| Titre : | Imputation of Incident Events in Longitudinal Cohort Studies. (2011) |
| Auteurs : | George HOWARD ; Mary CUSHMAN ; Virginia-J HOWARD ; Suzanne-E JUDD ; Brett-M KISSELA ; Dawn-O KLEINDORFER ; Leslie-A MCCLURE ; Claudia-S MOY ; Paul MUNTNER ; David-J RHODES ; Monika-M SAFFORD ; Hemant-K TIWARI ; Department of Biostatistics. School of Public Health. University of Alabama at Birmingham. Birmingham. AL. USA ; Department of Neurology. School of Medicine. University of Cincinnati. Cincinnati. OH. USA |
| Type de document : | Article |
| Dans : | American journal of epidemiology (vol. 174, n° 6, 2011) |
| Pagination : | 718-726 |
| Langues: | Anglais |
| Mots-clés : | Incidence ; Epidémiologie |
| Résumé : | [BDSP. Notice produite par INIST-CNRS l9BJR0xt. Diffusion soumise à autorisation]. Longitudinal cohort studies normally identify and adjudicate incident events detected during follow-up by retrieving medical records. There are several reasons why the adjudication process may not be successfully completed for a suspected event including the inability to retrieve medical records from hospitals and an insufficient time between the suspected event and data analysis. These "incomplete adjudications" are normally assumed not to be events, an approach which may be associated with loss of precision and introduction of bias. In this article, the authors evaluate the use of multiple imputation methods designed to include incomplete adjudications in analysis. Using data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study, 2008-2009, they demonstrate that this approach may increase precision and reduce bias in estimates of the relations between risk factors and incident events. |
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