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Liu L, He K, Wang D, Ma S, Qu A, Luan Y, Miller JP, Song Y, Liu L. Health Care Provider Clustering Using Fusion Penalty in Quasi-Likelihood. Biom J 2024; 66:e202300185. [PMID: 39101657 DOI: 10.1002/bimj.202300185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 03/25/2024] [Accepted: 03/29/2024] [Indexed: 08/06/2024]
Abstract
There has been growing research interest in developing methodology to evaluate the health care providers' performance with respect to a patient outcome. Random and fixed effects models are traditionally used for such a purpose. We propose a new method, using a fusion penalty to cluster health care providers based on quasi-likelihood. Without any priori knowledge of grouping information, our method provides a desirable data-driven approach for automatically clustering health care providers into different groups based on their performance. Further, the quasi-likelihood is more flexible and robust than the regular likelihood in that no distributional assumption is needed. An efficient alternating direction method of multipliers algorithm is developed to implement the proposed method. We show that the proposed method enjoys the oracle properties; namely, it performs as well as if the true group structure were known in advance. The consistency and asymptotic normality of the estimators are established. Simulation studies and analysis of the national kidney transplant registry data demonstrate the utility and validity of our method.
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Affiliation(s)
- Lili Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
- Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China
| | - Kevin He
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Di Wang
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Shujie Ma
- Department of Statistics, University of California, Riverside, California, USA
| | - Annie Qu
- Department of Statistics, University of California, Irvine, California, USA
| | - Yihui Luan
- Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China
| | - J Philip Miller
- Division of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Yizhe Song
- Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Lei Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
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2
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Shi Y, Li H, Wang C, Chen J, Jiang H, Shih YCT, Zhang H, Song Y, Feng Y, Liu L. A flexible quasi-likelihood model for microbiome abundance count data. Stat Med 2023; 42:4632-4643. [PMID: 37607718 PMCID: PMC11045296 DOI: 10.1002/sim.9880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 07/28/2023] [Accepted: 08/01/2023] [Indexed: 08/24/2023]
Abstract
In this article, we present a flexible model for microbiome count data. We consider a quasi-likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi-likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package "fql" for the application of our method.
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Affiliation(s)
- Yiming Shi
- Division of Biostatistics, Washington University in St. Louis, St. Louis, Missouri
| | - Huilin Li
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, New York
| | - Chan Wang
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, New York
| | - Jun Chen
- Division of Computational Biology, Mayo Clinic, Rochester, Minnesota
| | - Hongmei Jiang
- Department of Statistics, Northwestern University, Evanston, Illinois
| | - Ya-Chen T. Shih
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Haixiang Zhang
- Center for Applied Mathematics, Tianjin University, Tianjin, China
| | - Yizhe Song
- Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, Missouri
| | - Yang Feng
- Department of Biostatistics, College of Global Public Health, New York University, New York, New York
| | - Lei Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, Missouri
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3
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d'Elbée M, Terris-Prestholt F, Briggs A, Griffiths UK, Larmarange J, Medley GF, Gomez GB. Estimating health care costs at scale in low- and middle-income countries: Mathematical notations and frameworks for the application of cost functions. HEALTH ECONOMICS 2023; 32:2216-2233. [PMID: 37332114 DOI: 10.1002/hec.4722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 04/13/2023] [Accepted: 05/12/2023] [Indexed: 06/20/2023]
Abstract
Appropriate costing and economic modeling are major factors for the successful scale-up of health interventions. Various cost functions are currently being used to estimate costs of health interventions at scale in low- and middle-income countries (LMICs) potentially resulting in disparate cost projections. The aim of this study is to gain understanding of current methods used and provide guidance to inform the use of cost functions that is fit for purpose. We reviewed seven databases covering the economic and global health literature to identify studies reporting a quantitative analysis of costs informing the projected scale-up of a health intervention in LMICs between 2003 and 2019. Of the 8725 articles identified, 40 met the inclusion criteria. We classified studies according to the type of cost functions applied-accounting or econometric-and described the intended use of cost projections. Based on these findings, we developed new mathematical notations and cost function frameworks for the analysis of healthcare costs at scale in LMICs setting. These notations estimate variable returns to scale in cost projection methods, which is currently ignored in most studies. The frameworks help to balance simplicity versus accuracy and increase the overall transparency in reporting of methods.
