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Titolo:
MARGINAL REGRESSION-MODELS FOR CLUSTERED ORDINAL MEASUREMENTS
Autore:
HEAGERTY PJ; ZEGER SL;
Indirizzi:
UNIV WASHINGTON,DEPT BIOSTAT SEATTLE WA 98195 JOHNS HOPKINS UNIV,SCH PUBL HLTH BALTIMORE MD 21205
Titolo Testata:
Journal of the American Statistical Association
fascicolo: 435, volume: 91, anno: 1996,
pagine: 1024 - 1036
Fonte:
ISI
Lingua:
ENG
Soggetto:
GENERALIZED ESTIMATING EQUATIONS; LONGITUDINAL DATA-ANALYSIS; EXPONENTIAL MODEL; BINARY DATA; RESPONSES; DISCRETE; RATIO;
Keywords:
ESTIMATING EQUATION; GLOBAL ODDS RATIO; PROPORTIONAL ODDS MODEL;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
CompuMath Citation Index
Science Citation Index Expanded
Citazioni:
25
Recensione:
Indirizzi per estratti:
Citazione:
P.J. Heagerty e S.L. Zeger, "MARGINAL REGRESSION-MODELS FOR CLUSTERED ORDINAL MEASUREMENTS", Journal of the American Statistical Association, 91(435), 1996, pp. 1024-1036

Abstract

This article constructs statistical models for clustered ordinal measurements. We specify two regression models: one for the marginal meansand one for the marginal pairwise global odds ratios. Of particular interest are problems in which the odds ratio regression is a focus. Simple assumptions about higher-order conditional moments give a quadratic exponential likelihood function with second-order estimating equations (GEE2) as score equations. But computational difficulty can arise for large clusters when both the mean response and the association between measures is of interest. First, we present GEE1 as an alternativeestimation strategy. Second, we extend to repeated ordinal measurements the method developed by Carey et al. for binary observations that is based on alternating logistic regressions (ALR) for the marginal mean parameters and the pairwise log-odds ratio parameters. We study the efficiency-of GEE1 and ALR relative to full maximum likelihood. We demonstrate the utility of our regression methods for ordinal data by applying the methods to a surgical follow-up study.

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Documento generato il 24/09/20 alle ore 05:23:09