A functional generalized method of moments approach for longitudinal studies with missing responses and covariate measurement error

Grace Y. Yi; Yanyuan Ma; Raymond J. Carroll

doi:10.1093/biomet/asr076

A functional generalized method of moments approach for longitudinal studies with missing responses and covariate measurement error

Grace Y. Yi, Yanyuan Ma, Raymond J. Carroll

Research output: Contribution to journal › Article › peer-review

39 Scopus citations

Abstract

Abstract Covariate measurement error and missing responses are typical features in longitudinal data analysis. There has been extensive research on either covariate measurement error or missing responses, but relatively little work has been done to address both simultaneously. In this paper, we propose a simple method for the marginal analysis of longitudinal data with time-varying covariates, some of which are measured with error, while the response is subject to missingness. Our method has a number of appealing properties: assumptions on the model are minimal, with none needed about the distribution of the mismeasured covariate; implementation is straightforward and its applicability is broad. We provide both theoretical justification and numerical results.

Original language	English (US)
Pages (from-to)	151-165
Number of pages	15
Journal	Biometrika
Volume	99
Issue number	1
DOIs	https://doi.org/10.1093/biomet/asr076
State	Published - Feb 2 2012
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2021-03-31
Acknowledgements: The authors thank the referees for their helpful comments. Yi’s research was supported by the Natural Sciences and Engineering Research Council of Canada. Ma’s research was supported by the National Science Foundation and the National Institute of Neurological Disorders and Stroke. Carroll’s research was supported by the National Cancer Institute, the National Institute of Neurological Disorders and Stroke and the King Abdullah University of Science and Technology.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

General Agricultural and Biological Sciences
Applied Mathematics
Statistics and Probability
Statistics, Probability and Uncertainty
General Mathematics
Agricultural and Biological Sciences (miscellaneous)

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10.1093/biomet/asr076

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title = "A functional generalized method of moments approach for longitudinal studies with missing responses and covariate measurement error",

abstract = "Abstract Covariate measurement error and missing responses are typical features in longitudinal data analysis. There has been extensive research on either covariate measurement error or missing responses, but relatively little work has been done to address both simultaneously. In this paper, we propose a simple method for the marginal analysis of longitudinal data with time-varying covariates, some of which are measured with error, while the response is subject to missingness. Our method has a number of appealing properties: assumptions on the model are minimal, with none needed about the distribution of the mismeasured covariate; implementation is straightforward and its applicability is broad. We provide both theoretical justification and numerical results.",

author = "Yi, {Grace Y.} and Yanyuan Ma and Carroll, {Raymond J.}",

note = "KAUST Repository Item: Exported on 2021-03-31 Acknowledgements: The authors thank the referees for their helpful comments. Yi{\textquoteright}s research was supported by the Natural Sciences and Engineering Research Council of Canada. Ma{\textquoteright}s research was supported by the National Science Foundation and the National Institute of Neurological Disorders and Stroke. Carroll{\textquoteright}s research was supported by the National Cancer Institute, the National Institute of Neurological Disorders and Stroke and the King Abdullah University of Science and Technology. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2012",

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doi = "10.1093/biomet/asr076",

language = "English (US)",

volume = "99",

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journal = "Biometrika",

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AU - Yi, Grace Y.

AU - Ma, Yanyuan

AU - Carroll, Raymond J.

N1 - KAUST Repository Item: Exported on 2021-03-31 Acknowledgements: The authors thank the referees for their helpful comments. Yi’s research was supported by the Natural Sciences and Engineering Research Council of Canada. Ma’s research was supported by the National Science Foundation and the National Institute of Neurological Disorders and Stroke. Carroll’s research was supported by the National Cancer Institute, the National Institute of Neurological Disorders and Stroke and the King Abdullah University of Science and Technology. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2012/2/2

Y1 - 2012/2/2

N2 - Abstract Covariate measurement error and missing responses are typical features in longitudinal data analysis. There has been extensive research on either covariate measurement error or missing responses, but relatively little work has been done to address both simultaneously. In this paper, we propose a simple method for the marginal analysis of longitudinal data with time-varying covariates, some of which are measured with error, while the response is subject to missingness. Our method has a number of appealing properties: assumptions on the model are minimal, with none needed about the distribution of the mismeasured covariate; implementation is straightforward and its applicability is broad. We provide both theoretical justification and numerical results.

AB - Abstract Covariate measurement error and missing responses are typical features in longitudinal data analysis. There has been extensive research on either covariate measurement error or missing responses, but relatively little work has been done to address both simultaneously. In this paper, we propose a simple method for the marginal analysis of longitudinal data with time-varying covariates, some of which are measured with error, while the response is subject to missingness. Our method has a number of appealing properties: assumptions on the model are minimal, with none needed about the distribution of the mismeasured covariate; implementation is straightforward and its applicability is broad. We provide both theoretical justification and numerical results.

UR - http://hdl.handle.net/10754/668397

UR - https://academic.oup.com/biomet/article/99/1/151/252316

UR - http://www.scopus.com/inward/record.url?scp=84863274565&partnerID=8YFLogxK

U2 - 10.1093/biomet/asr076

DO - 10.1093/biomet/asr076

M3 - Article

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VL - 99

SP - 151

EP - 165

JO - Biometrika

JF - Biometrika

IS - 1

ER -

A functional generalized method of moments approach for longitudinal studies with missing responses and covariate measurement error

Abstract

Bibliographical note

ASJC Scopus subject areas

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