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Distributed implementation of the Cox Proportional Hazards algorithm
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README.md

d_coxph

About

This repository hosts the R implementation of the distributed Cox Proportional Hazards algorithm as described by Lu et al. that can be used in VANTAGE6.

How to use this algorithm?

The following steps assume you have R (and RStudio) and git installed. If you run into trouble, please create an issue in the tracker.

1. Getting the code

First, clone the repository and enter the directory:

git clone https://github.com/IKNL/dcoxph.git
cd dcoxph

2. Installing dependencies

Next, install the required packages in R. Either run the following in bash:

RScript install_packages.R

or run the following in R:

packages <- c(
  "abind",
  "dplyr",
  "httr",
  "rjson"
)

install.packages(packages)

3. Optional: encapsulating the distributed code in a Docker image

The code is split into a local and a distributed part. Both parts are implemented in the same R script dl_coxph.R. The Docker registry at https://docker-registry.distributedlearning.ai already hosts an image with the distributed code. I

f you are using your own installation of the infrastructure, you/the researcher should create a Docker image that holds the distributed code (see also build_docker.sh) and push the image to a (private) Docker registry. This requires Docker to be installed on the machine.

4. Running the algorithm

This step assumes you have access to a central server and know your username, password and collaboration id.

A researcher then runs the analysis by:

  1. Creating a client that communicates with the distributed learning infrastructure
  2. Calling the method dcoxph with the appropriate parameters

This is illustrated by the following R code:

source("Client.R")
source("dl_coxph.R")

# Create a client object to communicate with the server.
client <- Client(host, username, password, collaboration_id)
client$authenticate()

# Parameters used to interpret the hub's datastore
expl_vars <- c("Age","Race2","Race3","Mar2","Mar3","Mar4","Mar5","Mar9",
             "Hist8520","hist8522","hist8480","hist8501","hist8201",
             "hist8211","grade","ts","nne","npn","er2","er4")
time_col <- "Time"
censor_col <- "Censor"

results <- dcoxph(client, expl_vars, time_col, censor_col)

For an overview of the working of the algorithm, see the figure below: Systems overview

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