Analytical method for detecting outlier evaluators.

ABSTRACT:

Background

Epidemiologic and medical studies often rely on evaluators to obtain measurements of exposures or outcomes for study participants, and valid estimates of associations depends on the quality of data. Even though statistical methods have been proposed to adjust for measurement errors, they often rely on unverifiable assumptions and could lead to biased estimates if those assumptions are violated. Therefore, methods for detecting potential 'outlier' evaluators are needed to improve data quality during data collection stage.

Methods

In this paper, we propose a two-stage algorithm to detect 'outlier' evaluators whose evaluation results tend to be higher or lower than their counterparts. In the first stage, evaluators' effects are obtained by fitting a regression model.

SUBMITTER: Wu Y 

PROVIDER: S-EPMC10391872 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature