Bayesian combined statistical decision limits with covariates

Music City Center 

Decision limits are crucial in laboratory medicine for guiding diagnostic and decision-making processes. While reference ranges offer general guidelines, decision limits, often one-sided upper limits, serve as diagnostic criteria for specific conditions. In complex diagnoses which require the use of several analytes, computing separate univariate decision limits increases the number of false positives. As an alternative to this, it is recommended to construct multivariate decision limits that account for the cross-correlations among analytes. Moreover, appropriate decision limits may also be needed for specific values of covariates (e.g., age and sex). For this reason, this study proposes an approach to compute regression-based multivariate statistical decision limits within the multivariate normal framework. The criterion used in obtaining the decision limits is related to Bayesian tolerance regions. Simulation results show that the proposed statistical decision limits have highly satisfactory frequentist properties. Finally, the approach used in this study controls the desired false positive rate at a prespecified level of confidence.

decision limits

Bayesian multivariate regression

tolerance interval

laboratory medicine 

Section on Bayesian Statistical Science