Estimating lifetime medical costs from censored claims data

Health Economics
Jing-Shiang HwangJung-Der Wang

Abstract

Claims databases consisting of routinely collected longitudinal records of medical expenditures are increasingly utilized for estimating expected medical costs of patients with a specific condition. Survival data of the patients of interest are usually highly censored, and observed expenditures are incomplete. In this study, we propose a survival-adjusted estimator for estimating mean lifetime costs, which integrates the product of the survival function and the mean cost function over the lifetime horizon. The survival function is estimated by a new algorithm of rolling extrapolation, aided by external information of age- and sex-matched referents simulated from national vital statistics. The mean cost function is estimated by a weighted average of mean expenditures of patients in a number of months prior to their death, of which the number could be determined by observed costs in their final months, and the weights depend on extrapolated hazards. We evaluate the performance of the proposed approach in comparison with that of a popular method using simulated data under various scenarios and 2 cohorts of intracerebral hemorrhage and ischemic stroke patients with a maximum follow-up of 13 years and conclude that our new method es...Continue Reading

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Citations

Mar 19, 2020·International Journal of Environmental Research and Public Health·Jing-Shiang Hwang, Tsuey-Hwa Hu
Jan 24, 2021·Injury Prevention : Journal of the International Society for Child and Adolescent Injury Prevention·Wei-Chih LienJung-Der Wang
Mar 10, 2021·Updates in Surgery·Yueh-Wei LiuYi-Hao Yen
Mar 7, 2021·Journal of the Formosan Medical Association = Taiwan Yi Zhi·I-Ying LeeJung-Der Wang
Apr 17, 2021·Clinical Journal of the American Society of Nephrology : CJASN·Yu-Tzu ChangChang-Ching Lin
May 23, 2021·Journal of the Formosan Medical Association = Taiwan Yi Zhi·Wu-Wei LaiJung-Der Wang
Apr 21, 2021·American Journal of Epidemiology·Yan-Teng PengWen-Chung Lee

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