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.

Version: 3.16
Depends: survival
Published: 2022-02-04
DOI: 10.32614/CRAN.package.joint.Cox
Author: Takeshi Emura
Maintainer: Takeshi Emura <takeshiemura at>
License: GPL-2
NeedsCompilation: no
In views: MetaAnalysis, Survival
CRAN checks: joint.Cox results


Reference manual: joint.Cox.pdf


Package source: joint.Cox_3.16.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): joint.Cox_3.16.tgz, r-oldrel (arm64): joint.Cox_3.16.tgz, r-release (x86_64): joint.Cox_3.16.tgz, r-oldrel (x86_64): joint.Cox_3.16.tgz
Old sources: joint.Cox archive

Reverse dependencies:

Reverse depends: GFGM.copula
Reverse suggests: multipleOutcomes


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