groupedSurv
Efficient Estimation of Grouped Survival Models Using the Exact Likelihood Function
These 'Rcpp'-based functions compute the efficient score statistics for grouped time-to-event data (Prentice and Gloeckler, 1978), with the optional inclusion of baseline covariates. Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the 'Cuba' library (Hahn, 2005), and the source files are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, 2002).
- Version1.0.5.1
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- groupedSurv citation info
- Last release09/28/2023
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Team
Alexander Sibley
Yu Jiang
Show author detailsRolesContributorJiaxing Lin
Show author detailsRolesAuthorKouros Owzar
Show author detailsRolesAuthorTracy Truong
Show author detailsRolesAuthorZhiguo Li
Show author detailsRolesAuthorLayne Rogers
Show author detailsRolesContributorJanice McCarthy
Show author detailsRolesContributorAndrew Allen
Show author detailsRolesContributor
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