Estimating medical costs with censored data

Heejung Bang, Anastasios A. Tsiatis

Research output: Contribution to journalArticle

215 Citations (Scopus)

Abstract

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
JournalBiometrika
Volume87
Issue number2
StatePublished - 2000
Externally publishedYes

Fingerprint

Censored Data
Estimator
Costs and Cost Analysis
Costs
statistics
sampling
Cardiology
Incompleteness
Censoring
Statistical Inference
Censored data
Medical costs
Statistics
Simulation Study
methodology

Keywords

  • 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

Cite this

Bang, H., & Tsiatis, A. A. (2000). Estimating medical costs with censored data. Biometrika, 87(2), 329-343.

Estimating medical costs with censored data. / Bang, Heejung; Tsiatis, Anastasios A.

In: Biometrika, Vol. 87, No. 2, 2000, p. 329-343.

Research output: Contribution to journalArticle

Bang, H & Tsiatis, AA 2000, 'Estimating medical costs with censored data', Biometrika, vol. 87, no. 2, pp. 329-343.
Bang, Heejung ; Tsiatis, Anastasios A. / Estimating medical costs with censored data. In: Biometrika. 2000 ; Vol. 87, No. 2. pp. 329-343.
@article{ee3d782402f64991921b5830e5ce53fc,
title = "Estimating medical costs with censored data",
abstract = "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.",
keywords = "Cost analysis, Counting process, Efficiency, Martingale, Missing data, Semiparametrics, Survival analysis",
author = "Heejung Bang and Tsiatis, {Anastasios A.}",
year = "2000",
language = "English (US)",
volume = "87",
pages = "329--343",
journal = "Biometrika",
issn = "0006-3444",
publisher = "Oxford University Press",
number = "2",

}

TY - JOUR

T1 - Estimating medical costs with censored data

AU - Bang, Heejung

AU - Tsiatis, Anastasios A.

PY - 2000

Y1 - 2000

N2 - 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.

AB - 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.

KW - Cost analysis

KW - Counting process

KW - Efficiency

KW - Martingale

KW - Missing data

KW - Semiparametrics

KW - Survival analysis

UR - http://www.scopus.com/inward/record.url?scp=0001149964&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0001149964&partnerID=8YFLogxK

M3 - Article

VL - 87

SP - 329

EP - 343

JO - Biometrika

JF - Biometrika

SN - 0006-3444

IS - 2

ER -