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Health Services and Outcomes Research Methodology

18-04-2024

A terminal trend model for longitudinal medical cost data and survival

Authors: Qian Yang, Tor D. Tosteson, Anna N. A. Tosteson, Jeffrey C. Munson

Published in: Health Services and Outcomes Research Methodology

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Abstract

A joint modeling approach on survival and longitudinal data has proven to be valuable in end of life applications, especially when there is a strong relationship between the longitudinal measurements and survival outcomes in the presence of high mortality rates. We have implemented a novel joint modeling approach, a flexible terminal trend model, to study the trends and factors influencing end of life cost in clinic observational cohorts with significant mortality by comparing the differences in trajectories due to risk and treatment factors. The motivating data consisted of insurance claims for a cohort of patients with hip fracture among US Medicare beneficiaries age 66 and older during 2007–2011. We have developed a joint modeling approach for survival and cost data to study individual level trajectories of health care cost data and survival, using two regression submodels: a piecewise exponential model for survival time, and a retrospective spline regression model for costs. Model estimation through maximum likelihood methods is developed and evaluated through simulations. An approximation based on a conditional approach is shown to have superior properties and to be far more efficient.
Literature
Bakitas, M., Lyons, K.D., Hegel, M.T., Balan, S., Brokaw, F.C., Seville, J., et al.: Effects of a palliative care intervention on clinical outcomes in patients with advanced cancer: the project enable ii randomized controlled trial. JAMA 302(7), 741 (2009)CrossRefPubMedPubMedCentral
Blough, D.K, Ramsey, S.D.: Using generalized linear models to assess medical care costs. Health Serv. Outcomes Res. Methodol. 1, 185–202 (2000)
Blough, D.K., Madden, C.W., Hornbrook, M.C.: Modeling risk using generalized linear models. J. Health Econ. 18(2), 153–171 (1999)CrossRefPubMed
Buntin, M.B., Zaslavsky, A.M.: Too much ado about two-part models and transformation? J. Health Econ. 23(3), 525–542 (2004)CrossRefPubMed
Bynum, J.P.W., Bell, J.E., Cantu, R.V., Wang, Q., McDonough, C.M., Carmichael, D., et al.: Second fractures among older adults in the year following hip, shoulder or wrist fracture. Osteoporos. Int. J. Establ. Result. Coop. Eur. Found. Osteoporos. Natl. Osteoporos. Found. USA. 27(7), 2207–2215 (2016)CrossRef
Diehr, P., Yanez, D., Ash, A., Hornbrook, M., Lin, D.Y.: Methods for analyzing health care utilization and costs. Annu. Rev. Public Health 20(1), 125–144 (1999)CrossRefPubMed
Diehr, P., Williamson, J., Burke, G.L., Psaty, B.M.: The aging and dying processes and the health of older adults. J. Clin. Epidemiol. 55(3), 269–278 (2002)CrossRefPubMed
Duan, N.: Smearing estimate: a nonparametric retransformation method. J. Am. Stat. Assoc. 78(383), 605–610 (1983)CrossRef
Duan, N., Manning, W.G., Morris, C.N., Newhouse, J.P.: A Comparison of alternative models for the demand for medical care. J Bus Econ Stat. 1(2), 115–126 (1983)CrossRef
Duan, N., Manning, W.G., Morris, C.N., Newhouse, J.P.: Choosing between the sample-selection model and the multi-part model. J. Bus. Econ. Stat. 2(3), 283–289 (1984)CrossRef
Kurland, B.F., Johnson, L.L., Egleston, B.L., Diehr, P.H.: Longitudinal data with follow-up truncated by death: match the analysis method to research aims. Stat. Sci. Rev. J. Inst. Math. Stat. 24(2), 211 (2009)
Li, Z., Tosteson, T.D., Bakitas, M.A.: Joint modeling quality of life and survival using a terminal decline model in palliative care studies. Stat. Med. 32(8), 1394–1406 (2013)CrossRefPubMed
Lin, DY.: Linear regression analysis of censored medical costs. Biostatistics 1(1), 35–47 (2000)
Lin, D.Y., Feuer, E.J., Etzioni, R., Wax, Y.: Estimating medical costs from incomplete follow-up data. Biometrics 53(2), 419–434 (1997)CrossRefPubMed
Manning, WG: A two-part model of the demand for medical care : preliminary results from the health insurance study. Health. Econ. Health. Econ. Proc. World. Congr. Health. Econ. Leiden. Neth. Sept 1980 (1981)
Manning, W.G., Mullahy, J.: Estimating log models: to transform or not to transform? J. Health Econ. 20(4), 461–494 (2001)CrossRefPubMed
McCullagh, P., Nelder JA.: Generalized linear models, Second edition. CRC Press (1989)
Mihaylova, B., Briggs, A., O’Hagan, A., Thompson, S.G.: Review of statistical methods for analysing healthcare resources and costs. Health Econ. 20(8), 897–916 (2011)CrossRefPubMed
Munson, J.C., Bynum, J.P.W., Bell, J.E., Cantu, R., McDonough, C., Wang, Q., et al.: Patterns of prescription drug use before and after fragility fracture. JAMA Intern. Med. 176(10), 1531 (2016)CrossRefPubMedPubMedCentral
Nelder, J.A., Wedderburn, R.W.M.: Generalized linear models. J. Royal Stat. Soc. Ser. Gen. 135(3), 370–384 (1972)CrossRef
US Department of Health and Human Services. CMS statistics. Wash DC Cent Medicare Medicaid Serv. 34, (2004)
Metadata
Title
A terminal trend model for longitudinal medical cost data and survival
Authors
Qian Yang
Tor D. Tosteson
Anna N. A. Tosteson
Jeffrey C. Munson
Publication date
18-04-2024
Publisher
Springer US
Published in
Health Services and Outcomes Research Methodology
Print ISSN: 1387-3741
Electronic ISSN: 1572-9400
DOI
https://doi.org/10.1007/s10742-024-00326-5