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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 gmail.com> |
| 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: joint.Cox_3.16.zip, r-release: joint.Cox_3.16.zip, r-oldrel: joint.Cox_3.16.zip |
| 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 depends: | GFGM.copula |
| Reverse imports: | splineCox |
| Reverse suggests: | multipleOutcomes |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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