The utility of multivariate outlier detection techniques for data quality evaluation in large studies: an application within the ONDRI project.

ABSTRACT:

Background

Large and complex studies are now routine, and quality assurance and quality control (QC) procedures ensure reliable results and conclusions. Standard procedures may comprise manual verification and double entry, but these labour-intensive methods often leave errors undetected. Outlier detection uses a data-driven approach to identify patterns exhibited by the majority of the data and highlights data points that deviate from these patterns. Univariate methods consider each variable independently, so observations that appear odd only when two or more variables are considered simultaneously remain undetected. We propose a data quality evaluation process that emphasizes the use of multivariate outlier detection for identifying errors, and show that univariate approaches alo

SUBMITTER: Sunderland KM 

PROVIDER: S-EPMC6521365 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature