"
  )) |> 
  knitr::kable()
```



|   delay| coxph.HR.bias| coxph.gAHR.bias| coxph.HR.mse| coxph.gAHR.mse| coxph.HR.coverage| coxph.gAHR.coverage|
|-------:|-------------:|---------------:|------------:|--------------:|-----------------:|-------------------:|
|   0.000|     0.0103694|       0.0103694|    0.0201519|      0.0201519|              0.95|                0.95|
|  60.875|     0.0400061|      -0.2933273|    0.0192242|      0.1036646|              0.95|                0.51|
| 121.750|     0.0805660|      -0.2527674|    0.0245258|      0.0819263|              0.96|                0.67|
| 182.625|     0.1388977|      -0.1944356|    0.0420955|      0.0606081|              0.80|                0.73|
| 243.500|     0.1835182|      -0.1498151|    0.0578980|      0.0466636|              0.78|                0.82|
| 304.375|     0.1764311|      -0.1569022|    0.0609472|      0.0544376|              0.77|                0.79|




.
