title: Imputation of incident events in longitudinal cohort studies.
abstract: 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.
authors.affiliation: Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Ryals Building, Room 327D, 1665 University Boulevard, Birmingham, AL 35294-0022, USA. ghoward@uab.edu
authors.forename: George
authors.initials: G
authors.lastname: Howard
authors.forename: Leslie A
authors.initials: LA
authors.lastname: McClure
authors.forename: Claudia S
authors.initials: CS
authors.lastname: Moy
authors.forename: Monika M
authors.initials: MM
authors.lastname: Safford
authors.forename: Mary
authors.initials: M
authors.lastname: Cushman
authors.forename: Suzanne E
authors.initials: SE
authors.lastname: Judd
authors.forename: Brett M
authors.initials: BM
authors.lastname: Kissela
authors.forename: Dawn O
authors.initials: DO
authors.lastname: Kleindorfer
authors.forename: Virginia J
authors.initials: VJ
authors.lastname: Howard
authors.forename: David J
authors.initials: DJ
authors.lastname: Rhodes
authors.forename: Paul
authors.initials: P
authors.lastname: Muntner
authors.forename: Hemant K
authors.initials: HK
authors.lastname: Tiwari
mesh_terms: D015982:Bias; D002423:Cause of Death / Q000639:trends; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008499:Medical Records / Q000706:statistics & numerical data*; D008875:Middle Aged; D015233:Models, Statistical; D044469:Racial Groups; D012307:Risk Factors; D020521:Stroke / Q000208:ethnology* / Q000401:mortality; D014481:United States / Q000453:epidemiology