This is a bookdown with executable code demonstrating how to use the dsSurvival package to create privacy preserving survival models in DataSHIELD. dsSurvival builds privacy preserving survival models.
DataSHIELD is a platform for federated analysis of private data. This package can be used to build survival models, Cox proportional hazards models or Cox regression models.
The complete bookdown is available here:
https://neelsoumya.github.io/dsSurvivalbookdown
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:
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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
@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}
}The complete bookdown, tutorial, vignette with executable code and synthetic data is available here:
https://neelsoumya.github.io/dsSurvivalbookdown
Please 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/dsBaseClient@6.1.1')
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/
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Install R and R Studio
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In R, install the following packages
install.packages('devtools')
library(devtools)
devtools::install_github('neelsoumya/dsSurvivalClient')
install.packages('bookdown')
devtools::install_github('datashield/dsBaseClient@6.1.1')
install.packages('rmarkdown')
install.packages('knitr')
install.packages('tinytex')
install.packages('metafor')
install.packages('DSOpal')
install.packages('DSI')
install.packages('opalr')
or
R --no-save < installer_R.R
or
run the following script in R installer_R.R
Install R Studio and the development environment as described below:
https://data2knowledge.atlassian.net/wiki/spaces/DSDEV/pages/12943461/Getting+started
Install the virtual machines as described below:
https://data2knowledge.atlassian.net/wiki/spaces/DSDEV/pages/931069953/Installation+Training+Hub-+DataSHIELD+v6
https://data2knowledge.atlassian.net/wiki/spaces/DSDEV/pages/1657634881/Testing+100+VM
https://data2knowledge.atlassian.net/wiki/spaces/DSDEV/pages/1657634898/Tutorial+6.1.0+100+VM
Install dsBase and dsSurvival on Opal server in the Virtual Machine (type neelsoumya/dsSurvival and main in the textboxes)
See the bookdown below for a complete tutorial:
https://neelsoumya.github.io/dsSurvivalbookdown
A minimal example of a book based on R Markdown and bookdown (https://github.com/rstudio/bookdown).
The bookdown can be compiled by typing the following commands:
library(bookdown)
bookdown::serve_book()
Soumya Banerjee and Tom R.P. Bishop
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
@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}
}