Package: casebase 0.10.6

casebase: Fitting Flexible Smooth-in-Time Hazards and Risk Functions via Logistic and Multinomial Regression

Fit flexible and fully parametric hazard regression models to survival data with single event type or multiple competing causes via logistic and multinomial regression. Our formulation allows for arbitrary functional forms of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival curves for a given covariate profile. This approach accommodates any log-linear hazard function of prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing incidence density via population time plots. Based on the case-base sampling approach of Hanley and Miettinen (2009) <doi:10.2202/1557-4679.1125>, Saarela and Arjas (2015) <doi:10.1111/sjos.12125>, and Saarela (2015) <doi:10.1007/s10985-015-9352-x>.

Authors:Sahir Bhatnagar [aut, cre], Maxime Turgeon [aut], Jesse Islam [aut], Olli Saarela [aut], James Hanley [aut]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
casebase/json (API)

# Install 'casebase' in R:
install.packages('casebase', repos = c('https://sahirbhatnagar.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/sahirbhatnagar/casebase/issues

Pkgdown/docs site:https://sahirbhatnagar.com

Datasets:
  • bmtcrr - Data on transplant patients
  • brcancer - German Breast Cancer Study Group 2
  • eprchd - Estrogen plus Progestin and the Risk of Coronary Heart Disease
  • ERSPC - Data on the men in the European Randomized Study of Prostate Cancer Screening
  • simdat - Simulated data under Weibull model with Time-Dependent Treatment Effect
  • support - Study to Understand Prognoses Preferences Outcomes and Risks of Treatment

On CRAN:

Conda:

competing-riskscox-regressionregression-modelssurvival-analysis

7.45 score 8 stars 120 scripts 4.2k downloads 11 exports 24 dependencies

Last updated from:cbeba938cf. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK219
source / vignettesOK245
linux-release-x86_64OK185
macos-release-arm64OK128
macos-oldrel-arm64OK178
windows-develOK168
windows-releaseOK162
windows-oldrelOK147
wasm-releaseOK107

Exports:absoluteRiskabsoluteRisk.CompRiskcheckArgsEventIndicatorcheckArgsTimeEventfitSmoothHazardfitSmoothHazard.fithazardPlotpopTimeprepareXsampleCaseBasesummary

Dependencies:clicpp11data.tablefarverggplot2gluegtableisobandlabelinglatticelifecycleMatrixmgcvnlmeR6RColorBrewerrlangS7scalessurvivalvctrsVGAMviridisLitewithr

Plot Hazards and Hazard Ratios
Introduction | Hazard Function | One binary predictor, no interactions | Hazard functions on separate plots | Hazard functions on same plots | ggplot2 version | One binary predictor with interaction | One continuous predictor with interaction | One continuous predictor with interaction and several other predictors | Hazard Ratio | Manson Trial (eprchd) | Save results | Session information
Rendered fromplotsmoothHazard.Rmdusingknitr::rmarkdown

Last update: 2024-02-01
Started: 2020-06-19

Time-Varying Covariates
References
Rendered fromtime-varying-covariates.Rmdusingknitr::rmarkdown

Last update: 2023-08-03
Started: 2023-08-03

Population Time Plots
Overview | Load Required Packages | European Randomized Study of Prostate Cancer Screening Data | Exposure Stratified Population Time Plot | Plotting the base series | Stem Cell Data | Plotting the follow-up times for each observation | Plot the Case Series | Plot the Base Series | Plot the Competing event | Stratified by Disease | Change color points and legend labels | Veteran Data | Stratified by treatment population time plot | Stanford Heart Transplant Data | NCCTG Lung Cancer Data | Stratified by gender | Simulated Data Example | Simulate the data | Population Time Plot | Stratified by Binary Covariate z | Session information
Rendered frompopTime.Rmdusingknitr::rmarkdown

Last update: 2021-10-19
Started: 2016-03-02

Competing risk analysis
Data | Population-time plots | Analysis | Absolute risk | Session information | References
Rendered fromcompetingRisk.Rmdusingknitr::rmarkdown

Last update: 2021-02-06
Started: 2016-02-27

Introduction to casebase sampling
Methodological details | Analysis of the veteran dataset | Cumulative Incidence Curves | Session information | References
Rendered fromsmoothHazard.Rmdusingknitr::rmarkdown

Last update: 2021-02-06
Started: 2016-01-06

Customizing Population Time Plots
Setup | Introduction | The .params arguments | Change the Facet Labels | Changing the Plot Aesthetics | Change only the point colors | Change Point Color and Legend Labels | Session information
Rendered fromcustomizingpopTime.Rmdusingknitr::rmarkdown

Last update: 2020-07-03
Started: 2020-03-03

Plot Cumulative Incidence and Survival Curves
Introduction | Analysis of the brcancer dataset | Plotting Cumulative Incidence Curves | Using graphics::matplot | Survival Curves | Other families | glmnet | gam | Session information
Rendered fromplotabsRisk.Rmdusingknitr::rmarkdown

Last update: 2020-06-30
Started: 2020-06-29