Abstract

BackgroundMedical decision making based on quantitative test results depends on reliable reference intervals, which represent the range of physiological test results in a healthy population. Current methods for the estimation of reference limits focus either on modelling the age-dependent dynamics of different analytes directly in a prospective setting or the extraction of independent distributions from contaminated data sources, e.g. data with latent heterogeneity due to unlabeled pathologic cases. In this article, we propose a new method to estimate indirect reference limits with non-linear dependencies on covariates from contaminated datasets by combining the framework of mixture models and distributional regression.ResultsSimulation results based on mixtures of Gaussian and gamma distributions suggest accurate approximation of the true quantiles that improves with increasing sample size and decreasing overlap between the mixture components. Due to the high flexibility of the framework, initialization of the algorithm requires careful considerations regarding appropriate starting weights. Estimated quantiles from the extracted distribution of healthy hemoglobin concentration in boys and girls provide clinically useful pediatric reference limits similar to solutions obtained using different approaches which require more samples and are computationally more expensive.ConclusionsLatent class distributional regression models represent the first method to estimate indirect non-linear reference limits from a single model fit, but the general scope of applications can be extended to other scenarios with latent heterogeneity.

Highlights

  • Medical decision making based on quantitative test results depends on reliable reference intervals, which represent the range of physiological test results in a healthy population

  • Latent class distributional regression models represent the first method to estimate indirect non-linear reference limits from a single model fit, but the general scope of applications can be extended to other scenarios with latent heterogeneity

  • Simulation study To investigate whether or not the suggested latent class distributional regression models are able to approximate the true underlying non-linear components of an unlabeled dataset, we examined the performance of our approach using an adaptive simulation scenario described

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Summary

Introduction

Medical decision making based on quantitative test results depends on reliable reference intervals, which represent the range of physiological test results in a healthy population. Together with the recommendations that each laboratory should establish its own reference intervals due to potential transferability problems and conduct periodical reviews of the resulting estimates, this gold standard is an enormous and unmet challenge for most laboratories [4]. This task becomes even more demanding considering that many analytes vary greatly with respect to different covariates of the patient [5]. This problem is often solved by splitting the population into subgroups to determine separate intervals.

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