rfacts

The rfacts package is an R interface to theFixed and Adaptive Clinical Trial Simulator (FACTS) on Unix-like systems. It programmatically invokesFACTS to run clinical trial simulations, and it aggregates simulation output data into tidy data frames. These capabilities provide end-to-end automation for large-scale simulation workflows, and they enhance computational reproducibility. For more information, please visit thedocumentation website.

Disclaimer

rfacts is not a product of nor supported byBerry Consultants. The code base ofrfacts is completely independent from that ofFACTS, and the former only invokes the latter though dynamic system calls.

Limitations

FACTS files prior to version 6.2.4 are unsupported.
rfacts only works on Unix-like systems.
rfacts requires paths to pre-compiled versions of Mono, FLFLL, and the FACTS Linux engines. See the installation instructions below and theconfiguration guide.

Installation

To install the latest release from CRAN, open R and run the following.

install.packages("rfacts")

To install the latest development version:

install.packages("remotes")remotes::install_github("EliLillyCo/rfacts")

Next, set theRFACTS_PATHS environment variable appropriately. For instructions, please see theconfiguration guide.

Run FACTS simulations

First, create a*.facts XML file using theFACTS GUI. Therfacts package has several built-in examples, included with permission from Berry Consultants LLC.

library(rfacts)# get_facts_file_example() returns the path to# an example a FACTS file from rfacts itself.# For your own FACTS files you create yourself in the FACTS GUI,# you can skip get_facts_file_example().facts_file<-get_facts_file_example("contin.facts")basename(facts_file)#> [1] "contin.facts"

Then, run trial simulations withrun_facts(). By default, the results are written to a temporary directory. Set theoutput_path argument to customize the path.

out<-run_facts(facts_file,  n_sims=2,  verbose=FALSE)out#> [1] "/tmp/RtmpFv78Hj/file427b6fd72e42"head(get_csv_files(out))#> [1] "/tmp/RtmpFv78Hj/file427b6fd72e42/contin/acc1_drop1_resp1_params/patients00001.csv"#> [2] "/tmp/RtmpFv78Hj/file427b6fd72e42/contin/acc1_drop1_resp1_params/patients00002.csv"#> [3] "/tmp/RtmpFv78Hj/file427b6fd72e42/contin/acc1_drop1_resp1_params/weeks_freq_ignore_00001.csv"#> [4] "/tmp/RtmpFv78Hj/file427b6fd72e42/contin/acc1_drop1_resp1_params/weeks_freq_ignore_00002.csv"#> [5] "/tmp/RtmpFv78Hj/file427b6fd72e42/contin/acc1_drop1_resp1_params/weeks_freq_locf_00001.csv"#> [6] "/tmp/RtmpFv78Hj/file427b6fd72e42/contin/acc1_drop1_resp1_params/weeks_freq_locf_00002.csv"

Useread_patients() to read and aggregate all thepatients*.csv files.rfacts has several such functions, includingread_weeks() andread_mcmc().

read_patients(out)#> # A tibble: 2,400 x 15#>    facts_file facts_scenario facts_sim facts_id facts_output facts_csv#>    <chr>      <chr>              <int> <chr>    <chr>        <chr>#>  1 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  2 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  3 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  4 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  5 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  6 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  7 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  8 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  9 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#> 10 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#> # … with 2,390 more rows, and 9 more variables: facts_header <chr>,#> #   subject <int>, region <int>, date <dbl>, dose <int>, lastvisit <int>,#> #   dropout <int>, baseline <lgl>, visit_1 <dbl>

The simulation process

run_facts() has two sequential stages:

run_flfll(): generate the*.param files and the folder structure for the FACTS Linux engines.
run_engine(): execute the instructions in the*.param files to conduct trial simulations and produce CSV output.

out<-run_flfll(facts_file, verbose=FALSE)run_engine(facts_file, param_files=out, n_sims=4, verbose=FALSE)read_patients(out)#> # A tibble: 4,800 x 15#>    facts_file facts_scenario facts_sim facts_id facts_output facts_csv#>    <chr>      <chr>              <int> <chr>    <chr>        <chr>#>  1 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  2 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  3 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  4 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  5 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  6 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  7 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  8 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  9 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#> 10 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#> # … with 4,790 more rows, and 9 more variables: facts_header <chr>,#> #   subject <int>, region <int>, date <dbl>, dose <int>, lastvisit <int>,#> #   dropout <int>, baseline <lgl>, visit_1 <dbl>

run_engine() automatically detects the Linux engine required for your FACTS file. If you know the engine in advance, you can use a specific engine function such asrun_engine_contin() orrun_engine_dichot().

out<-run_flfll(facts_file, verbose=FALSE)run_engine_contin(param_files=out, n_sims=4, verbose=FALSE)read_patients(out)#> # A tibble: 4,800 x 15#>    facts_file facts_scenario facts_sim facts_id facts_output facts_csv#>    <chr>      <chr>              <int> <chr>    <chr>        <chr>#>  1 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  2 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  3 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  4 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  5 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  6 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  7 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  8 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  9 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#> 10 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#> # … with 4,790 more rows, and 9 more variables: facts_header <chr>,#> #   subject <int>, region <int>, date <dbl>, dose <int>, lastvisit <int>,#> #   dropout <int>, baseline <lgl>, visit_1 <dbl>

If you are unsure which engine function to use, callget_facts_engine()

get_facts_engine(facts_file)#> [1] "run_engine_contin"

Run a single scenario

If we take control of the simulation process, we can pick and choose which FACTS simulation scenarios to run and read.

