Flexible outlier detection in multicenter clinical trials.

Introduction: Multicenter clinical trials have become increasingly larger and more complex yet assuring high-quality data remains an essential task for data coordinating centers. Methods: A flexible algorithm is presented to exhaustively search for potential data anomalies by participant and site. The algorithm proceeds as a three-phase collaboration between the data coordinating center and clinical sites. First, participant-level data are examined in a univariate approach for all relevant variables. Values at the extreme tails of the distribution that also lie outside range checks and are previously unverified by clinical sites are deemed potential outliers. Second, participant-level data are examined in a multivariate machine learning approach among meaningful groups of related variables, Results: Application of the algorithm to MoTrPAC yielded an evaluation of over 1.9 million observations in Conclusion: It is recommended to apply this algorithm to a subset of participants early in a study, with repeated explorations over subsequent intervals throughout the study. The goal is to maximize data quality, particularly critical to the increasing occurrence of open-source, data resources.