Package: eventglm 1.4.5

eventglm: Regression Models for Event History Outcomes

A user friendly, easy to understand way of doing event history regression for marginal estimands of interest, including the cumulative incidence and the restricted mean survival, using the pseudo observation framework for estimation. For a review of the methodology, see Andersen and Pohar Perme (2010) <doi:10.1177/0962280209105020> or Sachs and Gabriel (2022) <doi:10.18637/jss.v102.i09>. The interface uses the well known formulation of a generalized linear model and allows for features including plotting of residuals, the use of sampling weights, and corrected variance estimation.

Authors:Michael C Sachs [aut, cre], Erin E Gabriel [aut], Morten Overgaard [ctb], Thomas A Gerds [ctb], Terry Therneau [ctb]

eventglm_1.4.5.tar.gz
eventglm_1.4.5.zip(r-4.7-x86_64)eventglm_1.4.5.zip(r-4.7-arm64)eventglm_1.4.5.zip(r-4.6-x86_64)eventglm_1.4.5.zip(r-4.6-arm64)eventglm_1.4.5.zip(r-4.5-x86_64)
eventglm_1.4.5.tgz(r-4.6-x86_64)eventglm_1.4.5.tgz(r-4.6-arm64)eventglm_1.4.5.tgz(r-4.5-x86_64)eventglm_1.4.5.tgz(r-4.5-arm64)
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eventglm_1.4.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
eventglm/json (API)

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

Bug tracker:https://github.com/sachsmc/eventglm/issues

Pkgdown/docs site:https://sachsmc.github.io

Datasets:
  • colon - Chemotherapy for Stage B/C colon cancer
  • mgus2 - Monoclonal gammopathy data

On CRAN:

Conda:

5.92 score 6 stars 1 packages 31 scripts 390 downloads 14 exports 29 dependencies

Last updated from:c112fcaf02. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK144
linux-devel-x86_64OK161
source / vignettesOK255
linux-release-arm64OK138
linux-release-x86_64OK195
macos-release-arm64OK133
macos-release-x86_64OK323
macos-oldrel-arm64OK135
macos-oldrel-x86_64OK257
windows-devel-arm64OK229
windows-devel-x86_64OK153
windows-release-arm64OK350
windows-release-x86_64OK108
windows-oldrel-x86_64OK124
wasm-releaseOK114

Exports:cumincglmjackknife.competing.risks2jackknife.survival2leaveOneOut.competing.risksleaveOneOut.competing.risks2leaveOneOut.survivalleaveOneOut.survival2pseudo_aaregpseudo_coxphpseudo_independentpseudo_infjackpseudo_stratifiedrmeanglmSurv

Dependencies:backportsbroomclicpp11dplyrgeepackgenericsgluelatticelifecyclemagrittrMASSMatrixpillarpkgconfigpurrrR6rlangsandwichstringistringrsurvivaltibbletidyrtidyselectutf8vctrswithrzoo

Examples of using eventglm and interpreting the results
Colon cancer treatment and time to death (standard survival) | Models for survival | Multiple time points (New in version 1.2.0) | Censoring assumptions | Monoclonal gammopathy data (Competing risks) | Relative survival/net survival | Case cohort sampling | References
Rendered fromexample-analysis.Rmdusingknitr::rmarkdown

Last update: 2024-02-22
Started: 2020-08-26

Comparison to other software
R code and results | Stata code and results | SAS code and results
Rendered fromstata-sas-comparason.Rmdusingknitr::rmarkdown

Last update: 2022-09-04
Started: 2020-12-11

Extending eventglm
Pseudo observation computation modules | Custom computation functions | Example: Parametric pseudo observations | Example 2: infinitesimal jackknife | Conclusion
Rendered fromextenstions.Rmdusingknitr::rmarkdown

Last update: 2022-09-04
Started: 2021-01-23

Readme and manuals

Help Manual

Help pageTopics
Compute inverse probability of censoring weights pseudo observationscalc_ipcw_pos
Error check censoring modelcheck_mod_cens
Chemotherapy for Stage B/C colon cancercolon
Confidence Intervals for pseudoglm Model Parametersconfint.pseudoglm
Generalized linear models for cumulative incidencecumincglm
Regression Models for Event History Outcomeseventglm
Utility to get jackknife pseudo observations of cumulative incidenceget_pseudo_cuminc
Utility to get jackknife pseudo observations of restricted meanget_pseudo_rmean
Compute jackknife pseudo-observations of the cause-specific cumulative incidence for competing risksjackknife.competing.risks2
Compute jackknife pseudo-observations of the survival functionjackknife.survival2
Compute jackknife pseudo-observations of the cause-specific cumulative incidence for competing risksleaveOneOut.competing.risks
Compute jackknife pseudo-observations of the cause-specific cumulative incidence for competing risksleaveOneOut.competing.risks2
Compute leave one out jackknife contributions of the survival functionleaveOneOut.survival
Compute leave one out jackknife contributions of the survival functionleaveOneOut.survival2
Match cause specification against model responsematch_cause
Monoclonal gammopathy datamgus2
Print method for pseudoglmprint.pseudoglm
Compute censoring weighted pseudo observationspseudo_aareg
Compute censoring weighted pseudo observationspseudo_coxph
Compute pseudo observations under independent censoringpseudo_independent
Compute infinitesimal jackknife pseudo observationspseudo_infjack
Compute pseudo-observations for the restricted mean survivalpseudo_rmst2
Compute pseudo observations using stratified jackknifepseudo_stratified
Pseudo-observation scaled residualsresiduals.pseudoglm
Generalized linear models for the restricted mean survivalrmeanglm
Summary methodsummary.pseudoglm
Compute covariance matrix of regression coefficient estimatesvcov.pseudoglm