[R] Approaches of Frailty estimation: coxme vs coxph(...frailty(id,	dist='gauss')) Dear List,


In documents (Therneau, 2003 : On mixed-effect cox
models, ...), as far as I came to know, coxme penalize
the partial likelihood (Ripatti, Palmgren, 2000) where
as frailtyPenal (in frailtypack package) uses  the
penalized the full likelihood approach (Rondeau et al,
2003).

How, then, coxme and coxph(...frailty(id,
dist='gauss')) differs? Just the coding algorithm, or
in approach too?

coxph(...frailty(id, dist='gamma')) estimates by means
of the penalized likelihood approach (Hougaard, 2000).
Same for coxph(...frailty(id, dist='gauss'))?

How these are related with nltm(...model="GFT") in
nltm package done in the approach of Non-linear
transformation (Tsodikov, 2003)?

Also, is the 3 stage approach (Hougaard, 2000, pp.267)
implimented anywhere in R?

Finally, Is there a R version of the Frailty.stable (A
set of Splus function to estimate parameters of a
positive stable frailty model) by  Wassell et al
(1999)?


Thanks for your valuable time.
Thanks in advance.


Mohammad Ehsanul Karim
Institute of Statistical Research and Training, 
University of Dhaka

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