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Affiliation(s)
- Marc d'Elbée
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
- University of Bordeaux, National Institute for Health and Medical Research (INSERM) UMR 1219, Research Institute for Sustainable Development (IRD) EMR 271, Bordeaux Population Health Centre, Bordeaux, France
- Ceped UMR 196, Université Paris Cité, Research Institute for Sustainable Development (IRD), Inserm, Paris, France
| | - Fern Terris-Prestholt
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
| | - Andrew Briggs
- Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Ulla Kou Griffiths
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
- Health Section, Program Group, UNICEF, New York, New York, USA
| | - Joseph Larmarange
- Ceped UMR 196, Université Paris Cité, Research Institute for Sustainable Development (IRD), Inserm, Paris, France
| | - Graham Francis Medley
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
| | - Gabriella Beatriz Gomez
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
- IAVI, New York, New York, USA
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4
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Wang S, Ning J, Xu Y, Shih YCT, Shen Y, Li L. An extension of estimating equations to model longitudinal medical cost trajectory with Medicare claims data linked to SEER cancer registry. Ann Appl Stat 2023. [DOI: 10.1214/22-aoas1659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Shikun Wang
- Department of Biostatistics, Columbia University
| | - Jing Ning
- Department of Biostatistics, University of Texas MD Anderson Cancer Center
| | - Ying Xu
- Department of Health Services Research, University of Texas MD Anderson Cancer Center
| | - Ya-Chen Tina Shih
- Department of Health Services Research, University of Texas MD Anderson Cancer Center
| | - Yu Shen
- Department of Biostatistics, University of Texas MD Anderson Cancer Center
| | - Liang Li
- Department of Biostatistics, University of Texas MD Anderson Cancer Center
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5
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Zheng Y, Zhao X, Zhang X. Quantile regression for massive data with network-induced dependence, and application to the New York statewide planning and research cooperative system. COMMUN STAT-THEOR M 2022. [DOI: 10.1080/03610926.2020.1786120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Yanqiao Zheng
- Department of Financial Engineering, School of Finance, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Xiaobing Zhao
- School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Xiaoqi Zhang
- Department of Financial Engineering, School of Finance, Zhejiang University of Finance and Economics, Hangzhou, China
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6
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Ren J, Tapert S, Fan CC, Thompson WK. A semi-parametric Bayesian model for semi-continuous longitudinal data. Stat Med 2022; 41:2354-2374. [PMID: 35274335 DOI: 10.1002/sim.9359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 01/21/2022] [Accepted: 02/03/2022] [Indexed: 11/11/2022]
Abstract
Semi-continuous data present challenges in both model fitting and interpretation. Parametric distributions may be inappropriate for extreme long right tails of the data. Mean effects of covariates, susceptible to extreme values, may fail to capture relevant information for most of the sample. We propose a two-component semi-parametric Bayesian mixture model, with the discrete component captured by a probability mass (typically at zero) and the continuous component of the density modeled by a mixture of B-spline densities that can be flexibly fit to any data distribution. The model includes random effects of subjects to allow for application to longitudinal data. We specify prior distributions on parameters and perform model inference using a Markov chain Monte Carlo (MCMC) Gibbs-sampling algorithm programmed in R. Statistical inference can be made for multiple quantiles of the covariate effects simultaneously providing a comprehensive view. Various MCMC sampling techniques are used to facilitate convergence. We demonstrate the performance and the interpretability of the model via simulations and analyses on the National Consortium on Alcohol and Neurodevelopment in Adolescence study (NCANDA) data on alcohol binge drinking.
