Titre :
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Use of multiple imputation to correct for nonresponse bias in a survey of urologic Symptoms among African-American men. (2002)
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Auteurs :
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Jeremy-Mg TAYLOR ;
Kristine-L COOPER ;
Steve-G HEERINGA ;
Trivellore-E RAGHUNATHAN ;
Aruna-V SARMA ;
John-T WEI ;
Department of Biostatistics. University of Michigan. Ann Arbor. MI. USA ;
Department of Urology. University of Michigan. Ann Arbor. MI. USA
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Type de document :
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Article
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Dans :
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American journal of epidemiology (vol. 156, n° 8, 2002)
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Pagination :
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774-782
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Langues:
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Anglais
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Mots-clés :
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Cancer
;
Prostate
;
Homme
;
Ethnie
;
Dépistage
;
Epidémiologie
;
Etats Unis
;
Amérique
;
Méthodologie
;
Questionnaire
;
Appareil urinaire [pathologie]
;
Amérique du Nord
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Résumé :
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[BDSP. Notice produite par INIST-CNRS qR0xsk1D. Diffusion soumise ... autorisation]. The Flint Men's Health Study is an ongoing population-based study of African-American men designed to address questions related to prostate cancer and urologic symptoms. The initial phase of the study was conducted in 1996-1997 in two stages : an interviewer-administered survey followed by a clinical examination. The response rate in the clinical examination phase was 52%. Thus, some data were missing for clinical examination variables, diminishing the generalizability of the results to the general population. This paper is a case study demonstrating the application of multiple imputation to address important questions related to prostate cancer and urologic symptoms in a data set with missing values. On the basis of the observed clinical examination data, the American Urological Association Symptoms Score showed a surprising reduction in symptoms in the oldest age group, but after multiple imputation there was a monotonically increasing trend with age. It appeared that multiple imputation corrected for nonresponse bias associated with the observed data. For other outcome measures-namely, the age-adjusted 95th percentile of prostate-specific antigen level and the association between urologic symptoms and prostate volume-results from the observed data and the multiply imputed data were similar.
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