Package: JMH 1.0.3
JMH: Joint Model of Heterogeneous Repeated Measures and Survival Data
Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data in the presence of heterogeneous within-subject variability, proposed by Li and colleagues (2023) <arxiv:2301.06584>. The proposed method models the within-subject variability of the biomarker and associates it with the risk of the competing risks event. The time-to-event data is modeled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal outcome is modeled using a mixed-effects location and scale model. The association is captured by shared random effects. The model is estimated using an Expectation Maximization algorithm.
Authors:
JMH_1.0.3.tar.gz
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JMH_1.0.3.tgz(r-4.5-x86_64)JMH_1.0.3.tgz(r-4.5-arm64)JMH_1.0.3.tgz(r-4.4-x86_64)JMH_1.0.3.tgz(r-4.4-arm64)JMH_1.0.3.tgz(r-4.3-x86_64)JMH_1.0.3.tgz(r-4.3-arm64)
JMH_1.0.3.tar.gz(r-4.5-noble)JMH_1.0.3.tar.gz(r-4.4-noble)
JMH_1.0.3.tgz(r-4.4-emscripten)JMH_1.0.3.tgz(r-4.3-emscripten)
JMH.pdf |JMH.html✨
JMH/json (API)
| # Install 'JMH' in R: |
| install.packages('JMH', repos = c('https://shanpengli.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/shanpengli/jmh/issues
Last updated 29 days agofrom:5e5d56cde7. Checks:1 OK, 10 WARNING. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Feb 06 2025 |
| R-4.5-win-x86_64 | WARNING | Feb 06 2025 |
| R-4.5-mac-x86_64 | WARNING | Feb 06 2025 |
| R-4.5-mac-aarch64 | WARNING | Feb 06 2025 |
| R-4.5-linux-x86_64 | WARNING | Feb 06 2025 |
| R-4.4-win-x86_64 | WARNING | Feb 06 2025 |
| R-4.4-mac-x86_64 | WARNING | Feb 06 2025 |
| R-4.4-mac-aarch64 | WARNING | Feb 06 2025 |
| R-4.3-win-x86_64 | WARNING | Feb 06 2025 |
| R-4.3-mac-x86_64 | WARNING | Feb 06 2025 |
| R-4.3-mac-aarch64 | WARNING | Feb 06 2025 |
Exports:AUCJMMLSMJMMLSMMAEQJMMLSMPAJMMLSMPEJMMLSMsurvfitJMMLSMsurvPAJMMLSM
Dependencies:backportsbase64encbslibcachemcaretcheckmateclasscliclockclustercmprskcodetoolscolorspacecpp11data.tablediagramdigestdoParalleldplyre1071evaluatefansifarverfastmapfontawesomeforeachforeignFormulafsfuturefuture.applygenericsggplot2globalsgluegowergridExtragtablehardhathighrHmischtmlTablehtmltoolshtmlwidgetsipredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixMatrixModelsmemoisemetsmgcvmimeModelMetricsmultcompmunsellmvtnormnlmennetnumDerivPAmeasuresparallellypecpillarpkgconfigplotrixplyrpolsplinepROCprodlimprogressrproxyPublishpurrrquantregR6rangerrappdirsRColorBrewerRcppRcppArmadilloRcppEigenrecipesreshape2riskRegressionrlangrmarkdownrmsrpartrstudioapisandwichsassscalesshapeSparseMsparsevctrsSQUAREMstatmodstringistringrsurvivalTH.datatibbletidyrtidyselecttimechangetimeDatetimeregtimeROCtinytextzdbutf8vctrsviridisviridisLitewithrxfunyamlzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Time-dependent AUC for joint models | AUCJMMLSM |
| Simulated competing risks data | cdata |
| Joint Modeling for Continuous outcomes | JMMLSM |
| A metric of prediction accuracy of joint model by comparing the predicted risk with the empirical risks stratified on different predicted risk group. | MAEQJMMLSM |
| Time-dependent AUC for joint models | PAJMMLSM |
| A metric of prediction accuracy of joint model by comparing the predicted risk with the counting process. | PEJMMLSM |
| Plot conditional probabilities for new subjects | plot.survfitJMMLSM |
| Print JMMLSM | print.JMMLSM |
| Print survfitJMMLSM | print.survfitJMMLSM |
| Print AUCJMMLSM | summary.AUCJMMLSM |
| Print MAEQJMMLSM | summary.MAEQJMMLSM |
| Print PAJMMLSM | summary.PAJMMLSM |
| Print PEJMMLSM | summary.PEJMMLSM |
| Prediction in Joint Models | survfitJMMLSM |
| Prediction in Joint Models | survPAJMMLSM |
| Variance-covariance matrix of the estimated parameters for joint models | vcov vcov.JMMLSM |
| Simulated longitudinal data | ydata |
