Multivariate reference and tolerance regions based on conditional transformation models: Application to glycemic markers

• Published in: Biometrical journal Vol. 65; no. 8; pp. e2200229 - n/a
• Main Authors: Lado‐Baleato, Óscar, Cadarso‐Suárez, Carmen, Kneib, Thomas, Gude, Francisco
• Format: Journal Article
• Language: English
• Published: Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.12.2023
• Subjects: Bernstein basis; Decision making; diabetes; Genetic transformation; Multivariate analysis; multivariate regression; Normal distribution; Patients; reference regions; Statistical analysis; transformation analysis; Transformations; transformation analysis; reference regions; Bernstein basis; multivariate regression; diabetes
• ISSN: 0323-3847; 1521-4036; 1521-4036
• DOI: 10.1002/bimj.202200229

The reference interval is the most widely used medical decision‐making, constituting a central tool in determining whether an individual is healthy or not. When the results of several continuous diagnostic tests are available for the same patient, their clinical interpretation is more reliable if a multivariate reference region (MVR) is available rather than multiple univariate reference intervals. MVRs, defined as regions containing 95% of the results of healthy subjects, extend the concept of the reference interval to the multivariate setting. However, they are rarely used in clinical practice owing to difficulties associated with their interpretability and the restrictions inherent to the assumption of a Gaussian distribution. Further statistical research is thus needed to make MVRs more applicable and easier for physicians to interpret. Since the joint distribution of diagnostic test results may well change with patient characteristics independent of disease status, MVRs adjusted for covariates are desirable. The present work introduces a novel formulation for MVRs based on multivariate conditional transformation models (MCTMs). Additionally, we take into account the estimation uncertainty of such MVRs by means of tolerance regions. These conditional MVRs imply no parametric restriction on the response, and potentially nonlinear continuous covariate effects can be estimated. MCTMs allow the estimation of the effects of covariates on the joint distribution of multivariate response variables and on these variables' marginal distributions, via the use of most likely transformation estimation. Our contributions proved reliable when tested with simulated data and for a real data application with two glycemic markers.