Innovative Methods for Modeling Longitudinal Medical Costs It is projected that health care costs per person would increase from $8,160 in 2009 to $13,100 in 2018, and that total health care costs will account for over 20% of the gross domestic product by 2018. Statistical analysis of medical cost data is becoming increasingly important with the heightened interests in containing the rising health care cost. Medical cost data are routinely collected in billing records of hospitals and claims of health insurance plans (e.g., Medicare, Medicaid, or commercial insurance). The wide availability of such data has motivated the development and application of the state-of-the-art statistical and econometric methods. With technological advances in automated data collection and management, medical costs are now often gathered at regular time intervals (e.g., daily or monthly), creating a longitudinal data pattern. The objective of this study is to develop and disseminate a number of models to analyze longitudinal medical costs data. There are five aims in this grant. First, we will expand the currently available econometric models of medical costs to longitudinal data and compare the performance of these models. Second, we will explore the use of more flexible functional forms of covariate specification in modeling longitudinal medical cost data. Third, we will extend the above models to jointly analyze medical costs and multiple health outcomes (e.g., survival, or quality of life), and study the effect of risk factors on them simultaneously. Fourth, we will apply hierarchical models to address the clustering effect in modeling longitudinal medical cost at different levels, e.g., health plans, families, and members. Finally, we will develop ready-to-use software to facilitate the practical application of methods developed from the proposed study. In addition to testing the performance of the proposed methods in simulation studies, these innovative methods will be applied to empirical case studies using three real-world databases: Clinical Data Repository (CDR) at the University of Virginia (UVA) Health System, Medical Expenditure Panel Survey (MEPS), and the SEER- Medicare databases. We expect the application of the proposed methods to these case studies will substantially advance our understanding of the influence of demographics, physician practice patterns, diseases, and health policies on the cost of medical care.

Public Health Relevance

Rising health care cost is a major concern for health policy makers. To better understand the factors associated with the growth in medical cost, it is important to study the longitudinal history of medical cost data. We propose to develop better methods to analyze longitudinal medical care costs data. To demonstrate the advantages of our proposed methods in clinical or policy decision making, we will apply these methods to a number of clinical- or policy-relevant case studies. We will also make programming codes of these methods available to other researchers who are interested in medical cost studies.

National Institute of Health (NIH)
Agency for Healthcare Research and Quality (AHRQ)
Research Project (R01)
Project #
Application #
Study Section
Health Systems Research (HSR)
Program Officer
Henderson, Melford
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Northwestern University at Chicago
Public Health & Prev Medicine
Schools of Medicine
United States
Zip Code
Liu, Lei; Huang, Xuelin; Yaroshinsky, Alex et al. (2016) Joint frailty models for zero-inflated recurrent events in the presence of a terminal event. Biometrics 72:204-14
Shen, Chan; Tina Shih, Ya-Chen (2016) Therapeutic substitutions in the midst of new technology diffusion: The case of treatment for localized prostate cancer. Soc Sci Med 151:110-20
Liu, Lei; Strawderman, Robert L; Johnson, Bankole A et al. (2016) Analyzing repeated measures semi-continuous data, with application to an alcohol dependence study. Stat Methods Med Res 25:133-52
Chen, Jinsong; Liu, Lei; Shih, Ya-Chen T et al. (2016) A flexible model for correlated medical costs, with application to medical expenditure panel survey data. Stat Med 35:883-94
Shih, Ya-Chen Tina; Smieliauskas, Fabrice; Geynisman, Daniel M et al. (2015) Trends in the Cost and Use of Targeted Cancer Therapies for the Privately Insured Nonelderly: 2001 to 2011. J Clin Oncol 33:2190-6
Coca Perraillon, Marcelo; Shih, Ya-Chen Tina; Thisted, Ronald A (2015) Predicting the EQ-5D-3L Preference Index from the SF-12 Health Survey in a National US Sample: A Finite Mixture Approach. Med Decis Making 35:888-901
Geynisman, Daniel M; Hu, Jim C; Liu, Lei et al. (2015) Treatment patterns and costs for metastatic renal cell carcinoma patients with private insurance in the United States. Clin Genitourin Cancer 13:e93-100
Carter, Stacey C; Lipsitz, Stuart; Shih, Ya-Chen T et al. (2014) Population-based determinants of radical prostatectomy operative time. BJU Int 113:E112-8
Shih, Ya-Chen Tina; Xu, Ying; Dong, Wenli et al. (2014) First do no harm: population-based study shows non-evidence-based trastuzumab prescription may harm elderly women with breast cancer. Breast Cancer Res Treat 144:417-25
Yu, Zhangsheng; Liu, Lei; Bravata, Dawn M et al. (2014) Joint model of recurrent events and a terminal event with time-varying coefficients. Biom J 56:183-97

Showing the most recent 10 out of 17 publications