Semiparametric approach for non-monotone missing covariates in a parametric regression model

Handle URI:
http://hdl.handle.net/10754/599588
Title:
Semiparametric approach for non-monotone missing covariates in a parametric regression model
Authors:
Sinha, Samiran; Saha, Krishna K.; Wang, Suojin
Abstract:
Missing covariate data often arise in biomedical studies, and analysis of such data that ignores subjects with incomplete information may lead to inefficient and possibly biased estimates. A great deal of attention has been paid to handling a single missing covariate or a monotone pattern of missing data when the missingness mechanism is missing at random. In this article, we propose a semiparametric method for handling non-monotone patterns of missing data. The proposed method relies on the assumption that the missingness mechanism of a variable does not depend on the missing variable itself but may depend on the other missing variables. This mechanism is somewhat less general than the completely non-ignorable mechanism but is sometimes more flexible than the missing at random mechanism where the missingness mechansim is allowed to depend only on the completely observed variables. The proposed approach is robust to misspecification of the distribution of the missing covariates, and the proposed mechanism helps to nullify (or reduce) the problems due to non-identifiability that result from the non-ignorable missingness mechanism. The asymptotic properties of the proposed estimator are derived. Finite sample performance is assessed through simulation studies. Finally, for the purpose of illustration we analyze an endometrial cancer dataset and a hip fracture dataset.
Citation:
Sinha S, Saha KK, Wang S (2014) Semiparametric approach for non-monotone missing covariates in a parametric regression model. Biom 70: 299–311. Available: http://dx.doi.org/10.1111/biom.12159.
Publisher:
Wiley-Blackwell
Journal:
Biometrics
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
26-Feb-2014
DOI:
10.1111/biom.12159
PubMed ID:
24571224
PubMed Central ID:
PMC4061254
Type:
Article
ISSN:
0006-341X
Sponsors:
The authors thank the Editor, an Associate Editor, and two referees for their constructive and helpful comments and suggestions which have led to a much improved version of the manuscript, and Hua Yun Chen for kindly providing the hip fracture dataset. This research was partially supported by NSF grant SES 0961618, NIH grant R03CA176760, and Award Number KUS-CI-016-04 made by King Abdullah University of Science and Technology (KAUST). Part of the work was carried out while Wang was visiting Australian National University supported by the Mathematical Sciences Research Visitor Program.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorSinha, Samiranen
dc.contributor.authorSaha, Krishna K.en
dc.contributor.authorWang, Suojinen
dc.date.accessioned2016-02-28T05:53:52Zen
dc.date.available2016-02-28T05:53:52Zen
dc.date.issued2014-02-26en
dc.identifier.citationSinha S, Saha KK, Wang S (2014) Semiparametric approach for non-monotone missing covariates in a parametric regression model. Biom 70: 299–311. Available: http://dx.doi.org/10.1111/biom.12159.en
dc.identifier.issn0006-341Xen
dc.identifier.pmid24571224en
dc.identifier.doi10.1111/biom.12159en
dc.identifier.urihttp://hdl.handle.net/10754/599588en
dc.description.abstractMissing covariate data often arise in biomedical studies, and analysis of such data that ignores subjects with incomplete information may lead to inefficient and possibly biased estimates. A great deal of attention has been paid to handling a single missing covariate or a monotone pattern of missing data when the missingness mechanism is missing at random. In this article, we propose a semiparametric method for handling non-monotone patterns of missing data. The proposed method relies on the assumption that the missingness mechanism of a variable does not depend on the missing variable itself but may depend on the other missing variables. This mechanism is somewhat less general than the completely non-ignorable mechanism but is sometimes more flexible than the missing at random mechanism where the missingness mechansim is allowed to depend only on the completely observed variables. The proposed approach is robust to misspecification of the distribution of the missing covariates, and the proposed mechanism helps to nullify (or reduce) the problems due to non-identifiability that result from the non-ignorable missingness mechanism. The asymptotic properties of the proposed estimator are derived. Finite sample performance is assessed through simulation studies. Finally, for the purpose of illustration we analyze an endometrial cancer dataset and a hip fracture dataset.en
dc.description.sponsorshipThe authors thank the Editor, an Associate Editor, and two referees for their constructive and helpful comments and suggestions which have led to a much improved version of the manuscript, and Hua Yun Chen for kindly providing the hip fracture dataset. This research was partially supported by NSF grant SES 0961618, NIH grant R03CA176760, and Award Number KUS-CI-016-04 made by King Abdullah University of Science and Technology (KAUST). Part of the work was carried out while Wang was visiting Australian National University supported by the Mathematical Sciences Research Visitor Program.en
dc.publisherWiley-Blackwellen
dc.subjectDimension Reductionen
dc.subjectMissing At Randomen
dc.subjectEstimating Equationsen
dc.subjectNon-ignorable Missing Dataen
dc.subjectRobust Methoden
dc.subject.meshModels, Statisticalen
dc.subject.meshRegression Analysisen
dc.titleSemiparametric approach for non-monotone missing covariates in a parametric regression modelen
dc.typeArticleen
dc.identifier.journalBiometricsen
dc.identifier.pmcidPMC4061254en
dc.contributor.institutionDepartment of Statistics, Texas A&M University, College Station, Texas, 77843, U.S.A.en
kaust.grant.numberKUS-CI-016-04en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.