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.
Journal article
2025-04-08T00:00:00+00:00
194
886 - 888
2
bias, measurement error, random error, systematic error, Humans, Data Interpretation, Statistical, Bias, Models, Statistical, Epidemiologic Studies