[R] mixed-effects models for left-censored data?

[A.J. Rossini]

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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