Assessing the Potential for Bias From Nonresponse to a Study Follow-up Interview: An Example From the Agricultural Health Study. Issue 4 (7th July 2017)

• Record Type: Journal Article
• Authors: Rinsky, Jessica L.; Richardson, David B.; Wing, Steve; Beard, John D.; Alavanja, Michael; Beane Freeman, Laura E.; Chen, Honglei; Henneberger, Paul K.; Kamel, Freya; Sandler, Dale P.; Hoppin, Jane A.
• Is Part Of: American journal of epidemiology. Volume 186:Issue 4(2017)
• Page Start: 395
• Page End: 404
• Publication Date: 2017-07-07
• Subjects: attrition -- epidemiologic methods -- inverse probability weights -- loss to follow-up -- occupational/environmental epidemiology -- prospective studies -- selection bias; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwx098
• Languages: English
• ISSNs: 0002-9262

Abstract

Abstract: Prospective cohort studies are important tools for identifying causes of disease. However, these studies are susceptible to attrition. When information collected after enrollment is through interview or exam, attrition leads to missing information for nonrespondents. The Agricultural Health Study enrolled 52, 394 farmers in 1993–1997 and collected additional information during subsequent interviews. Forty-six percent of enrolled farmers responded to the 2005–2010 interview; 7% of farmers died prior to the interview. We examined whether response was related to attributes measured at enrollment. To characterize potential bias from attrition, we evaluated differences in associations between smoking and incidence of 3 cancer types between the enrolled cohort and the subcohort of 2005–2010 respondents, using cancer registry information. In the subcohort we evaluated the ability of inverse probability weighting (IPW) to reduce bias. Response was related to age, state, race/ethnicity, education, marital status, smoking, and alcohol consumption. When exposure and outcome were associated and case response was differential by exposure, some bias was observed; IPW conditional on exposure and covariates failed to correct estimates. When response was nondifferential, subcohort and full-cohort estimates were similar, making IPW unnecessary. This example provides a demonstration of investigating the influence of attrition in cohort studies using information that has been self-reported after enrollment.