dsSurvivalClient is a package for building survival functions (client side) in DataSHIELD (a platform for federated analysis of private data). These are client side functions for building survival models, Cox proportional hazards models and Cox regression models.
A tutorial in bookdown format with executable code is available here:
https://neelsoumya.github.io/dsSurvivalbookdown/
DataSHIELD is a platform for federated analysis of private data. DataSHIELD has a client-server architecture and this package has a client side and server side component.
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The server side package is called
dsSurvival -
The client side package is called
dsSurvivalClient
If you use the code, please cite the following manuscript:
Banerjee S, Sofack G, Papakonstantinou T, Avraam D, Burton P, et al. (2022), dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD, bioRxiv: 2022.01.04.471418.
https://www.biorxiv.org/content/10.1101/2022.01.04.471418v2
https://doi.org/10.1101/2022.01.04.471418
https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-022-06085-1
A bib file is available here:
https://github.com/neelsoumya/dsSurvival/blob/main/CITATION.bib
Install R
and R Studio
https://www.rstudio.com/products/rstudio/download/preview/
Install the following packages:
install.packages('devtools')
library(devtools)
devtools::install_github('neelsoumya/dsSurvivalClient')
devtools::install_github('datashield/[email protected]')
install.packages('rmarkdown')
install.packages('knitr')
install.packages('tinytex')
install.packages('metafor')
install.packages('DSOpal')
install.packages('DSI')
install.packages('opalr')Follow the tutorial in bookdown format with executable code:
https://neelsoumya.github.io/dsSurvivalbookdown/
This uses the Opal demo server which has all server-side packages preinstalled
https://opal-sandbox.mrc-epid.cam.ac.uk/
You can also see the script simple_script.R
https://github.com/neelsoumya/dsSurvival/blob/main/vignettes/simple_script.R
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Install R Studio and the development environment as described below:
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Install the virtual machines as described below:
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Install dsBase and dsSurvival on Opal server in the Virtual Machine (type neelsoumya/dsSurvival and main in the textboxes) as shown in the screenshot below
Please see the link below on how to install a package in Opal
https://opaldoc.obiba.org/en/latest/web-user-guide/administration/datashield.html#add-package
install.packages('devtools')
library(devtools)
devtools::install_github('neelsoumya/dsBaseClient')
devtools::install_github('neelsoumya/dsSurvivalClient')
If you want to use a certain release then you can do the following
library(devtools)
devtools::install_github('neelsoumya/[email protected]')
If you want to try privacy preserving survival curves (available in v2.0), you can use the main branch or you can do the following
library(devtools)
devtools::install_github('neelsoumya/dsSurvivalClient', ref = 'privacy_survival_curves')
or
library(devtools)
devtools::install_github('neelsoumya/[email protected]')
A tutorial in bookdown format is available here:
https://neelsoumya.github.io/dsSurvivalbookdown/
A screenshot of meta-analyzed hazard ratios from a survival model is shown below.
For polished publication ready plots, use the following script forestplot_FINAL.R
https://github.com/neelsoumya/dsSurvival/blob/main/forestplot_FINAL.R
If you want to learn the basics of survival models, see the following repository:
https://github.com/neelsoumya/survival_models
v1.0.0: A basic release of survival models in DataSHIELD. This release has Cox proportional hazards models, summaries of models, diagnostics and the ability to meta-analyze hazard ratios. There is also capability to generate forest plots of meta-analyzed hazard ratios. This release supports study-level meta-analysis.
A shiny graphical user interface for building survival models in DataSHIELD has also been created by Xavier Escriba Montagut and Juan Gonzalez. It calls dsSurvival and dsSurvivalClient.
v1.0.1: Minor fixes.
v2.0.0: This release has privacy preserving survival curves.
We acknowledge the help and support of the DataSHIELD technical team. We are especially grateful to Elaine Smith, Eleanor Hyde, Shareen Tan, Stuart Wheater, Yannick Marcon, Paul Burton, Demetris Avraam, Patricia Ryser-Welch, Kevin Rue-Albrecht, Maria Gomez Vazquez and Wolfgang Viechtbauer for fruitful discussions and feedback.
We thank @joerghenkebuero for suggestions about documentation, @AlanRace and Stefan Buchka for bug fixes and Xavier Escriba Montagut for a fix to the plotting functionality.
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Soumya Banerjee, Demetris Avraam, Paul Burton, Xavier Escriba Montagut, Juan Gonzalez, Tom R. P. Bishop and DataSHIELD technical team
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DataSHIELD
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DataSHIELD is a platform that enables the non-disclosive analysis of distributed sensitive data
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If you use the code, please cite the following manuscript:
Banerjee S, Sofack G, Papakonstantinou T, Avraam D, Burton P, et al. (2022), dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD, bioRxiv: 2022.01.04.471418.
https://www.biorxiv.org/content/10.1101/2022.01.04.471418v2
https://doi.org/10.1101/2022.01.04.471418
https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-022-06085-1
A bib file is available here:
https://github.com/neelsoumya/dsSurvivalClient/blob/main/project/CITATION.bib
@article{Banerjee2022,
author = {Banerjee, Soumya and Sofack, Ghislain and Papakonstantinou, Thodoris and Avraam, Demetris and Burton, Paul and Z{\"{o}}ller, Daniela and Bishop, Tom RP},
doi = {10.1101/2022.01.04.471418},
journal = {bioRxiv},
month = {jan},
pages = {2022.01.04.471418},
publisher = {Cold Spring Harbor Laboratory},
title = {{dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD}},
year = {2022}
}
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