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An improved survival estimator for censored medical costs with a kernel approach

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  • Shuai Chen
  • Wenbin Lu
  • Hongwei Zhao

Abstract

Cost assessment serves as an essential part in economic evaluation of medical interventions. In many studies, costs as well as survival data are frequently censored. Standard survival analysis techniques are often invalid for censored costs, due to the induced dependent censoring problem. Owing to high skewness in many cost data, it is desirable to estimate the median costs, which will be available with estimated survival function of costs. We propose a kernel-based survival estimator for costs, which is monotone, consistent, and more efficient than several existing estimators. We conduct numerical studies to examine the finite-sample performance of the proposed estimator.

Suggested Citation

  • Shuai Chen & Wenbin Lu & Hongwei Zhao, 2018. "An improved survival estimator for censored medical costs with a kernel approach," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(23), pages 5702-5716, December.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:23:p:5702-5716
    DOI: 10.1080/03610926.2017.1400059
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