# Example FACTS file built into rfacts.facts_file<-get_facts_file_example("contin.facts")# Set up the files for the scenarios.param_files<-run_flfll(facts_file, verbose=FALSE)# Each scenario has its own folder with internal parameter files.scenarios<-get_param_dirs(param_files)# not in rfacts <= 1.0.0scenarios#> [1] "/tmp/RtmpFv78Hj/file427b68486ae6/contin/acc1_drop1_resp1_params"#> [2] "/tmp/RtmpFv78Hj/file427b68486ae6/contin/acc1_drop1_resp2_params"#> [3] "/tmp/RtmpFv78Hj/file427b68486ae6/contin/acc2_drop1_resp1_params"#> [4] "/tmp/RtmpFv78Hj/file427b68486ae6/contin/acc2_drop1_resp2_params"# Let's pick one of those scenarios and run the simulations.scenario<-scenarios[1]run_engine_contin(scenario, n_sims=2, verbose=FALSE)read_patients(scenario)#> # A tibble: 600 x 15#>    facts_file facts_scenario facts_sim facts_id facts_output facts_csv#>    <chr>      <chr>              <int> <chr>    <chr>        <chr>#>  1 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  2 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  3 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  4 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  5 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  6 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  7 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  8 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  9 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#> 10 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#> # … with 590 more rows, and 9 more variables: facts_header <chr>,#> #   subject <int>, region <int>, date <dbl>, dose <int>, lastvisit <int>,#> #   dropout <int>, baseline <lgl>, visit_1 <dbl>

Parallel computing

rfacts makes it straightforward to parallelize across simulations. First, userun_flfll() to create a directory of param files. Be sure to supply anoutput_path that all the parallel workers can access (e.g. notempfile()s).

library(rfacts)facts_file<-get_facts_file_example("contin.facts")param_files<-file.path(getwd(),"param_files")run_flfll(facts_file,param_files)#> [1] "/home/c240390/projects/rfacts/param_files"

Next, write a custom function that accepts the param files, runs a single simulation for each param file, and returns the important data in memory. Be sure to set a unique seed for each simulation iteration.

sim_once<-function(iter,param_files){# Copy param files to a temp file in order to# (1) Avoid race conditions in parallel processing, and# (2) Make things run faster: temp files are on local node storage.out<-tempfile()fs::dir_copy(path=param_files, new_path=out)# Run the engine once per param file.run_engine_contin(out, n_sims=1L, seed=iter)# Return aggregated patients files.read_patients(out)# Reads fast because `out` is a tempfile().}

At this point, we should test this function locally without parallel computing.

library(dplyr)# All the patients files were named patients00001.csv,# so do not trust the facts_sim column.# For data post-processing, use the facts_id column instead.lapply(seq_len(4),sim_once, param_files=param_files)%>%bind_rows()#> # A tibble: 4,800 x 15#>    facts_file facts_scenario facts_sim facts_id facts_output facts_csv#>    <chr>      <chr>              <int> <chr>    <chr>        <chr>#>  1 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  2 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  3 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  4 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  5 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  6 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  7 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  8 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#>  9 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#> 10 contin.fa… acc1_drop1_re…         1 file427… patients     /tmp/Rtm…#> # … with 4,790 more rows, and 9 more variables: facts_header <chr>,#> #   subject <int>, region <int>, date <dbl>, dose <int>, lastvisit <int>,#> #   dropout <int>, baseline <lgl>, visit_1 <dbl>

Parallel computing happens when we callsim_once() repeatedly over several parallel workers. A powerful and convenient parallel computing solution isclustermq. Here is a sketch of how to use it withrfacts.mclapply() from theparallel package is a quick and dirty alternative.

# Configure clustermq to use our grid and your template file.# If you are using a scheduler like SGE, you need to write a template file# like clustermq.tmpl. To learn how, visit# https://mschubert.github.io/clustermq/articles/userguide.html#configuration-1options(clustermq.scheduler="sge", clustermq.template="clustermq.tmpl")# Run the computation.library(clustermq)patients<-Q(  fun=sim_once,  iter=seq_len(50),  const=list(params=params),  pkgs=c("fs","rfacts"),  n_jobs=4)%>%bind_rows()# Show aggregated patient data.patients

Alternatives toclustermq includeparallel::mclapply(),furrr::future_map(), andfuture.apply::future_lapply().

Helpers

Variousget_facts_*() functions interrogate FACTS files.

get_facts_scenarios(facts_file)#> [1] "acc1_drop1_resp1" "acc1_drop1_resp2" "acc2_drop1_resp1" "acc2_drop1_resp2"get_facts_version(facts_file)#> [1] "6.2.5.22668"get_facts_versions()#> [1] "6.3.1"   "6.2.5"   "6.0.0.1"