Isolating cost drivers in interstitial lung disease treatment using nonparametric Bayesian methods
Abstract: 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.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Isolating cost drivers in interstitial lung disease treatment using nonparametric Bayesian methods ; volume:62 ; number:8 ; year:2020 ; pages:1896-1908 ; extent:13
Biometrical journal ; 62, Heft 8 (2020), 1896-1908 (gesamt 13)
- Creator
- DOI
-
10.1002/bimj.202000076
- URN
-
urn:nbn:de:101:1-2022070508543100392712
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:27 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Kurz, Christoph
- Stafford, Seth
Other Objects (12)
Efficient Bayesian nonparametric hazard regression
Bayesian nonparametric models for networks
Introduction to Nonparametric Bayesian Models
Efficient Bayesian Nonparametric Hazard Regression
Bayesian nonparametric inference for queueing systems
Practical nonparametric and semiparametric Bayesian statistics
Spatial non-parametric Bayesian clustered coefficients
Bayesian modeling via discrete nonparametric priors
Bayesian Nonparametric Inference for Queueing Systems
Statistical relational learning with nonparametric Bayesian models
Decompounding discrete distributions : a nonparametric Bayesian approach
Nonparametric Bayesian inverse problems with Laplacian noise
Efficient Bayesian nonparametric hazard regression
Bayesian nonparametric models for networks
Introduction to Nonparametric Bayesian Models
Efficient Bayesian Nonparametric Hazard Regression
Bayesian nonparametric inference for queueing systems
Practical nonparametric and semiparametric Bayesian statistics
Spatial non-parametric Bayesian clustered coefficients
Bayesian modeling via discrete nonparametric priors
Bayesian Nonparametric Inference for Queueing Systems
Statistical relational learning with nonparametric Bayesian models
Decompounding discrete distributions : a nonparametric Bayesian approach
Nonparametric Bayesian inverse problems with Laplacian noise
Efficient Bayesian nonparametric hazard regression
Bayesian nonparametric models for networks
Introduction to Nonparametric Bayesian Models
Efficient Bayesian Nonparametric Hazard Regression
Bayesian nonparametric inference for queueing systems
Practical nonparametric and semiparametric Bayesian statistics
Spatial non-parametric Bayesian clustered coefficients
Bayesian modeling via discrete nonparametric priors
Bayesian Nonparametric Inference for Queueing Systems
Statistical relational learning with nonparametric Bayesian models
Decompounding discrete distributions : a nonparametric Bayesian approach