An investigation of penalization and data augmentation to improve convergence of generalized estimating equations for clustered binary outcomes

被引：0

作者：

Angelika Geroldinger

Rok Blagus

Helen Ogden

Georg Heinze

机构：

[1] Section for Clinical Biometrics,Medical University of Vienna, Center for Medical Statistics, Informatics and Intelligent System

[2] University of Ljubljana,undefined

[3] Institute of Biostatistics and Medical Informatics,undefined

[4] University of Southampton,undefined

[5] School of Mathematical Sciences,undefined

来源：

BMC Medical Research Methodology | / 22卷

关键词：

Clustered data; Firth’s logistic regression; Generalized estimating equations; Logistic regression; Non-convergence; Separation;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

引用

共 45 条

[1]

Albert A(1984)On the existence of maximum-likelihood estimates in logistic-regression models Biometrika 71 1-10

[2]

Anderson JA(1993)Bias reduction of maximum-likelihood-estimates Biometrika 80 27-38

[3]

Firth D(2014)Small sample GEE estimation of regression parameters for longitudinal data Stat Med 33 3869-3881

[4]

Paul S(2019)Bias-reduced and separation-proof GEE with small or sparse longitudinal binary data Stat Med 38 2544-2560

[5]

Zhang XM(2002)A solution to the problem of separation in logistic regression Stat Med 21 2409-2419

[6]

Mondol MH(2021)Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models Biometrika 108 71-82

[7]

Rahman MS(2017)Firth's logistic regression with rare events: accurate effect estimates and predictions? Stat Med 36 2302-2317

[8]

Heinze G(1986)Longitudinal data-analysis using generalized linear-models Biometrika 73 13-22

[9]

Schemper M(2007)Diagnostic techniques in generalized estimating equations J Stat Comput Simul 77 879-888

[10]

Kosmidis I(2015)Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes Stat Med 34 281-296