Abstract:
PURPOSE:Selection bias is a form of systematic error that can be severe in compromised study designs such as case-control studies with inappropriate selection mechanisms or follow-up studies that suffer from extensive attrition. External adjustment for selection bias is commonly undertaken when such bias is suspected, but the methods used can be overly simplistic, if not unrealistic, and fail to allow for simultaneous adjustment of associations of the exposure and covariates with the outcome, when of interest. Internal adjustment for selection bias via inverse probability weighting allows bias parameters to vary with the levels of covariates but has only been formalized for longitudinal studies with covariate data on patients up until loss to follow-up. METHODS:We demonstrate the use of inverse probability weighting and externally obtained bias parameters to perform internal adjustment of selection bias in studies lacking covariate data on unobserved participants. RESULTS:The "true" or selection-adjusted odds ratio for the association between exposure and outcome was successfully obtained by analyzing only data on those in the selected stratum (i.e., responders) weighted by the inverse probability of their being selected as function of their observed covariate data. CONCLUSIONS:This internal adjustment technique using user-supplied bias parameters and inverse probability weighting for selection bias can be applied to any type of observational study.
journal_name
Ann Epidemioljournal_title
Annals of epidemiologyauthors
Thompson CA,Arah OAdoi
10.1016/j.annepidem.2014.07.014subject
Has Abstractpub_date
2014-10-01 00:00:00pages
747-53issue
10eissn
1047-2797issn
1873-2585pii
S1047-2797(14)00364-0journal_volume
24pub_type
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journal_title:Annals of epidemiology
pub_type: 杂志文章
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journal_title:Annals of epidemiology
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journal_title:Annals of epidemiology
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pub_type: 杂志文章
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pub_type: 临床试验,杂志文章,多中心研究,随机对照试验
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journal_title:Annals of epidemiology
pub_type: 杂志文章
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更新日期:2016-08-01 00:00:00