scholarly journals Variable selection and inference procedures for marginal analysis of longitudinal data with missing observations and covariate measurement error

2015 ◽  
Vol 43 (4) ◽  
pp. 498-518 ◽  
Author(s):  
Grace Y. Yi ◽  
Xianming Tan ◽  
Runze Li
Keyword(s):  
Measurement Error ◽  
Variable Selection ◽  
Longitudinal Data ◽  
Missing Observations ◽  
Marginal Analysis ◽  
Covariate Measurement Error

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

Biometrika ◽  
2012 ◽  
Vol 99 (1) ◽  
pp. 151-165 ◽  
Author(s):  
Grace Y. Yi ◽  
Yanyuan Ma ◽  
Raymond J. Carroll
Keyword(s):  
Measurement Error ◽  
Longitudinal Data ◽  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Marginal Analysis ◽  
Theoretical Justification ◽  
Simple Method ◽  
Missing Responses ◽  
Covariate Measurement Error ◽  
Time Varying Covariates

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.

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