Dimitris Rizopoulos <d.rizopoulos@erasmusmc.nl> and Spyridoula Tsonaka <spyridoula.tsonaka@med.kuleuven.be>

Dimitris Rizopoulos <d.rizopoulos@erasmusmc.nl> and Spyridoula
Tsonaka <s.tsonaka@lumc.nl>
Regression models for grouped and coarse data, under the
Coarsened At Random assumption.

Efficient Estimation of Grouped Survival Models Using the Exact
Likelihood Function
Jiaxing Lin [aut],
Alexander Sibley [aut],
Tracy Truong [aut],
Kouros Owzar [aut],
Zhiguo Li [aut],
Yu Jiang [ctb],
Janice McCarthy [ctb],
Andrew Allen [ctb]
The core of this 'Rcpp'-based package is a set of functions to compute the efficient score statistics for grouped survival models. The functions are designed to analyze grouped time-to-event data with the optional inclusion of either baseline covariates or family structure of related individuals (e.g., trios). Functions for estimating the baseline hazards, frailty variance, nuisance parameters, and fixed effects are also provided. The functions encompass two processes for discrete-time shared frailty model data with random effects: (1) evaluation of the multiple variable integration to compute the exact proportional-hazards-model-based likelihood and (2) estimation of the desired parameters using maximum likelihood. For data without family structure, only the latter step is performed. The integration is evaluated by the 'Cuhre' algorithm from the 'Cuba' library (Hahn, T. (2005). Cuba-a library for multidimensional numerical integration, Comput. Phys. Commun. 168, 78-95 <doi:10.1016/j.cpc.2005.01.010>), and the source files of the 'Cuhre' function 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, R., Algorithms for Minimization without Derivatives, Dover, 2002, ISBN 0-486-41998-3).
Rcpp (>= 0.12.4), doParallel, doRNG, parallel, foreach, qvalue

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).