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riskRegression

R package for risk regression and prediction with censored data

https://github.com/tagteam/riskregression

Science Score: 49.0%

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    CITATION.cff file
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    Found 2 DOI reference(s) in README
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    5 of 24 committers (20.8%) from academic institutions
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    Low similarity (10.6%) to scientific vocabulary
Last synced: 6 months ago · JSON representation

Repository

R package for risk regression and prediction with censored data

Basic Info
  • Host: GitHub
  • Owner: tagteam
  • Language: R
  • Default Branch: master
  • Size: 16 MB
Statistics
  • Stars: 51
  • Watchers: 10
  • Forks: 20
  • Open Issues: 3
  • Releases: 0
Created almost 11 years ago · Last pushed 6 months ago
Metadata Files
Readme

README.md

R/riskRegression: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks

CRAN\_Status\_Badge R-CMD-check

Implementation of the following methods for event history analysis: Risk regression models for survival endpoints also in the presence of competing risks are fitted using binomial regression based on a time sequence of binary event status variables. A formula interface for the Fine-Gray regression model and an interface for the combination of cause-specific Cox regression models. A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under the ROC curve for binary possibly time-dependent outcome. Inverse probability of censoring weighting and pseudo values are used to deal with right censored data. Lists of risk markers and lists of risk models are assessed simultaneously. Cross-validation repeatedly splits the data, trains the risk prediction models on one part of each split and then summarizes and compares the performance across splits.

Installation

{.r org-language="R" exports="both" eval="never"} library(devtools) install_github("tagteam/riskRegression")

References

The following references provide the methodological framework for the features of riskRegression.

  1. T.A. Gerds and M.W. Kattan (2021). Medical Risk Prediction Models: With Ties to Machine Learning (1st ed.) Chapman and Hall/CRC https://doi.org/10.1201/9781138384484

  2. T.A. Gerds and M. Schumacher. Consistent estimation of the expected Brier score in general survival models with right-censored event times. Biometrical Journal, 48(6):1029--1040, 2006.

  3. T.A. Gerds and M. Schumacher. Efron-type measures of prediction error for survival analysis. Biometrics, 63(4):1283--1287, 2007.

  4. T.A. Gerds, T. Cai, and M. Schumacher. The performance of risk prediction models. Biometrical Journal, 50(4):457--479, 2008.

  5. U B Mogensen, H. Ishwaran, and T A Gerds. Evaluating random forests for survival analysis using prediction error curves. Journal of Statistical Software, 50(11), 2012.

  6. P. Blanche, J-F Dartigues, and H. Jacqmin-Gadda. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Statistics in Medicine, 32(30): 5381--5397, 2013.

  7. Paul Blanche, Ce\'cile Proust-Lima, Lucie Loube`re, Claudine Berr, Jean- Franc,ois Dartigues, and He\'le`ne Jacqmin-Gadda. Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks. Biometrics, 71 (1):102--113, 2015.

Functions predict.CauseSpecificCox{.verbatim}, predictCox{.verbatim} and iidCox{.verbatim}:

