Using inverse probability-weighted estimators in comparative effectiveness analyses with observational databases

被引：431

作者：

Curtis, Lesley H.

Hammill, Bradley G.

Eisenstein, Eric L.

Kramer, Judith M.

Anstrom, Kevin J.

机构：

[1] Duke Clin Res Inst, Ctr Clin & Genet Econ, Durham, NC 27715 USA

[2] Duke Univ, Sch Med, Dept Med, Durham, NC 27706 USA

[3] Duke Univ, Sch Med, Dept Biostat & Bioinformat, Durham, NC USA

来源：

MEDICAL CARE | 2007年 / 45卷 / 10期

关键词：

comparative study; regression analysis; statistical models; propensity score; bias (epidemiology); treatment effects; causal inference; observational studies;

D O I：

10.1097/MLR.0b013e31806518ac

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

Inverse probability-weighted estimation is a powerful tool for use with observational data. In this article, we describe how this propensity score-based method can be used to compare the effectiveness of 2 or more treatments. First, we discuss the inherent problems in using observational data to assess comparative effectiveness. Next, we provide a conceptual explanation of inverse probability-weighted estimation and point readers to sources that address the method in more formal, technical terms. Finally, we offer detailed guidance about how to implement the estimators in comparative effectiveness analyses.

引用

页码：S103 / S107

页数：5

共 25 条

[1] Utilizing propensity scores to estimate causal treatment effects with censored time-lagged data [J].