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Affiliation(s)
- Junting Ren
- Division of Biostatistics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, California, USA.,Population Neuroscience and Genetics Lab, University of California San Diego, La Jolla, California, USA
| | - Susan Tapert
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
| | - Chun Chieh Fan
- Population Neuroscience and Genetics Lab, University of California San Diego, La Jolla, California, USA.,Center for Human Development, University of California San Diego, La Jolla, California, USA
| | - Wesley K Thompson
- Population Neuroscience and Genetics Lab, University of California San Diego, La Jolla, California, USA.,Department of Radiology, University of California San Diego, La Jolla, California, USA
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7
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Wei G, Qin G. Empirical likelihood-based inferences for median medical cost regression models with censored data. J Biopharm Stat 2020; 31:216-232. [PMID: 32951509 DOI: 10.1080/10543406.2020.1821701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Recent studies show that medical cost data can be heavily censored and highly skewed, which leads to have more complex cost data analysis. In this paper, we propose influence function and empirical likelihood (EL)-based methods to construct confidence regions for regression parameters in median cost regression models with censored data. We further propose confidence intervals for the median cost with given covariates using the proposed EL-based confidence regions. Simulation studies are conducted to compare the proposed EL-based confidence regions with the existing normal approximation-based confidence regions in terms of coverage probabilities. The new EL-based methods are observed to have better finite sample performances than existing methods particularly when the censoring proportion is high. The new methods are also illustrated through a real data example.
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Affiliation(s)
- Guanhao Wei
- Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, USA
| | - Gengsheng Qin
- Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, USA
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8
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Abstract
Administrative claims data are big data generated from healthcare encounters. Claims data contain information on insurance payment as well as clinical diagnoses and procedure codes to ascertain medical conditions and treatments, making them valuable sources for economic evaluation research. This paper offers an introductory overview of the use of claims data for oncology-related cost-of-illness, cost comparison, and cost-effectiveness analyses. We reviewed analytical methods commonly employed in these analyses, such as the phase of care approach and net costing method for cost-of-illness studies, propensity score matching methods for cost comparison studies, and net benefit regression models for cost-effectiveness studies. We used published studies to explain each method and to discuss methodological challenges of conducting economic studies using claims data.
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Affiliation(s)
- Ya-Chen Tina Shih
- Section of Cancer Economics and Policy, Department of Health Services Research, The University of Texas M. D. Anderson Cancer Center, Houston, TX.
| | - Lei Liu
- Division of Biostatistics, Washington University School of Medicine in St. Louis, MO
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9
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Kim HJ, Roh T, Choi T. Bayesian analysis of semiparametric Bernstein polynomial regression models for data with sample selection. STATISTICS-ABINGDON 2019. [DOI: 10.1080/02331888.2019.1624964] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Hea-Jung Kim
- Department of Statistics, Dongguk University, Seoul, Republic of Korea
| | - Taeyoung Roh
- Department of Statistics, Korea University, Seoul, Republic of Korea
| | - Taeryon Choi
- Department of Statistics, Korea University, Seoul, Republic of Korea
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10
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Yoon G, Jiang W, Liu L, Shih YCT. Simple Quasi-Bayes Approach for Modeling Mean Medical Costs. Int J Biostat 2019; 16:ijb-2018-0122. [PMID: 31194679 DOI: 10.1515/ijb-2018-0122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 04/26/2019] [Indexed: 11/15/2022]
Abstract
AbstractSeveral statistical issues associated with health care costs, such as heteroscedasticity and severe skewness, make it challenging to estimate or predict medical costs. When the interest is modeling the mean cost, it is desirable to make no assumption on the density function or higher order moments. Another challenge in developing cost prediction models is the presence of many covariates, making it necessary to apply variable selection methods to achieve a balance of prediction accuracy and model simplicity. We propose Spike-or-Slab priors for Bayesian variable selection based on asymptotic normal estimates of the full model parameters that are consistent as long as the assumption on the mean cost is satisfied. In addition, the scope of model searching can be reduced by ranking the Z-statistics. This method possesses four advantages simultaneously: robust (due to avoiding assumptions on the density function or higher order moments), parsimonious (feature of variable selection), informative (due to its Bayesian flavor, which can compare posterior probabilities of candidate models) and efficient (by reducing model searching scope with the use of Z-ranking). We apply this method to the Medical Expenditure Panel Survey dataset.
