Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Nonparametric estimation of medical cost quantiles in the presence of competing terminal events

Nonparametric estimation of medical cost quantiles in the presence of competing terminal events Medical care costs are commonly used by health policy-makers and decision-maker for evaluating health care service and decision on treatment plans. This type of data is commonly recorded in surveillance systems when inpatient or outpatient care service is provided. In this paper, we formulate medical cost data as a recurrent marker process, which is composed of recurrent events (inpatient or outpatient cares) and repeatedly measured marker measurements (medical charges). We consider nonparametric estimation of the quantiles of cost distribution among survivors in the absence or presence of competing terminal events. Statistical methods are developed for quantile estimation of the cost distribution for the purposes of evaluating cost performance in relation to recurrent events, marker measurements and time to the terminal event for different competing risk groups. The proposed approaches are illustrated by an analysis of data from the Surveillance, Epidemiology, and End Results (SEER) and Medicare linked database. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Nonparametric estimation of medical cost quantiles in the presence of competing terminal events

Wang, Mei-Cheng; Sun, Yifei
Biostatistics & Epidemiology , Volume 1 (1): 14 – Jan 1, 2017
Download PDF

Nonparametric estimation of medical cost quantiles in the presence of competing terminal events

Abstract

Medical care costs are commonly used by health policy-makers and decision-maker for evaluating health care service and decision on treatment plans. This type of data is commonly recorded in surveillance systems when inpatient or outpatient care service is provided. In this paper, we formulate medical cost data as a recurrent marker process, which is composed of recurrent events (inpatient or outpatient cares) and repeatedly measured marker measurements (medical charges). We consider...
Loading next page...
 
/lp/taylor-francis/nonparametric-estimation-of-medical-cost-quantiles-in-the-presence-of-1sLa0ikNFv
Publisher
Taylor & Francis
Copyright
© 2017 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2017.1342185
Publisher site
See Article on Publisher Site

Abstract

Medical care costs are commonly used by health policy-makers and decision-maker for evaluating health care service and decision on treatment plans. This type of data is commonly recorded in surveillance systems when inpatient or outpatient care service is provided. In this paper, we formulate medical cost data as a recurrent marker process, which is composed of recurrent events (inpatient or outpatient cares) and repeatedly measured marker measurements (medical charges). We consider nonparametric estimation of the quantiles of cost distribution among survivors in the absence or presence of competing terminal events. Statistical methods are developed for quantile estimation of the cost distribution for the purposes of evaluating cost performance in relation to recurrent events, marker measurements and time to the terminal event for different competing risk groups. The proposed approaches are illustrated by an analysis of data from the Surveillance, Epidemiology, and End Results (SEER) and Medicare linked database.

Journal

Biostatistics & Epidemiology – Taylor & Francis

Published: Jan 1, 2017

Keywords: Competing risks; marked recurrent events; nonparametric estimation; quantile estimation

References

  • Nonparametric estimation of the joint distribution of survival time and mark variables
  • Estimating medical costs from incomplete follow-up data
  • Estimating medical costs with censored data
  • Estimating the mean of an increasing stochastic process at a censored stopping time
  • On estimating medical cost and incremental cost-effectiveness ratios with censored data
  • Calibration regression of censored lifetime medical cost
  • Proportional hazards models with continuous marks. Ann Stat.
  • Backward estimation of stochastic processes with failure events as time origins
  • Evaluating utility measurement with recurrent marker processes in the presence of competing terminal events
  • Overview of the SEER-Medicare data: content, research applications, and generalizability to the united states elderly population
  • Cardiovascular disease competes with breast cancer as the leading cause of death for older females diagnosed with breast cancer: a retrospective cohort study
  • Analyzing recurrent event data with informative censoring