Research article summary (published 13 Jun 2009):

Correlated bivariate continuous and binary outcomes: issues and applications.

Full Abstract

Increasingly multiple outcomes are collected in order to characterize treatment effectiveness or to evaluate the impact of large policy initiatives. Often the multiple outcomes are non-commensurate, e.g. measured on different scales. The common approach to inference is to model each outcome separately ignoring the potential correlation among the responses. We describe and contrast several full likelihood and quasi-likelihood multivariate methods for non-commensurate outcomes. We present a new multivariate model to analyze binary and continuous correlated outcomes using a latent variable. We study the efficiency gains of the multivariate methods relative to the univariate approach. For complete data, all approaches yield consistent parameter estimates. When the mean structure of all outcomes depends on the same set of covariates, efficiency gains by adopting a multivariate approach are negligible. In contrast, when the mean outcomes depend on different covariate sets, large efficiency gains are realized. Three real examples illustrate the different approaches. Copyright (c) 2009 John Wiley & Sons, Ltd.

Author information

Author/s: Teixeira-Pinto, Armando (A); Normand, Sharon-Lise T (SL);

Affiliation: Faculty of Medicine, Department of Biostatistics and Medical Informatics, University of Porto, Porto, Portugal. tpinto(-atsign-)med.up.pt

Grants: R01-MH54693 (Agency:NIMH NIH HHS) ; R01-MH61434 (Agency:NIMH NIH HHS)

Journal and publication information

Publication Type: Journal Article; Research Support, N.I.H., Extramural

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

Reference: 2009-Jun; vol 28 (issue 13) : pp 1753-73

Dates: Created 2009/05/13; Completed 2009/07/01;

PMID: 19358234, status: MEDLINE (last retrieval date: 7/1/2009, IMS Date: 01 Jul 2009 00:00:00)

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.

Algorithms Analysis of Variance Biometry - methods Confidence Intervals Coronary Artery Disease - therapy Coronary Restenosis - etiology Humans Interferon Type I, Recombinant - therapeutic use Likelihood Functions

Likelihood Functions Macular Degeneration - drug therapy Managed Care Programs Models, Statistical Monte Carlo Method Multivariate Analysis Schizophrenia - therapy Stents Treatment Outcome

Associated Chemicals: Interferon Type I, Recombinant (0)

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