Research article summary (published 28 Aug 2009):

A selection model for longitudinal binary responses subject to non-ignorable attrition.

Full Abstract

Longitudinal studies collect information on a sample of individuals which is followed over time to analyze the effects of individual and time-dependent characteristics on the observed response. These studies often suffer from attrition: individuals drop out of the study before its completion time and thus present incomplete data records. When the missing mechanism, once conditioned on other (observed) variables, does not depend on current (eventually unobserved) values of the response variable, the dropout mechanism is known to be ignorable. We propose a selection model extending semiparametric variance component models for longitudinal binary responses to allow for dependence between the missing data mechanism and the primary response process. The model is applied to a data set from a methadone maintenance treatment programme held in Sidney, 1986.

Author information

Author/s: Alfò, Marco (M); Maruotti, Antonello (A);

Affiliation: Dipartimento di Statistica, Probabilità e Statistiche Applicate, Sapienza Università di Roma, Italy.

Journal and publication information

Publication Type: Journal Article

Journal: Statistics in medicine (Stat Med), published in England. (Language: eng)

Reference: 2009-Aug; vol 28 (issue 19) : pp 2435-50

Dates: Created 2009/07/14; Completed 2009/10/01;

PMID: 19424960, status: MEDLINE (last retrieval date: 10/1/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Biometry Databases, Factual Humans Likelihood Functions Linear Models Longitudinal Studies

Longitudinal Studies Methadone - therapeutic use Models, Statistical Opioid-Related Disorders - rehabilitation Regression Analysis Statistics, Nonparametric

Associated Chemicals: Methadone (76-99-3)

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