TY - JOUR
T1 - A functional generalized method of moments approach for longitudinal studies with missing responses and covariate measurement error
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
SN - 1464-3510
VL - 99
SP - 151
EP - 165
JO - Biometrika
JF - Biometrika
IS - 1
ER -