Multilevel cluster-weighted models for the evaluation of hospitals

In recent years, increasing attention has been directed toward problems inherent to quality control in healthcare services. In particular, it is necessary to measure effectiveness with respect to improving healthcare outcomes of diagnostic procedures or specific treatment episodes. The performance of hospitals is usually evaluated by multilevel models and other methods for risk adjustment. However, these approaches are not suitable for data with large unobserved heterogeneity. A potentially large source of unobserved heterogeneity comes from the variation of the regression coefficients between groups of individuals sharing similar but unobserved characteristics. To overcome such drawbacks, we propose the multilevel cluster-weighted model, a new mixture model approach for handling hierarchical data.

[1]  Bengt Muthén,et al.  Multilevel regression mixture analysis , 2009 .

[2]  Giorgio Vittadini,et al.  The Generalized Linear Mixed Cluster-Weighted Model , 2015, Journal of Classification.

[3]  Nigel Rice,et al.  A quasi-Monte Carlo comparison of developments in parametric and semi-parametric regression methods for heavy tailed and non-normal data: with an application to healthcare costs , 2013 .

[4]  Antonio Punzo,et al.  Finite mixtures of unimodal beta and gamma densities and the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\end{d , 2012, Computational Statistics.

[5]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[6]  Harvey Goldstein,et al.  League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance , 1996 .

[7]  Christophe Biernacki,et al.  Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models , 2003, Comput. Stat. Data Anal..

[8]  Nigel Rice,et al.  Multilevel Models: Applications to Health Data , 1996, Journal of health services research & policy.

[9]  Salvatore Ingrassia,et al.  Model-based clustering via linear cluster-weighted models , 2012, Comput. Stat. Data Anal..

[10]  Paul D. McNicholas,et al.  Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models , 2010, Comput. Stat. Data Anal..

[11]  M. Glickman,et al.  Statistical Methods for Profiling Providers of Medical Care: Issues and Applications , 1997 .

[12]  Neil Gershenfeld,et al.  Nonlinear Inference and Cluster‐Weighted Modeling , 1997 .

[13]  Dimitris Karlis,et al.  Choosing Initial Values for the EM Algorithm for Finite Mixtures , 2003, Comput. Stat. Data Anal..

[14]  A. Punzo.  Flexible mixture modelling with the polynomial Gaussian cluster-weighted model , 2012, 1207.0939.

[15]  Salvatore Ingrassia,et al.  Decision boundaries for mixtures of regressions , 2016 .

[16]  Alastair H Leyland,et al.  League tables and acute myocardial infarction , 1998, The Lancet.

[17]  Massimo Stafoggia,et al.  P.Re.Val.E.: outcome research program for the evaluation of health care quality in Lazio, Italy , 2012, BMC Health Services Research.

[18]  Gregory R. Hancock,et al.  Advances in Latent Variable Mixture Models , 2007 .

[19]  Giorgio Vittadini,et al.  The effectiveness-efficiency trade-off in health care: The case of hospitals in Lombardy, Italy. , 2014, Regional science and urban economics.

[20]  Christian Geiser,et al.  Data Analysis with Mplus , 2012 .

[21]  B. Lindsay,et al.  The distribution of the likelihood ratio for mixtures of densities from the one-parameter exponential family , 1994 .

[22]  Giorgio Vittadini,et al.  Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions , 2012, J. Classif..

[23]  Michel Wedel,et al.  Concomitant variables in finite mixture models , 2002 .

[24]  S. Normand,et al.  An Administrative Claims Model Suitable for Profiling Hospital Performance Based on 30-Day Mortality Rates Among Patients With an Acute Myocardial Infarction , 2006, Circulation.

[25]  A M Zaslavsky,et al.  Statistical issues in reporting quality data: small samples and casemix variation. , 2001, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[26]  Harvey Goldstein,et al.  Multilevel Statistical Models: Goldstein/Multilevel Statistical Models , 2010 .

[27]  Richard Lilford,et al.  Use and misuse of process and outcome data in managing performance of acute medical care: avoiding institutional stigma , 2004, The Lancet.

[28]  George B. Macready,et al.  Concomitant-Variable Latent-Class Models , 1988 .

[29]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[30]  R H Brook,et al.  Adjusted hospital death rates: a potential screen for quality of medical care. , 1987, American journal of public health.

[31]  Thomas A. Louis,et al.  Statistical Issues in Assessing Hospital Performance , 2012 .