This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Rabe-Hesketh, S. Articles by Pickles, A. Search for Related Content PubMed PubMed Citation Articles by Rabe-Hesketh, S. Articles by Pickles, A. Social Bookmarking                         What's this?

Multilevel models for censored and latent responses

Department of Biostatistics and Computing, Institute of Psychiatry, King’s College, London, UK, spaksrh{at}iop.kcl.ac.uk

Shuying Yang

MRC Biostatistics Unit, University of Cambridge, UK

Andrew Pickles

School of Epidemiology and Health Science, The University of Manchester, UK

Multilevel models were originally developed to allow linear regression or ANOVA models to be applied to observations that are not mutually independent. This lack of independence commonly arises due to clustering of the units of observations into ‘higher level units’ such as patients in hospitals. In linear mixed models, the within-cluster correlations are modelled by including random effects in a linear model.

In this paper, we discuss generalizations of linear mixed models suitable for responses subject to systematic and random measurement error and interval censoring.

The first example uses data from two cross-sectional surveys of schoolchildren to investigate risk factors for early first experimentation with cigarettes. Here the recalled times of the children’s first cigarette are likely to be subject to both systematic and random measurement errors as well as being interval censored. We describe multilevel models for interval censored survival times as special cases of generalized linear mixed models and discuss methods of estimating systematic recall bias.

The second example is a longitudinal study of mental health problems of patients nested in clinics. Here the outcome is measured by multiple questionnaires allowing the measurement errors to be modelled within a linear latent growth curve model. The resulting model is a multilevel structural equation model. We briefly discuss such models both as extensions of linear mixed models and as extensions of structural equation models. Several different model structures are examined.

An important goal of the paper is to place a number of methods that readers may have considered as being distinct within a single overall modelling framework.

Statistical Methods in Medical Research, Vol. 10, No. 6, 409-427 (2001)
DOI: 10.1177/096228020101000604


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?

This article has been cited by other articles:

				S. Rabe-Hesketh, A. Pickles, and A. Skrondal Correcting for covariate measurement error in logistic regression using nonparametric maximum likelihood estimation Statistical Modeling, October 1, 2003; 3(3): 215 - 232. [Abstract] [PDF]

				D. A Bennett Review of analytical methods for prospective cohort studies using time to event data: single studies and implications for meta-analysis Statistical Methods in Medical Research, August 1, 2003; 12(4): 297 - 319. [Abstract] [PDF]