Linked Life Data

19432773

Source:http://linkedlifedata.com/resource/pubmed/id/19432773

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2010-5-24
pubmed:abstractText
In this article, we study the estimation of mean response and regression coefficient in semiparametric regression problems when response variable is subject to nonrandom missingness. When the missingness is independent of the response conditional on high-dimensional auxiliary information, the parametric approach may misspecify the relationship between covariates and response while the nonparametric approach is infeasible because of the curse of dimensionality. To overcome this, we study a model-based approach to condense the auxiliary information and estimate the parameters of interest nonparametrically on the condensed covariate space. Our estimators possess the double robustness property, i.e., they are consistent whenever the model for the response given auxiliary covariates or the model for the missingness given auxiliary covariate is correct. We conduct a number of simulations to compare the numerical performance between our estimators and other existing estimators in the current missing data literature, including the propensity score approach and the inverse probability weighted estimating equation. A set of real data is used to illustrate our approach.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1541-0420
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
66
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
115-22
pubmed:dateRevised
2011-8-25
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Adjustment for missingness using auxiliary information in semiparametric regression.
pubmed:affiliation
Department of Biostatistics, University of North Carolina, USA. dzeng@bios.unc.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural