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These are client side functions for building survival models, Cox proportional hazards models and Cox regression models.\n\nA tutorial in bookdown format with executable code is available here:\n\nhttps://neelsoumya.github.io/dsSurvivalbookdown/\n\n\n\nDataSHIELD 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.\n\n* The server side package is called `dsSurvival`\n\n    * https://github.com/neelsoumya/dsSurvival\n\n* The client side package is called `dsSurvivalClient`\n\n    * https://github.com/neelsoumya/dsSurvivalClient\n\n\nIf you use the code, please cite the following manuscript:\n\nBanerjee 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.\n\nhttps://www.biorxiv.org/content/10.1101/2022.01.04.471418v2\n\nhttps://doi.org/10.1101/2022.01.04.471418\n\nhttps://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-022-06085-1\n\nA bib file is available here:\n\nhttps://github.com/neelsoumya/dsSurvival/blob/main/CITATION.bib\n\n\n## Quick start\n\nInstall R \n\n   https://www.r-project.org/\n\nand R Studio \n\n   https://www.rstudio.com/products/rstudio/download/preview/\n\n\nInstall the following packages:\n\n\n```r \ninstall.packages('devtools')\nlibrary(devtools)\ndevtools::install_github('neelsoumya/dsSurvivalClient')\ndevtools::install_github('datashield/dsBaseClient@6.1.1')\ninstall.packages('rmarkdown')\ninstall.packages('knitr')\ninstall.packages('tinytex')\ninstall.packages('metafor')\ninstall.packages('DSOpal')\ninstall.packages('DSI')\ninstall.packages('opalr')\ninstall.packages('patchwork')\n```\n\n\nFollow the tutorial in bookdown format with executable code:\n\nhttps://neelsoumya.github.io/dsSurvivalbookdown/\n\nThis uses the Opal demo server which has all server-side packages preinstalled\n\nhttps://opal-sandbox.mrc-epid.cam.ac.uk/\n\n\nYou can also see the script `simple_script.R`\n\nhttps://github.com/neelsoumya/dsSurvival/blob/main/vignettes/simple_script.R\n\n\n## Installation\n\n* Install R Studio and the development environment as described below:\n\n    * https://data2knowledge.atlassian.net/wiki/spaces/DSDEV/pages/12943461/Getting+started\n\n\n* Install the virtual machines as described below:\n\n    * https://data2knowledge.atlassian.net/wiki/spaces/DSDEV/pages/931069953/Installation+Training+Hub-+DataSHIELD+v6\n\n    * https://data2knowledge.atlassian.net/wiki/spaces/DSDEV/pages/1657634881/Testing+100+VM\n\n    * https://data2knowledge.atlassian.net/wiki/spaces/DSDEV/pages/1657634898/Tutorial+6.1.0+100+VM\n\n    * https://hub.docker.com/layers/rock-base/datashield/rock-base/6.2-R4.2/images/sha256-2b1ae879a4387e1dac6843ea59ac1db61816ee78467c9d30d6769a2aed330b0e?context=explore\n\n* 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\n\n![Screenshot of installation of package in VM](https://github.com/neelsoumya/dsSurvivalClient/blob/main/project/Capture_VM_install_screenshot.PNG)\n\n\nPlease see the link below on how to install a package in Opal\n\nhttps://opaldoc.obiba.org/en/latest/web-user-guide/administration/datashield.html#add-package\n\n\n```r\n\ninstall.packages('devtools')\n\nlibrary(devtools)\n\ndevtools::install_github('neelsoumya/dsBaseClient')\n\ndevtools::install_github('neelsoumya/dsSurvivalClient')\n\n```\n\nIf you want to use a certain release then you can do the following\n\n```r\n\nlibrary(devtools)\n\ndevtools::install_github('neelsoumya/dsSurvivalClient@v1.0.0')\n\n```\n\nIf you want to try privacy preserving survival curves (available in v2.0), you can use the main branch or you can do the following\n\n```r\n\nlibrary(devtools)\n\ndevtools::install_github('neelsoumya/dsSurvivalClient', ref = 'privacy_survival_curves')\n\n```\n\n\nor\n\n```r\n\nlibrary(devtools)\n\ndevtools::install_github('neelsoumya/dsSurvivalClient@v2.1.3')\n\n```\n\n\n\n## Usage\n\nA tutorial in bookdown format is available here: \n\nhttps://neelsoumya.github.io/dsSurvivalbookdown/\n\n\n\nA screenshot of meta-analyzed hazard ratios from a survival model is shown below.