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https://github.com/neelsoumya/dssurvival

Survival functions for DataSHIELD. Package for building survival models, Cox proportional hazards models and Cox regression models in DataSHIELD.
https://github.com/neelsoumya/dssurvival

clinical-informatics cox-models cox-regression datashield datashield-technical-team datashield-testing dssurvival meta-analysis r survival-analysis survival-functions survival-models

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Survival functions for DataSHIELD. Package for building survival models, Cox proportional hazards models and Cox regression models in DataSHIELD.

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README 

        
# dsSurvival



[![License](https://img.shields.io/badge/license-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0.html)



## Introduction



`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.



* The client side package is called `dsSurvivalClient`



    * https://github.com/neelsoumya/dsSurvivalClient



* The server side package is called `dsSurvival`



    * https://github.com/neelsoumya/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



```bibtex

@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}

}

```



## Quick start



* Install R 



   https://www.r-project.org/



and R Studio 



   https://www.rstudio.com/products/rstudio/download/preview/



* Install the following packages in R:



```r 

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')

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



## Installation



* 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

    

    * https://hub.docker.com/layers/rock-base/datashield/rock-base/6.2-R4.2/images/sha256-2b1ae879a4387e1dac6843ea59ac1db61816ee78467c9d30d6769a2aed330b0e?context=explore



* 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



![Screenshot of installation of package in VM](https://github.com/neelsoumya/dsSurvivalClient/blob/main/project/Capture_VM_install_screenshot.PNG)



In the newer version of Opal your window may look like the screenshot below



![Screenshot in new version of Opal](newopal_screenshot.png)



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



```r



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



```r



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



```r



library(devtools)



devtools::install_github('neelsoumya/dsSurvivalClient', ref = 'privacy_survival_curves')



```



or



```r



library(devtools)



devtools::install_github('neelsoumya/[email protected]')



```



## Usage



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.



![A screenshot of meta-analyzed hazard ratios from the survival model is shown below](https://github.com/neelsoumya/dsSurvivalClient/blob/main/project/screenshot_survival_models.png)



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:



https://neelsoumya.github.io/dsSurvivalbookdown/computational-workflow.html#plotting-of-privacy-preserving-survival-curves



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



## Release notes



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`.



* https://github.com/isglobal-brge/ShinyDataSHIELD



* https://isglobal-brge.github.io/ShinyDataSHIELD_bookdown/



* https://isglobal-brge.github.io/ShinyDataSHIELD_bookdown/functionalities.html#survival-analysis



* https://datashield-demo.obiba.org/



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.



## Acknowledgements



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.



## Contact



* Soumya Banerjee, Demetris Avraam, Paul Burton, Xavier Escriba Montagut, Juan Gonzalez, Tom R. P. Bishop and DataSHIELD technical team



* [email protected]



* DataSHIELD 



    * DataSHIELD is a platform that enables the non-disclosive analysis of distributed sensitive data 



    * https://www.datashield.ac.uk



## Citation



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



```bibtex

@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}

}



```



## Publications



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