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Affiliation(s)
- Grace Yoon
- Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX, USA
| | - Wenxin Jiang
- Department of Statistics, Northwestern University, Evanston, IL, USA
| | - Lei Liu
- Department of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
| | - Ya-Chen Tina Shih
- Department of Health Services Research, MD Anderson Cancer Center, Houston, TX, USA
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11
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Liu L, Shih YCT, Strawderman RL, Zhang D, Johnson BA, Chai H. Statistical Analysis of Zero-Inflated Nonnegative Continuous Data: A Review. Stat Sci 2019. [DOI: 10.1214/18-sts681] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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12
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Zhao X, Wang W, Liu L, Shih YCT. A flexible quantile regression model for medical costs with application to Medical Expenditure Panel Survey Study. Stat Med 2018; 37:2645-2666. [PMID: 29722044 DOI: 10.1002/sim.7670] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2016] [Revised: 03/03/2018] [Accepted: 03/08/2018] [Indexed: 11/11/2022]
Abstract
Medical costs are often skewed to the right and heteroscedastic, having a sophisticated relation with covariates. Mean function regression models with low-dimensional covariates have been extensively considered in the literature. However, it is important to develop a robust alternative to find the underlying relationship between medical costs and high-dimensional covariates. In this paper, we propose a new quantile regression model to analyze medical costs. We also consider variable selection, using an adaptive lasso penalized variable selection method to identify significant factors of the covariates. Simulation studies are conducted to illustrate the performance of the estimation method. We apply our method to the analysis of the Medical Expenditure Panel Survey dataset.
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Affiliation(s)
- Xiaobing Zhao
- School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, China
| | - Weiwei Wang
- School of Statistics, East China Normal University, Shanghai, China
| | - Lei Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, U.S.A
| | - Ya-Chen T Shih
- Department of Health Services Research, MD Anderson Cancer Center, Houston, TX, U.S.A
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13
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Li L, Wu CH, Ning J, Huang X, Tina Shih YC, Shen Y. Semiparametric Estimation of Longitudinal Medical Cost Trajectory. J Am Stat Assoc 2018; 113:582-592. [PMID: 30853736 DOI: 10.1080/01621459.2017.1361329] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Estimating the average monthly medical costs from disease diagnosis to a terminal event such as death for an incident cohort of patients is a topic of immense interest to researchers in health policy and health economics because patterns of average monthly costs over time reveal how medical costs vary across phases of care. The statistical challenges to estimating monthly medical costs longitudinally are multifold; the longitudinal cost trajectory (formed by plotting the average monthly costs from diagnosis to the terminal event) is likely to be nonlinear, with its shape depending on the time of the terminal event, which can be subject to right censoring. The goal of this paper is to tackle this statistically challenging topic by estimating the conditional mean cost at any month t given the time of the terminal event s. The longitudinal cost trajectories with different terminal event times form a bivariate surface of t and s, under the constraint t ≤ s. We propose to estimate this surface using bivariate penalized splines in an Expectation-Maximization algorithm that treats the censored terminal event times as missing data. We evaluate the proposed model and estimation method in simulations and apply the method to the medical cost data of an incident cohort of stage IV breast cancer patients from the Surveillance, Epidemiology and End Results-Medicare Linked Database.
