mixed-effects models for left-censored data?

Thomas Lumley <tlumley at u.washington.edu> writes:

On Wed, 11 Jun 2003, Remko Duursma wrote:

For a random intercept model you could use survreg() and frailty() in the
survival package.

In general the random effects tobit model will be quite hard to fit,
involving a numerical integration whose dimension is the number of random
effects.   Some sort of EM algorithm might work.

One huge catch with that approach is heterscedasticity, which seems to
pop its head up all too often with limit-of-detection assay data.

There is a paper by Pettit in Biometrics some time ago on censored linear
mixed models -- I don't have the reference with me.

There is also a paper by a fellow named Jim Hughes, in Biometrics
(late 90s?), on this exact topic -- he used single imputation, whereas
he mentioned later (private communication) that a multiple imputation
approach would be better.  The S-PLUS code (it isn't pretty) is
somewhere on his WWW page, buried deep in the U Washington
Biostatistcs WWW site.

At least it used to be.  

best,
-tony

A.J. Rossini  /  rossini at u.washington.edu  /  rossini at scharp.org
Biomedical/Health Informatics and Biostatistics, University of Washington.
Biostatistics, HVTN/SCHARP, Fred Hutchinson Cancer Research Center.
FHCRC: 206-667-7025 (fax=4812)|Voicemail is pretty sketchy/use Email 

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