A few days ago I uploaded to CRAN a new package called 'eha', which
stands for 'Event History Analysis'. Its main focus is on proportional
hazards modeling in survival analysis, and in that respect eha can
be regarded as a complement and an extension to the 'survival'
package. In fact eha requires survival. Eha contains three functions
for proportional hazards analysis:
1. 'coxreg': Performs Cox regression, almost as 'coxph' in
survival.
There are two methods, 'efron' (default) and 'breslow', exactly
as in
coxph. There are two extensions, compared to coxph: (i) Sampling of
survivors in risk sets (at event times), which can be useful with
huge data sets and few events. (ii) The so-called 'weird bootstrap':
For the fitted model, new events are drawn in each risk set with
probabilities given by the fitted model, independently between
risk sets (that's the 'weird' part). This is repeated R times
and the output is two Rxp matrices, one with the bootstrap estimates
of the regression coefficients, and one with the corresponding
standard errors. The analysis is up to the user for now.
The 'boot' package?
2. 'mlreg': A discrete time proportional hazards model is fitted along
the lines of Kalbfleisch & Prentice (1980, pp. 98--103). See also
Brostr?m (2002): "Cox regression; Ties withot tears", Communications
in Statistics, Theory & Methods 31, 285--297. This function has two methods;
"ML", the purely discrete model with one parameter per observed
distinct
event time, and "MPPL", which is a hybrid between Cox regression and
the discrete model: Only tied event times are associated to a unique
parameter; the untied event times contributes a "Cox regression term".
For completely untied data this results in ordinary Cox regression.
"MPPL" can be regarded as an attempt to handle tied data in Cox
regression,
comparable to the 'efron' method. This method does not break down
because
of too heavily tied data, which the efron method might do.
3. 'weibreg': Weibull regression for left truncated and right censored
data. Allows for stratification with different shape and scale parameters
in the strata.
Moreover, there are functions for extracting subsamples as 'rectangles'
in the Lexis diagram, including external ('communal') covariates in a
'survival data frame', extracting information from risk sets, summary
statistics from the Lexis diagram, etc, etc.
G?ran
---
G?ran Brostr?m tel: +46 90 786 5223
Department of Statistics fax: +46 90 786 6614
Ume? University http://www.stat.umu.se/egna/gb/
SE-90187 Ume?, Sweden e-mail: gb at stat.umu.se
