Estimating medical costs with censored data

Heejung Bang, Anastasios A. Tsiatis

Research output: Contribution to journalArticlepeer-review

259 Scopus citations


Incompleteness of follow-up data is a common problem in estimating medical costs. Naive analysis using summary statistics on the collected data can result in severely misleading statistical inference. This paper focuses on the problem of estimating the mean medical cost from a sample of individuals whose medical costs may be right censored. A class of weighted estimators which account appropriately for censoring are introduced. Our estimators are shown to be consistent and asymptotically normal with easily estimated variances. The efficiency of these estimators is studied with the goal of finding as efficient an estimator for the mean medical cost as is feasible. Extensive simulation studies are used to show that our estimators perform well in finite samples, even with heavily censored data, for a variety of circumstances. The methods are applied to a set of cost data from a cardiology trial conducted by the Duke University Medical Center. Extensions to other censored data problems are also discussed.

Original languageEnglish (US)
Pages (from-to)329-343
Number of pages15
Issue number2
StatePublished - 2000
Externally publishedYes


  • Cost analysis
  • Counting process
  • Efficiency
  • Martingale
  • Missing data
  • Semiparametrics
  • Survival analysis

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Statistics and Probability
  • Mathematics(all)
  • Applied Mathematics


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