Linked Life Data

7503045

Source:http://linkedlifedata.com/resource/pubmed/id/7503045

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
12
pubmed:dateCreated
1996-1-18
pubmed:abstractText
Epidemiologic studies often encounter missing covariate values. While simple methods such as stratification on missing-data status, conditional-mean imputation, and complete-subject analysis are commonly employed for handling this problem, several studies have shown that these methods can be biased under reasonable circumstances. The authors review these results in the context of logistic regression and present simulation experiments showing the limitations of the methods. The method based on missing-data indicators can exhibit severe bias even when the data are missing completely at random, and regression (conditional-mean) imputation can be inordinately sensitive to model misspecification. Even complete-subject analysis can outperform these methods. More sophisticated methods, such as maximum likelihood, multiple imputation, and weighted estimating equations, have been given extensive attention in the statistics literature. While these methods are superior to simple methods, they are not commonly used in epidemiology, no doubt due to their complexity and the lack of packaged software to apply these methods. The authors contrast the results of multiple imputation to simple methods in the analysis of a case-control study of endometrial cancer, and they find a meaningful difference in results for age at menarche. In general, the authors recommend that epidemiologists avoid using the missing-indicator method and use more sophisticated methods whenever a large proportion of data are missing.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0002-9262
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
142
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1255-64
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
1995
pubmed:articleTitle
A critical look at methods for handling missing covariates in epidemiologic regression analyses.
pubmed:affiliation
Department of Epidemiology, UCLA School of Public Health, 90095-1772, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Review