期刊
AMERICAN JOURNAL OF EPIDEMIOLOGY
卷 173, 期 5, 页码 569-577出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kwq385
关键词
epidemiologic methods; selection bias; survival analysis
资金
- National Institute on Drug Abuse [F31 DA022114]
- National Institute of Allergy and Infectious Diseases [R03 AI071763]
- National Cancer Institute [UO1 AI 35042, 5 MO1 RR 00052, UO1 AI 35043, UO1 AI 35039, UO1 AI 35040, UO1 AI 35041]
In time-to-event analyses, artificial censoring with correction for induced selection bias using inverse probability-of-censoring weights can be used to 1) examine the natural history of a disease after effective interventions are widely available, 2) correct bias due to noncompliance with fixed or dynamic treatment regimens, and 3) estimate survival in the presence of competing risks. Artificial censoring entails censoring participants when they meet a predefined study criterion, such as exposure to an intervention, failure to comply, or the occurrence of a competing outcome. Inverse probability-of-censoring weights use measured common predictors of the artificial censoring mechanism and the outcome of interest to determine what the survival experience of the artificially censored participants would be had they never been exposed to the intervention, complied with their treatment regimen, or not developed the competing outcome. Even if all common predictors are appropriately measured and taken into account, in the context of small sample size and strong selection bias, inverse probability-of-censoring weights could fail because of violations in assumptions necessary to correct selection bias. The authors used an example from the Multicenter AIDS Cohort Study, 1984-2008, regarding estimation of long-term acquired immunodeficiency syndrome-free survival to demonstrate the impact of violations in necessary assumptions. Approaches to improve correction methods are discussed.
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