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- W927930346 endingPage "171" @default.
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- W927930346 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." @default.
- W927930346 created "2016-06-24" @default.
- W927930346 creator A5015070076 @default.
- W927930346 creator A5061915997 @default.
- W927930346 creator A5081588608 @default.
- W927930346 date "2015-07-12" @default.
- W927930346 modified "2023-10-16" @default.
- W927930346 title "Are All Biases Missing Data Problems?" @default.
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- W927930346 doi "https://doi.org/10.1007/s40471-015-0050-8" @default.
- W927930346 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4643276" @default.
- W927930346 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26576336" @default.
- W927930346 hasPublicationYear "2015" @default.
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