dsSurvival is a package for building survival functions for DataSHIELD (a platform for federated analysis of private data). These are server side functions for 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 in R 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 client side package is called
dsSurvivalClient -
The server side package is called
dsSurvival
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 bibliography 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}
}-
Install R
and R Studio
https://www.rstudio.com/products/rstudio/download/preview/
- Install the following packages in R:
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')
install.packages('patchwork')- Follow the tutorial in bookdown format with executable code and synthetic data:
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
-
Install R Studio and the development environment as described below:
-
Install the virtual machines as described below:
-
Install
dsBaseanddsSurvivalon Opal server in the Virtual Machine (type neelsoumya/dsSurvival and main in the textboxes) as shown in the screenshot below
In the newer version of Opal your window may look like the screenshot below
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
If you have an older version of Opal, please use this version by Stuart Wheater
https://github.com/StuartWheater/dsSurvival
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/dsSurvivalClient@v1.0.0')
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/dsSurvivalClient@v2.1.3')
A tutorial with executable code in bookdown format is available here:
https://neelsoumya.github.io/dsSurvivalbookdown/
A screenshot of the 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
or the script simple_script.R
https://github.com/neelsoumya/dsSurvival/blob/main/vignettes/simple_script.R
If you want to plot survival curves or Kaplan-Meier curves, see the following link:
If you want to learn the basics of survival models, please see the following repository:
https://github.com/neelsoumya/survival_models
If you want to learn coding models in DataSHIELD, see the following repository:
https://github.com/neelsoumya/dsMiscellaneous
v1.0.0: A basic first 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 (SLMA).
A shiny graphical user interface for building survival models in DataSHIELD has also been created by Xavier Escriba Montagut and Juan Gonzalez. It uses dsSurvival and dsSurvivalClient.
v1.0.1: Minor fixes.
v2.0.0: This has privacy preserving survival curves.
v2.1.0: This has vcov() functionality.
v2.1.1: This has minor fixes.
v2.1.2: This has minor fixes.
v2.1.3: This has minor fixes, fixes for plotting of a stratified survival analysis and use of ggplot in plotting 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 Yannick Marcon and @StuartWheater for fixes, @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/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 following publications describe dsSurvival
Banerjee, S., Sofack, G.N., Papakonstantinou, T. et al. dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD. BMC Res Notes 15, 197 (2022). https://doi.org/10.1186/s13104-022-06085-1
Banerjee, S., Bishop, T.R.P. dsSurvival 2.0: privacy enhancing survival curves for survival models in the federated DataSHIELD analysis system. BMC Res Notes 16, 98 (2023). https://doi.org/10.1186/s13104-023-06372-5
If you use the code, please cite the following manuscript:
Banerjee, S., Sofack, G.N., Papakonstantinou, T. et al. dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD. BMC Res Notes 15, 197 (2022). https://doi.org/10.1186/s13104-022-06085-1