Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts

Biometrics ◽  
2017 ◽  
Vol 73 (4) ◽  
pp. 1132-1139 ◽  
Author(s):  
Huiming Lin ◽  
Bo Fu ◽  
Guoyou Qin ◽  
Zhongyi Zhu
Keyword(s):  
Longitudinal Data ◽  
Robust Estimation ◽  
Linear Models ◽  
Doubly Robust Estimation ◽  
Partial Linear ◽  
Partial Linear Models ◽  
Doubly Robust

Doubly Robust Estimation with the R Package drgee

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Johan Zetterqvist ◽  
Arvid Sjölander
Keyword(s):  
Robust Estimation ◽  
Linear Models ◽  
Model Misspecification ◽  
R Package ◽  
Epidemiologic Research ◽  
Important Concern ◽  
Doubly Robust Estimation ◽  
Unbiased Estimates ◽  
Doubly Robust ◽  
Biased Estimates

AbstractA common goal of epidemiologic research is to study the association between a certain exposure and a certain outcome, while controlling for important covariates. This is often done by fitting a restricted mean model for the outcome, as in generalized linear models (GLMs) and in generalized estimating equations (GEEs). If the covariates are high-dimensional, then it may be difficult to well specify the model. This is an important concern, since model misspecification may lead to biased estimates. Doubly robust estimation is an estimation technique that offers some protection against model misspecification. It utilizes two models, one for the outcome and one for the exposure, and produces unbiased estimates of the exposure-outcome association if either model is correct, not necessarily both. Despite its obvious appeal, doubly robust estimation is not used on a regular basis in applied epidemiologic research. One reason for this could be the lack of up-to-date software. In this paper we describe a new

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