Message-ID: <Pine.A41.4.44.0306111731000.60474-100000@homer15.u.washington.edu>
Date: 2003-06-12T00:35:29Z
From: Thomas Lumley
Subject: mixed-effects models for left-censored data?
In-Reply-To: <JPCHHECPKPMDNDAA@mailcity.com>
On Wed, 11 Jun 2003, Remko Duursma wrote:
> Dear R-helpers,
>
> excuse me if this is not exclusively an R-related question.
>
> I have data from a nested design, both temporally and spatially, and the
> reponse variable of interest is left-censored. That is, only values >
> "some treshold" are available, otherwise "LOW" is reported.
>
> Are there ways of building a linear model with both fixed and random
> effects, when the response variable is censored? Can the tobit model be
> modified to do this? Does anyone have experience with this type of
> dataset?
>
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.
There is a paper by Pettit in Biometrics some time ago on censored linear
mixed models -- I don't have the reference with me.
-thomas