### Abstract

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 language | English (US) |
---|---|

Pages (from-to) | 419-434 |

Number of pages | 16 |

Journal | Biometrics |

Volume | 53 |

Issue number | 2 |

DOIs | |

State | Published - Jun 1 1997 |

Externally published | Yes |

### Fingerprint

### Keywords

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

### ASJC Scopus subject areas

- Agricultural and Biological Sciences(all)
- Public Health, Environmental and Occupational Health
- Agricultural and Biological Sciences (miscellaneous)
- Applied Mathematics
- Statistics and Probability

### Cite this

*Biometrics*,

*53*(2), 419-434. https://doi.org/10.2307/2533947

**Estimating medical costs from incomplete follow-up data.** / Lin, D. Y.; Feuer, E. J.; Etzioni, Ruth; Wax, Y.

Research output: Contribution to journal › Article

*Biometrics*, vol. 53, no. 2, pp. 419-434. https://doi.org/10.2307/2533947

}

TY - JOUR

T1 - Estimating medical costs from incomplete follow-up data

AU - Lin, D. Y.

AU - Feuer, E. J.

AU - Etzioni, Ruth

AU - Wax, Y.

PY - 1997/6/1

Y1 - 1997/6/1

N2 - 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.

AB - 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.

KW - Censoring

KW - Cost analysis

KW - Economic evaluation

KW - Health services

KW - Medical care

KW - Missing data

KW - Resource utilization

KW - Survival analysis

KW - Treatment cost

UR - http://www.scopus.com/inward/record.url?scp=0030910046&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030910046&partnerID=8YFLogxK

U2 - 10.2307/2533947

DO - 10.2307/2533947

M3 - Article

C2 - 9192444

AN - SCOPUS:0030910046

VL - 53

SP - 419

EP - 434

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 2

ER -