The publication detail shows the title, authors (with indicators showing other profiled authors), information on the publishing organization, abstract and a link to the article in PubMed. This abstract is what is used to create the fingerprint of the publication. If any grants are referenced by the publication, they will be listed here as well.
Limitation of inverse probability-of-censoring weights in estimating survival in the presence of strong selection bias.
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, 27599-7435, USA. email@example.com
American journal of epidemiology 2011;173(5):569-77.
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.
This section shows information related to the publication - computed using the fingerprint of the publication - including related publications, related experts and related grants with fingerprints representing significant amounts of overlap between their fingerprint and this publication. The red dots indicate whether those experts or terms appear within the publication, thereby showing potential and actual connections.
Stephen R Cole; Lisa P Jacobson; Phyllis C Tien; Lawrence Kingsley; Joan S Chmiel; Kathryn AnastosAmerican journal of epidemiology 2010;171(1):113-22.
L P Jacobson; R Li; J Phair; J B Margolick; C R Rinaldo; R Detels; A MuñozAmerican journal of epidemiology 2002;155(8):760-70.
Steven G Deeks; Stephen J Gange; Mari M Kitahata; Michael S Saag; Amy C Justice; Robert S Hogg; Joseph J Eron; John T Brooks; Sean B Rourke; M John Gill; et al.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2009;49(10):1582-90.
Appears in this Publication
Author of this Publication