Package: joint.Cox 3.16

Takeshi Emura

joint.Cox: Joint Frailty-Copula Models for Tumour Progression and Death in Meta-Analysis

Fit survival data and perform dynamic prediction under joint frailty-copula models for tumour progression and death. Likelihood-based methods are employed for estimating model parameters, where the baseline hazard functions are modeled by the cubic M-spline or the Weibull model. The methods are applicable for meta-analytic data containing individual-patient information from several studies. Survival outcomes need information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression). Methodologies were published in Emura et al. (2017) <doi:10.1177/0962280215604510>, Emura et al. (2018) <doi:10.1177/0962280216688032>, Emura et al. (2020) <doi:10.1177/0962280219892295>, Shinohara et al. (2020) <doi:10.1080/03610918.2020.1855449>, Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>, and Emura et al. (2021) <doi:10.1177/09622802211046390>. See also the book of Emura et al. (2019) <doi:10.1007/978-981-13-3516-7>. Survival data from ovarian cancer patients are also available.

Authors:Takeshi Emura

joint.Cox_3.16.tar.gz
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joint.Cox.pdf |joint.Cox.html✨
joint.Cox/json (API)

# Install 'joint.Cox' in R:
install.packages('joint.Cox', repos = 'https://cloud.r-project.org')
Datasets:
  • dataOvarian - Survival data of 1003 ovarian cancer patients from 4 independent studies.
  • dataOvarian1 - Data on time-to-recurrence and 158 gene expressions for 912 ovarian cancer patients from 4 independent studies.
  • dataOvarian2 - Data on time-to-death and 128 gene expressions for 912 ovarian cancer patients from 4 independent studies.

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.48 score 5 stars 2 packages 574 downloads 15 exports 3 dependencies

Last updated 3 years agofrom:8b924e04d6. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 21 2025
R-4.5-linuxOKMar 21 2025
R-4.4-linuxOKMar 21 2025

Exports:cmprskCox.regcondCox.regF.KMF.predictionF.windowF.window.WeibullF.windowsF.windows.WeibullI.splinejointCox.indep.regjointCox.regjointCox.Weibull.regM.splinesplineCox.regWeibull.simu

Dependencies:latticeMatrixsurvival

Citation

To cite package ‘joint.Cox’ in publications use:

Emura T (2022). joint.Cox: Joint Frailty-Copula Models for Tumour Progression and Death in Meta-Analysis. R package version 3.16, https://CRAN.R-project.org/package=joint.Cox.

ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see ‘help("citation")’.

Corresponding BibTeX entry: 

  @Manual{,
    title = {joint.Cox: Joint Frailty-Copula Models for Tumour
      Progression and Death in Meta-Analysis},
    author = {Takeshi Emura},
    year = {2022},
    note = {R package version 3.16},
    url = {https://CRAN.R-project.org/package=joint.Cox},
  }

Readme and manuals

Help Manual

Help pageTopics
Joint Frailty-Copula Models for Tumour Progression and Death in Meta-Analysisjoint.Cox-package joint.Cox
The Competing Risks Version of Penalized Likelihood Estimation under the Joint Cox Models Between Tumour Progression and Death for Meta-AnalysiscmprskCox.reg
Penalized Likelihood Estimation under the Joint Cox Models Between Tumour Progression and Death for Meta-Analysis; A Conditional Copula ApproachcondCox.reg
Survival data of 1003 ovarian cancer patients from 4 independent studies.dataOvarian
Data on time-to-recurrence and 158 gene expressions for 912 ovarian cancer patients from 4 independent studies.dataOvarian1
Data on time-to-death and 128 gene expressions for 912 ovarian cancer patients from 4 independent studies.dataOvarian2
Prediction of death using the Kaplan-Meier estimatorF.KM
Dynamic prediction of deathF.prediction
Dynamic prediction of death under the joint frailty-copula modelF.window
Dynamic prediction of death under the joint frailty-copula model (the Weibull baseline hazard functions)F.window.Weibull
Dynamic prediction of death under the joint frailty-copula modelF.windows
Dynamic prediction of death under the joint frailty-copula model (the Weibull baseline hazard functions)F.windows.Weibull
I-spline basis functionI.spline
Penalized Likelihood Estimation under the Joint Cox Models Between Tumour Progression and Death for Meta-AnalysisjointCox.indep.reg
Penalized Likelihood Estimation under the Joint Cox Models Between Tumour Progression and Death for Meta-AnalysisjointCox.reg
Weibull-based Likelihood Estimation under the Joint Cox Models Between Tumour Progression and Death for Meta-AnalysisjointCox.Weibull.reg
M-spline basis functionM.spline
Fitting the Cox model for survival data using a penalized spline modelsplineCox.reg
Simulating data from the Weibull joint frailty-copula modelWeibull.simu