scholarly journals Marginal Structural Models, Doubly Robust Estimation, and Bias Analysis in Perinatal and Paediatric Epidemiology

2013 ◽  
Vol 27 (3) ◽  
pp. 263-265 ◽  
Author(s):  
Onyebuchi A. Arah ◽  
Madhuri Sudan ◽  
Jørn Olsen ◽  
Leeka Kheifets
Keyword(s):  
Robust Estimation ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Bias Analysis ◽  
Doubly Robust Estimation ◽  
Doubly Robust

scholarly journals Adjustment for Missing Data in Complex Surveys Using Doubly Robust Estimation

Epidemiology ◽  
2010 ◽  
Vol 21 (6) ◽  
pp. 863-871 ◽  
Author(s):  
Kathleen E. Wirth ◽  
Eric J. Tchetgen Tchetgen ◽  
Megan Murray
Keyword(s):  
Missing Data ◽  
Robust Estimation ◽  
Complex Surveys ◽  
Doubly Robust Estimation ◽  
Doubly Robust

Comparing Alternatives for Estimation from Nonprobability Samples

2019 ◽  
Vol 8 (2) ◽  
pp. 231-263 ◽  
Author(s):  
Richard Valliant
Keyword(s):  
Robust Estimation ◽  
Population Control ◽  
Simulation Studies ◽  
Data Set ◽  
External Data ◽  
Doubly Robust Estimation ◽  
Doubly Robust ◽  
External Population ◽  
Selection Probabilities ◽  
As If

Abstract Three approaches to estimation from nonprobability samples are quasi-randomization, superpopulation modeling, and doubly robust estimation. In the first, the sample is treated as if it were obtained via a probability mechanism, but unlike in probability sampling, that mechanism is unknown. Pseudo selection probabilities of being in the sample are estimated by using the sample in combination with some external data set that covers the desired population. In the superpopulation approach, observed values of analysis variables are treated as if they had been generated by some model. The model is estimated from the sample and, along with external population control data, is used to project the sample to the population. The specific techniques are the same or similar to ones commonly employed for estimation from probability samples and include binary regression, regression trees, and calibration. When quasi-randomization and superpopulation modeling are combined, this is referred to as doubly robust estimation. This article reviews some of the estimation options and compares them in a series of simulation studies.

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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