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Affiliation(s)
- Liang Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler St., Houston, TX, 77030
| | - Chih-Hsien Wu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
| | - Jing Ning
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler St., Houston, TX, 77030
| | - Xuelin Huang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler St., Houston, TX, 77030
| | - Ya-Chen Tina Shih
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center
| | - Yu Shen
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler St., Houston, TX, 77030
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14
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Smith VA, Preisser JS. A marginalized two-part model with heterogeneous variance for semicontinuous data. Stat Methods Med Res 2018; 28:1412-1426. [PMID: 29451088 DOI: 10.1177/0962280218758358] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Semicontinuous data, characterized by a point mass at zero followed by a positive, continuous distribution, arise frequently in medical research. These data are typically analyzed using two-part mixtures that separately model the probability of incurring a positive outcome and the distribution of positive values among those who incur them. In such a conditional specification, however, standard two-part models do not provide a marginal interpretation of covariate effects on the overall population. We have previously proposed a marginalized two-part model that yields more interpretable effect estimates by parameterizing the model in terms of the marginal mean. In the original formulation, a constant variance was assumed for the positive values. We now extend this model to a more general framework by allowing non-constant variance to be explicitly modeled as a function of covariates, and incorporate this variance into two flexible distributional assumptions, log-skew-normal and generalized gamma, both of which take the log-normal distribution as a special case. Using simulation studies, we compare the performance of each of these models with respect to bias, coverage, and efficiency. We illustrate the proposed modeling framework by evaluating the effect of a behavioral weight loss intervention on health care expenditures in the Veterans Affairs health system.
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Affiliation(s)
- Valerie A Smith
- 1 Center for Health Services Research in Primary Care, Durham VAMC, Durham, NC, USA.,2 Department of Population Health Sciences, Duke University, Durham, NC, USA
| | - John S Preisser
- 3 Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
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15
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Hwang YT, Huang CH, Yeh WL, Shen YD. The weighted general linear model for longitudinal medical cost data – an application in colorectal cancer. J Appl Stat 2017. [DOI: 10.1080/02664763.2016.1169255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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16
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The Portion of Health Care Costs Associated With Lifestyle-Related Modifiable Health Risks Based on a Sample of 223,461 Employees in Seven Industries: The UM-HMRC Study. J Occup Environ Med 2016; 57:1284-90. [PMID: 26641823 DOI: 10.1097/jom.0000000000000600] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE This study estimates the percent of health care costs associated with employees' modifiable health risks. METHODS Cross-sectional multivariate analysis of 223,461 employees from seven industries who completed a health risk assessment during 2007 to 2012. RESULTS Modifiable health risks were associated with 26.0% of health care costs ($761/person) among employees with no self-reported medical conditions and 25.4% among employees with a medical condition ($2598/person). The prevalence and relative costs of each of the 10 risks were different for those without and with medical conditions, but high body mass index was the most prevalent risk for both groups (41.0% and 63.9%) and also contributed the largest percentage of excess costs (7.2% and 7.3%). CONCLUSIONS This study, coupled with past work, gives an employer a sense of the magnitude that might be saved if modifiable health risks could be eliminated.
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17
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Chen J, Liu L, Shih YCT, Zhang D, Severini TA. A flexible model for correlated medical costs, with application to medical expenditure panel survey data. Stat Med 2015; 35:883-94. [PMID: 26403805 DOI: 10.1002/sim.6743] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Revised: 08/25/2015] [Accepted: 08/31/2015] [Indexed: 11/07/2022]
Abstract
We propose a flexible model for correlated medical cost data with several appealing features. First, the mean function is partially linear. Second, the distributional form for the response is not specified. Third, the covariance structure of correlated medical costs has a semiparametric form. We use extended generalized estimating equations to simultaneously estimate all parameters of interest. B-splines are used to estimate unknown functions, and a modification to Akaike information criterion is proposed for selecting knots in spline bases. We apply the model to correlated medical costs in the Medical Expenditure Panel Survey dataset. Simulation studies are conducted to assess the performance of our method.
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Affiliation(s)
- Jinsong Chen
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, U.S.A
| | - Lei Liu
- Department of Preventive Medicine, Northwestern University, Chicago, IL, U.S.A
| | - Ya-Chen T Shih
- Department of Medicine, The University of Chicago, Chicago, IL, U.S.A
| | - Daowen Zhang
- Department of Statistics, North Carolina State University, Raleigh, NC, U.S.A
| | - Thomas A Severini
- Department of Statistics, Northwestern University, Evanston, IL, U.S.A
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