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Higher-order evidence (ie, evidence about evidence) allows epidemiologists and other health data scientists to account for measurement error in validation data. Here, to illustrate the use of higher-order evidence, we provide a minimal nontrivial example of estimating the proportion and show how higher-order evidence can be used to construct sensitivity analyses. The proposed method provides a flexible approach to account for multiple levels of distortion in the results of epidemiologic studies.

More information Original publication

DOI

10.1093/aje/kwae321

Type

Journal article

Publication Date

2025-04-08T00:00:00+00:00

Volume

194

Pages

886 - 888

Total pages

2

Keywords

bias, measurement error, random error, systematic error, Humans, Data Interpretation, Statistical, Bias, Models, Statistical, Epidemiologic Studies