  • Brice Ozenne, Anne Lyngholm Sorensen, Thomas Scheike, Christian Torp-Pedersen and Thomas Alexander Gerds. riskRegression: Predicting the Risk of an Event using Cox Regression Models. The R Journal (2017) 9:2, pages 440-460.

```{=latex} @article{gerds2006consistent, title = {Consistent Estimation of the Expected {B}rier Score in General Survival Models with Right-Censored Event Times}, author = {Gerds, T.A. and Schumacher, M.}, journal = {Biometrical Journal}, volume = 48, number = 6, pages = {1029--1040}, year = 2006, publisher = {Wiley Online Library} }

@article{gerds2007efron, title = {Efron-Type Measures of Prediction Error for Survival Analysis}, author = {Gerds, T.A. and Schumacher, M.}, journal = {Biometrics}, volume = 63, number = 4, pages = {1283--1287}, year = 2007, publisher = {Wiley Online Library} }

@article{gerds2008performance, title = {The performance of risk prediction models}, author = {Gerds, T.A. and Cai, T. and Schumacher, M.}, journal = {Biometrical Journal}, volume = 50, number = 4, pages = {457--479}, year = 2008, publisher = {Wiley Online Library} }

@Article{mogensen2012pec, title = {Evaluating random forests for survival analysis using prediction error curves}, author = {Mogensen, U B and Ishwaran, H. and Gerds, T A}, journal = {Journal of Statistical Software}, year = 2012, volume = 50, number = 11 }

@article{Blanche2013statmed, title = "{Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks}", author = {Blanche, P. and Dartigues, J-F and Jacqmin-Gadda, H.}, journal = {Statistics in Medicine}, volume = 32, number = 30, pages = {5381--5397}, year = 2013 }

@article{blanche2015, title = {Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks}, author = {Blanche, Paul and Proust-Lima, C{\'e}cile and Loub{`e}re, Lucie and Berr, Claudine and Dartigues, Jean-Fran{\c{c}}ois and Jacqmin-Gadda, H{\'e}l{`e}ne}, journal = {Biometrics}, volume = 71, number = 1, pages = {102--113}, year = 2015, publisher = {Wiley Online Library} }

@article{ozenne2017, title = {riskRegression: Predicting the Risk of an Event using Cox Regression Modelss}, author = {Ozenne, Brice and Sørensen, Anne Lyngholm and Scheike, Thomas and Torp-Pedersen, Christian and Gerds, Thomas Alexander}, journal = {The R Journal}, volume = 9, number = 2, pages = {440--460}, year = 2017 } ```

Owner

  • Name: Thomas Alexander Gerds
  • Login: tagteam
  • Kind: user

GitHub Events

Total
  • Issues event: 9
  • Watch event: 6
  • Member event: 1
  • Issue comment event: 14
  • Push event: 28
  • Fork event: 1
Last Year
  • Issues event: 9
  • Watch event: 6
  • Member event: 1
  • Issue comment event: 14
  • Push event: 28
  • Fork event: 1

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 1,363
  • Total Committers: 24
  • Avg Commits per committer: 56.792
  • Development Distribution Score (DDS): 0.621
Past Year
  • Commits: 139
  • Committers: 8
  • Avg Commits per committer: 17.375
  • Development Distribution Score (DDS): 0.403
Top Committers
Name Email Commits
Thomas Alexander Gerds t****g@b****k 517
eestet75 j****h@h****m 291
bozenne b****z@s****k 215
Brice Maxime Hugues Ozenne h****2@s****k 177
Brice Maxime Hugues Ozenne h****2@k****k 35
tag t****g@e****d 35
muschellij2 m****2@g****m 27
Brice Ozenne b****e@o****r 21
Johan Sebastian Ohlendorff 6****5 9
Anders Munch a****n@g****m 7
Jesse j****m@g****m 4
Brice Ozenne b****e@b****e 4
Nikolaj Tollenaar n****l@g****m 4
bozenne b****e@g****m 3
paulowhite p****e@g****m 3
Marvin Wright g****b@w****e 2
rnmorte r****m@h****k 2
Alessandro Gasparini l****n@g****m 1
scheike s****e@e****d 1
tagteam t****m@e****d 1
Klaus K. Holst k****t 1
Anne Lyngholm Sørensen l****2@s****k 1
Lukas Burk j****2 1
kattanm 6****m 1
Committer Domains (Top 20 + Academic)
sund.root.ku.dk: 2 hst.aau.dk: 1 wrig.de: 1 briceozenne-pc76.home: 1 orange.fr: 1 ku.dk: 1 sund.ku.dk: 1 biostat.ku.dk: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 60
  • Total pull requests: 8
  • Average time to close issues: 11 days
  • Average time to close pull requests: 17 days
  • Total issue authors: 45
  • Total pull request authors: 6
  • Average comments per issue: 2.57
  • Average comments per pull request: 0.75
  • Merged pull requests: 8
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 7
  • Pull requests: 0
  • Average time to close issues: 19 days
  • Average time to close pull requests: N/A
  • Issue authors: 7
  • Pull request authors: 0
  • Average comments per issue: 1.57
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • RobinDenz1 (6)
  • ellessenne (3)
  • jemus42 (3)
  • jfiksel (3)
  • erikvona (2)
  • drs81 (2)
  • NikKrieger (2)
  • BenGoudsmit (2)
  • millgreg (1)
  • DzenisKoca (1)
  • findaz (1)
  • loffleraSMH (1)
  • peter-austin (1)
  • sahasud1 (1)
  • krzyzinskim (1)
Pull Request Authors
  • ellessenne (3)
  • kkholst (1)
  • muschellij2 (1)
  • jemus42 (1)
  • mnwright (1)
  • Jesse-Islam (1)
Top Labels
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 12,127 last-month
  • Total docker downloads: 45,852
  • Total dependent packages: 15
  • Total dependent repositories: 22
  • Total versions: 28
  • Total maintainers: 1
cran.r-project.org: riskRegression

Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks

  • Versions: 28
  • Dependent Packages: 15
  • Dependent Repositories: 22
  • Downloads: 12,127 Last month
  • Docker Downloads: 45,852
Rankings
Dependent packages count: 4.3%
Downloads: 4.5%
Forks count: 5.2%
Dependent repos count: 5.9%
Average: 5.9%
Docker downloads count: 7.4%
Stargazers count: 8.1%
Maintainers (1)
tag@biostat.ku.dk
Last synced: 6 months ago

Dependencies

.github/workflows/R-CMD-check.yaml actions
  • actions/checkout v3 composite
  • r-lib/actions/check-r-package v2 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite
DESCRIPTION cran
  • R >= 3.5.0 depends
  • Publish * imports
  • Rcpp * imports
  • cmprsk * imports
  • data.table >= 1.12.2 imports
  • doParallel * imports
  • foreach * imports
  • ggplot2 >= 3.1.0 imports
  • graphics * imports
  • lattice * imports
  • lava >= 1.6.5 imports
  • mets * imports
  • mvtnorm * imports
  • parallel * imports
  • plotrix * imports
  • prodlim >= 2018.4.18 imports
  • ranger * imports
  • rms >= 5.1.3 imports
  • stats * imports
  • survival >= 2.44.1 imports
  • timereg >= 1.9.3 imports
  • R.rsp * suggests
  • SuperLearner * suggests
  • boot * suggests
  • casebase * suggests
  • flexsurv * suggests
  • gbm * suggests
  • glmnet * suggests
  • grpreg * suggests
  • hal9001 * suggests
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  • mstate * suggests
  • nnls * suggests
  • numDeriv * suggests
  • pROC * suggests
  • party * suggests
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  • penalized * suggests
  • randomForest * suggests
  • randomForestSRC * suggests
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  • testthat * suggests