Estimating medical costs from incomplete follow-up data

D. Y. Lin, E. J. Feuer, R. Etzioni, Y. Wax

Research output: Contribution to journalArticlepeer-review

380 Scopus citations


Estimation of the average total cost for treating patients with a particular disease is often complicated by the fact that the survival times are censored on some study subjects and their subsequent costs are unknown. The naive sample average of the observed costs from all study subjects or from the uncensored cases only can be severely biased, and the standard survival analysis techniques are not applicable. To minimize the bias induced by censoring, we partition the entire time period of interest into a number of small intervals and estimate the average total cost either by the sum of the Kaplan-Meier estimator for the probability of dying in each interval multiplied by the sample mean of the total costs from the observed deaths in that interval or by the sum of the Kaplan Meier estimator for the probability of being alive at the start of each interval multiplied by an appropriate estimator for the average cost over the interval conditional on surviving to the start of the interval. The resultant estimators are consistent if censoring occurs solely at the boundaries of the intervals. In addition, the estimators are asymptotically normal with easily estimated variances. Extensive numerical studies show that the asymptotic approximations are adequate for practical use and the biases of the proposed estimators are small even when censoring may occur in the interiors of the intervals. An ovarian cancer study is provided.

Original languageEnglish (US)
Pages (from-to)419-434
Number of pages16
Issue number2
StatePublished - Jun 1997
Externally publishedYes


  • Censoring
  • Cost analysis
  • Economic evaluation
  • Health services
  • Medical care
  • Missing data
  • Resource utilization
  • Survival analysis
  • Treatment cost

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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