Bayesian Multivariate Regression-Based Statistical Decision Limits - Tabien, Lian Mae T.; Michael Daniel C. Lucagbo, Phd. (2024)
ABSTRACT

Decision limits are crucial in laboratory medicine for guiding diagnostic and decision-making processes. While reference ranges offer general guideline, decision limits, often one-sided upper limits, serve as diagnostic criteria for specific conditions. The decisions limits are often expressed as prediction and tolerance intervals. However, in complex diagnoses requiring multiple correlated analyte measurements, constructing separate univariate intervals may increase false positives. It is recommended to construct multivariate statistical regions to account for the cross-correlations among analytes. Moreover, appropriate decisions limits may also be needed for specific values of covariates (e.g., age and sex). Hence, this thesis proposes regression-based combined statistical decision limits within the Bayesian multivariate normal framework, considering tolerance and prediction criteria. Simulation results show that the proposed statistical decision limits have highly satisfactory frequentist properties, outperforming benchmark methods even with low cross-correlations among analytes. Additionally, the proposed methodology is efficient, taking less than a minute to compute decision limits, indicating its practical utility in clinical settings.
Includes bibliographic references
Keywords: Decision Limits -- Bayesian Multivariate Regression -- Prediction Interval -- Tolerance Internal