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Kurz, C.F. ; Stafford, S.*

Isolating cost drivers in interstitial lung disease treatment using nonparametric Bayesian methods.

Biom. J. 62, 1896-1908 (2020)

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Mixture modeling is a popular approach to accommodate overdispersion, skewness, and multimodality features that are very common for health care utilization data. However, mixture modeling tends to rely on subjective judgment regarding the appropriate number of mixture components or some hypothesis about how to cluster the data. In this work, we adopt a nonparametric, variational Bayesian approach to allow the model to select the number of components while estimating their parameters. Our model allows for a probabilistic classification of observations into clusters and simultaneous estimation of a Gaussian regression model within each cluster. When we apply this approach to data on patients with interstitial lung disease, we find distinct subgroups of patients with differences in means and variances of health care costs, health and treatment covariates, and relationships between covariates and costs. The subgroups identified are readily interpretable, suggesting that this nonparametric variational approach to inference can discover valid insights into the factors driving treatment costs. Moreover, the learning algorithm we employed is very fast and scalable, which should make the technique accessible for a broad range of applications.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Bayesian Statistics ; Health Care Costs ; Lung Disease ; Mixture Model ; Nonparametric Models ; Variational Bayes; Variational Inference; Mixture; Medicine; Models; Care

ISSN (print) / ISBN 0323-3847

e-ISSN 1521-4036

Journal Biometrical Journal

Quellenangaben Volume: 62, Issue: 8, Pages: 1896-1908

Publisher Wiley

Publishing Place Weinheim

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Health Economics and Health Care Management (IGM)