A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data

Type: Article

Publication Date: 2007-05-19

Citations: 76

DOI: https://doi.org/10.1016/j.csda.2007.04.020

Locations

  • Computational Statistics & Data Analysis - View
  • Document Server@UHasselt (UHasselt) - View - PDF

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+ PDF Chat Multiple imputation methods for handling missing values in longitudinal studies with sampling weights: Comparison of methods implemented in Stata 2020 Anurika De Silva
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+ Accounting for interactions and complex inter-subject dependency in estimating treatment effect in cluster randomized trials with missing outcomes 2015 Mélanie Prague
Rui Wang
Alisa Stephens
Eric Tchetgen Tchetgen
Victor DeGruttola
+ Analyzing longitudinal binary data in clinical studies 2022 Yihan Li
Feng Dai
Yunxia Sui
Hong Li
Yanna Song
Tianyu Zhan
Greg Cicconetti
Man Jin
Hongwei Wang
Ivan S. F. Chan
+ PDF Chat A Comparative Analysis of Generalized Estimating Equations Methods for Incomplete Longitudinal Ordinal Data with Ignorable Dropouts 2018 Kago Edwin Ditlhong
Oscar Ngesa
Abdalla Yusuf Kombo
+ Using generalized estimating equations and extensions in randomized trials with missing longitudinal patient reported outcome data 2018 Melanie L. Bell
Nicholas J. Horton
Haryana M. Dhillon
Victoria Bray
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+ The impact of dichotomization in longitudinal data analysis: a simulation study 2009 Bongin Yoo
+ Missing Data 2023 Geert Molenberghs
Caroline Beunckens
Ivy Jansen
Herbert Thijs
Geert Verbeke
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+ Using Multiple Imputation with GEE with Non-monotone Missing Longitudinal Binary Outcomes 2020 Stuart R. Lipsitz
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+ The Impact of Missing Data in a Generalized Integer-Valued Autoregression Model for Count Data 2009 Mohamed Alosh
+ Comparison of Modern Methods for Analyzing Repeated Measures Data With Missing Values 2011 Guillermo Vallejo
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