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).