\n\n![Meta-analyzed hazard ratios from survival models](https://github.com/neelsoumya/dsSurvivalClient/blob/main/project/screenshot_survival_models.png)\n\nFor polished publication ready plots, use the following script `forestplot_FINAL.R`\n\n   https://github.com/neelsoumya/dsSurvival/blob/main/forestplot_FINAL.R\n   \nor the script `simple_script.R`\n\nhttps://github.com/neelsoumya/dsSurvival/blob/main/vignettes/simple_script.R\n\n\nIf you want to learn the basics of survival models, see the following repository:\n\nhttps://github.com/neelsoumya/survival_models\n\n\nIf you want to learn coding models in DataSHIELD, see the following repository:\n\nhttps://github.com/neelsoumya/dsMiscellaneous\n\n\n## Release notes\n\nv1.0.0: 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.\n\nA 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.\n\n\n* https://github.com/isglobal-brge/ShinyDataSHIELD\n\n* https://isglobal-brge.github.io/ShinyDataSHIELD_bookdown/\n\n* https://isglobal-brge.github.io/ShinyDataSHIELD_bookdown/functionalities.html#survival-analysis\n\n* https://datashield-demo.obiba.org/\n\n\nv1.0.1: Minor fixes.\n\nv2.0.0: This release has privacy preserving survival curves.\n\nv2.1.1: This has minor fixes.\n\nv2.1.2: This has minor fixes.\n\nv2.1.3: This has minor fixes, fixes for plotting of a stratified survival analysis and use of ggplot in plotting survival curves.\n\n\n## Acknowledgements\n\nWe acknowledge the help and support of the DataSHIELD technical team.\nWe 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.\n\nWe 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.\n\n## Contact\n\n* Soumya Banerjee, Demetris Avraam, Paul Burton, Xavier Escriba Montagut, Juan Gonzalez, Tom R. P. Bishop and DataSHIELD technical team\n\n* sb2333@cam.ac.uk\n\n* DataSHIELD \n\n    * DataSHIELD is a platform that enables the non-disclosive analysis of distributed sensitive data \n\n    * https://www.datashield.ac.uk\n    \n    \n## Citation\n\nIf you use the code, please cite the following manuscript:\n\nBanerjee 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.\n\nhttps://www.biorxiv.org/content/10.1101/2022.01.04.471418v2\n\nhttps://doi.org/10.1101/2022.01.04.471418 \n\nhttps://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-022-06085-1\n\nA bib file is available here:\n\nhttps://github.com/neelsoumya/dsSurvivalClient/blob/main/project/CITATION.bib\n\n```bibtex\n@article{Banerjee2022,\nauthor = {Banerjee, Soumya and Sofack, Ghislain and Papakonstantinou, Thodoris and Avraam, Demetris and Burton, Paul and Z{\\\"{o}}ller, Daniela and Bishop, Tom RP},\ndoi = {10.1101/2022.01.04.471418},\njournal = {bioRxiv},\nmonth = {jan},\npages = {2022.01.04.471418},\npublisher = {Cold Spring Harbor Laboratory},\ntitle = {{dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD}},\nyear = {2022}\n}\n```\n\n\u003c!--and the following DOI\n\n\n[![DOI](https://zenodo.org/badge/362161720.svg)](https://zenodo.org/badge/latestdoi/362161720)\n\nor see the CITATION.cff file edited using\n\nhttps://citation-file-format.github.io/cff-initializer-javascript/\n\n--\u003e\n\n## Publications\n\nThe following publications describe `dsSurvival`\n\nBanerjee, 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\n\nBanerjee, 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\n\n\nIf you use the code, please cite the following manuscript:\n\nBanerjee, 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\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneelsoumya%2Fdssurvivalclient","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fneelsoumya%2Fdssurvivalclient","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneelsoumya%2Fdssurvivalclient/lists"}