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Activity Number:
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162
- SPEED: Government Statistics, Health Policy, and Marketing
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Type:
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Contributed
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Date/Time:
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Monday, July 31, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Health Policy Statistics Section
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Abstract #324099
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View Presentation
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Title:
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A Terminal Trend Model for Longitudinal Medical Cost Data and Survival
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Author(s):
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Qian Yang* and Tor D. Tosteson and Anna N. A. Tosteson and Jeffrey C. Munson and Zhigang Li
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Companies:
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Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire and Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire and Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire and Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire and Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
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Keywords:
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Censoring ;
Longitudinal data ;
Joint modeling ;
Osteoporotic fractures ;
Healthcare cost ;
End of life trajectories
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Abstract:
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Background: A joint modeling approach on survival and longitudinal data has proven to be valuable in end of life applications, especially when there is high mortality rate. 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 elderly fracture cohorts with significant mortality by comparing the differences in trajectories among cohorts with different types of fractures, regional and patient factors. Materials and Methods: The data consist of insurance claims for three prospective cohorts of patients with hip, wrist and shoulder fracture among US Medicare beneficiaries age 66 and older during 2007-2011. We use our joint modeling approach for survival and cost data to study individual level trajectories of Medicare cost data and survival. We use two regression sub models: a piecewise exponential model for survival time, and a retrospective spline regression model for costs. Results: Simulations are used to demonstrate the statistical properties of our methods. Conclusion: Our methods are valid and useful for estimating survival and end of life cost trends in fracture cohorts.
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Authors who are presenting talks have a * after their name.
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