title: Are all biases missing data problems?
abstract: Estimating causal effects is a frequent goal of epidemiologic studies. Traditionally, there have been three established systematic threats to consistent estimation of causal effects. These three threats are bias due to confounders, selection, and measurement error. Confounding, selection, and measurement bias have typically been characterized as distinct types of biases. However, each of these biases can also be characterized as missing data problems that can be addressed with missing data solutions. Here we describe how the aforementioned systematic threats arise from missing data as well as review methods and their related assumptions for reducing each bias type. We also link the assumptions made by the reviewed methods to the missing completely at random (MCAR) and missing at random (MAR) assumptions made in the missing data framework that allow for valid inferences to be made based on the observed, incomplete data.
authors.affiliation: Department of Epidemiology, Center for Population Health and Clinical Epidemiology, Brown University School of Public Health, 121 South Main Street, Providence, Rhode Island 02912 (, , chanelle_howe@brown.edu ).
authors.forename: Chanelle J
authors.initials: CJ
authors.lastname: Howe
authors.affiliation: Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Kresge, Room 820, Boston, Massachusetts 02115 (, , lcain@hsph.harvard.edu ).
authors.forename: Lauren E
authors.initials: LE
authors.lastname: Cain
authors.affiliation: Department of Biostatistics, Center for Statistical Sciences, Brown University School of Public Health, Providence, Rhode Island 02912 (, , joseph_hogan@brown.edu ).
authors.forename: Joseph W
authors.initials: JW
authors.lastname: Hogan
keywords: Confounding bias; Measurement bias; Missing data; Selection bias