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Titolo:
USING BINARY LOGISTIC-REGRESSION MODELS FOR ORDINAL DATA WITH NONPROPORTIONAL ODDS
Autore:
BENDER R; GROUVEN U;
Indirizzi:
UNIV DUSSELDORF,DEPT METABOL DIS & NUTR,POB 101007 D-40001 DUSSELDORFGERMANY HOSP OSTSTADT,HANNOVER MED SCH,DEPT ANESTHESIOL,RES GRP INFORMAT & BIOMETRY HANNOVER GERMANY
Titolo Testata:
Journal of clinical epidemiology
fascicolo: 10, volume: 51, anno: 1998,
pagine: 809 - 816
SICI:
0895-4356(1998)51:10<809:UBLMFO>2.0.ZU;2-A
Fonte:
ISI
Lingua:
ENG
Soggetto:
CIGARETTE-SMOKING; MEDICAL-RESEARCH; FIT TESTS; RETINOPATHY; VARIABLES; GOODNESS; RISK;
Keywords:
LOGISTIC REGRESSION; ORDINAL DATA; PROPORTIONAL ODDS MODEL; NONPROPORTIONAL ODDS; DIABETIC RETINOPATHY;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Citazioni:
32
Recensione:
Indirizzi per estratti:
Citazione:
R. Bender e U. Grouven, "USING BINARY LOGISTIC-REGRESSION MODELS FOR ORDINAL DATA WITH NONPROPORTIONAL ODDS", Journal of clinical epidemiology, 51(10), 1998, pp. 809-816

Abstract

The proportional odds model (POM) is the most popular logistic regression model for analyzing ordinal response variables. However, violation of the main model assumption can lead to invalid results, This is demonstrated by application oi this method to data of a study investigating the effect of smoking on diabetic retinopathy. Since the proportional odds assumption is not fulfilled, separate binary logistic regression models are used for dichotomized response variables based upon cumulative probabilities. This approach is compared with polytomous logistic regression and the partial proportional odds model. The separate binary logistic regression approach is slightly less efficient than a joint model for the ordinal response. However, model building, investigating goodness-of-fit, and interpretation of the results is much easier for binary responses. The careful application of separate binary logistic regressions represents a simple and adequate tool to analyze ordinal data with non-proportional odds. (C) 1998 Elsevier Science Inc.

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Documento generato il 24/09/20 alle ore 05:09:12