Understanding the influence of individual variables contributing to multivariate outliers in assessments of data quality

Abstract

Summary Mahalanobis distance is often recommended to identify patients or clinical sites that are considered unusual in clinical trials. Patients extreme in one or more covariates may be considered outliers in that they reside some distance from the multivariate mean, which can be thought of as the center of the data cloud. Less often discussed, patients whose data are believed to be “too good to be true” are located near the centroid as inliers. In order to efficiently investigate these anomalies for potential lapses in data quality, it is important to understand how the individual variables contribute to each multivariate outlier. There is a lack of literature describing a reasonable workflow for identification of outliers and their subsequent investigation to understand how each variable contributes to an observation being considered extreme. We describe how to identify multivariate inliers and outliers, classify outliers according to varying levels of severity, and summarize the contributions of variables using principal components in a manner that is accessible to a wide audience with straightforward interpretation. We illustrate how numerous data visualizations, including Pareto plots, can facilitate further review even in studies containing numerous observations and variables. We illustrate these methodologies using data from a multicenter clinical trial.

Data-Influence Analytics in Predictive Models Applied to Asthma Disease

“…Thus, it is possible to avoid inaccurate conclusions from results of the study. Therefore, good statistical practices must be followed with sophisticated techniques, such as those presented in this work related to detection of influential data and outliers, as well as other possible inconsistencies in the data; see the studies presented in [48,49], which support our discussion in terms of data quality and analytics in medicine. Thus, our study can be a knowledge addition to the toolkit of diverse practitioners, including medical doctors, applied statisticians, and data scientists.…”

Bayesian central statistical monitoring using finite mixture models in multicenter clinical trials

“…However, it is difficult to investigate the causes that the site is out of normal. Mason et al [ 17 ] and Zink et al [ 18 ] tackle the problem of investigating the contribution of the individual variables to each multivariate outlier after detection of abnormalities. Zink et al [ 18 ] illustrated the application of the contribution plot [ 19 ] to RBM for real multicenter clinical trials.…”

“…Mason et al [ 17 ] and Zink et al [ 18 ] tackle the problem of investigating the contribution of the individual variables to each multivariate outlier after detection of abnormalities. Zink et al [ 18 ] illustrated the application of the contribution plot [ 19 ] to RBM for real multicenter clinical trials. Mason et al [ 17 ] proposed to calculate the magnitude of the contribution using decomposition of Hotelling's T 2 statistic into one dimensional variate.…”

“…After detection, though why these sites are atypical are investigated, it is necessary to specify how the individual variables contribute to each multivariate outlier. Mason et al [ 17 ] and Zink et al [ 18 ] tackle this problem. For this problem, Zink et al [ 18 ] illustrated the application of the contribution plot [ 19 ] to RBM for real multicenter clinical trials.…”