Linked Life Data

21495061

Source:http://linkedlifedata.com/resource/pubmed/id/21495061

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
15
pubmed:dateCreated
2011-6-14
pubmed:abstractText
In matched case-crossover studies, it is generally accepted that covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression model because any stratum effect is removed by the conditioning on the fixed number of sets of a case and controls in the stratum. Hence, the conditional logistic regression model is not able to detect any effects associated with the matching covariates by stratum. In addition, the matching covariates may be effect modification and the methods for assessing and characterizing effect modification by matching covariates are quite limited. In this article, we propose a unified approach in its ability to detect both parametric and nonparametric relationships between the predictor and the relative risk of disease or binary outcome, as well as potential effect modifications by matching covariates. Two methods are developed using two semiparametric models: (1) the regression spline varying coefficients model and (2) the regression spline interaction model. Simulation results show that the two approaches are comparable. These methods can be used in any matched case-control study and extend to multilevel effect modification studies. We demonstrate the advantage of our approach using an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis associated with drinking water turbidity.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1097-0258
pubmed:author
pubmed:copyrightInfo
Copyright © 2011 John Wiley & Sons, Ltd.
pubmed:issnType
Electronic
pubmed:day
10
pubmed:volume
30
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1837-51
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Semiparametric regression models for detecting effect modification in matched case-crossover studies.
pubmed:affiliation
Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, U.S.A.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't