# Parallelize 'riskRegression' functions

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The futurize package allows you to easily turn sequential code into parallel code by piping the sequential code to the `futurize()` function. Easy!

# TL;DR

    library(futurize)
    plan(multisession)
    library(riskRegression)
    library(survival)

    set.seed(42)
    d <- sampleData(200, outcome = "competing.risks")
    fit <- CSC(Hist(time, event) ~ X1 + X2 + X7 + X8, data = d)
    sc <- Score(list("CSC" = fit), data = d,
                formula = Hist(time, event) ~ 1,
                times = 5, B = 100, split.method = "bootcv") |> futurize()

# Introduction

This vignette demonstrates how to use this approach to parallelize riskRegression functions such as `Score()`.

The riskRegression package provides tools for risk regression modeling and prediction in survival analysis with competing risks. It supports fitting cause-specific Cox regression models, Fine-Gray regression, and absolute risk regression models. The `Score()` function performs bootstrap cross-validation for model evaluation, which is an excellent candidate for parallelization.

## Example: Bootstrap cross-validation with Score()

The `Score()` function evaluates prediction models via bootstrap cross-validation with metrics such as time-dependent AUC and Brier scores:

    library(riskRegression)
    library(survival)

    set.seed(42)
    d <- sampleData(200, outcome = "competing.risks")
    fit <- CSC(Hist(time, event) ~ X1 + X2 + X7 + X8, data = d)

    ## Bootstrap cross-validation with 100 bootstrap samples
    sc <- Score(list("CSC" = fit), data = d,
                formula = Hist(time, event) ~ 1,
                times = 5, B = 100, split.method = "bootcv")

Here `Score()` evaluates sequentially, but we can easily make it evaluate in parallel by piping to `futurize()`:

    library(futurize)
    library(riskRegression)
    library(survival)

    set.seed(42)
    d <- sampleData(200, outcome = "competing.risks")
    fit <- CSC(Hist(time, event) ~ X1 + X2 + X7 + X8, data = d)

    sc <- Score(list("CSC" = fit), data = d,
                formula = Hist(time, event) ~ 1,
                times = 5, B = 100, split.method = "bootcv") |> futurize()

This will distribute the bootstrap samples across the available parallel workers, given that we have set up parallel workers, e.g.

    plan(multisession)

The built-in `multisession` backend parallelizes on your local computer and works on all operating systems. There are other parallel backends to choose from, including alternatives to parallelize locally as well as distributed across remote machines, e.g.

    plan(future.mirai::mirai_multisession)

and

    plan(future.batchtools::batchtools_slurm)

# Supported Functions

The following riskRegression functions are supported by `futurize()`:

- `Score()` for 'list' with `seed = TRUE` as the default

# Without futurize: Manual PSOCK cluster setup

For comparison, here is what it takes to parallelize `Score()` using the parallel and doParallel packages directly, without futurize:

    library(riskRegression)
    library(survival)
    library(parallel)
    library(doParallel)

    set.seed(42)
    d <- sampleData(200, outcome = "competing.risks")
    fit <- CSC(Hist(time, event) ~ X1 + X2 + X7 + X8, data = d)

    ## Set up a PSOCK cluster and register it with foreach
    ncpus <- 4L
    cl <- makeCluster(ncpus)
    registerDoParallel(cl)

    ## Bootstrap cross-validation in parallel via foreach
    sc <- Score(list("CSC" = fit), data = d,
                formula = Hist(time, event) ~ 1,
                times = 5, B = 100, split.method = "bootcv",
                parallel = "as.registered")

    ## Tear down the cluster
    stopCluster(cl)
    registerDoSEQ()  ## reset foreach to sequential

This requires you to manually create a cluster, register it with doParallel, and remember to tear it down and reset the foreach backend when done. If you forget to call `stopCluster()`, or if your code errors out before reaching it, you leak background R processes. You also have to decide upfront how many CPUs to use and what cluster type to use. Switching to another parallel backend, e.g. a Slurm cluster, would require a completely different setup. With futurize, all of this is handled for you - just pipe to `futurize()` and control the backend with `plan()`.