Results 201 to 210 of about 852,757 (260)

Performance of Generalized Estimating Equations in Practical Situations

Biometrics, 1994
Moment methods for analyzing repeated binary responses have been proposed by Liang and Zeger (1986, Biometrika 73, 13-22), and extended by Prentice (1988, Biometrics 44, 1033-1048). In their generalized estimating equations (GEE), both Liang and Zeger (1986) and Prentice (1988) estimate the parameters associated with the expected value of an individual'
Stuart R. Lipsitz, Endel J. Orav, Garrett M. Fitzmaurice, Nan M. Laird   +3 more
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Using generalized estimating equations and extensions in randomized trials with missing longitudinal patient reported outcome data

Psycho-Oncology, 2018
Patient reported outcomes (PROs) are important in oncology research; however, missing data can pose a threat to the validity of results. Psycho‐oncology researchers should be aware of the statistical options for handling missing data robustly. One rarely
M. Bell, N. Horton, H. Dhillon, V. Bray, J. Vardy   +4 more
semanticscholar   +1 more source

Generalized Estimating Equation

2017
The generalized estimating equation (GEE) uses a quasi-likelihood approach for analyzing data with correlated outcomes. This is an extension of GLM and uses quasi-likelihood method for cluster or repeated outcomes. If observations on outcome variable are repeated, it is likely that the observations are correlated.
Rafiqul I. Chowdhury, M. Ataharul Islam
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Generalized Estimating Equations [PDF]

, 2011
Longitudinal data occurs frequently in medical studies, particularly in clinical trials. Often, the response variable is nonnormal, for example, it may be a binary variable, that is, “improved” or “not improved”. The approach to the analysis of such data discussed in this entry is the use of generalized estimating equations, which, while making weaker ...
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Generalized Estimating Equations

Journal of the American Statistical Association, 2004
(2004). Generalized Estimating Equations. Journal of the American Statistical Association: Vol. 99, No. 465, pp. 297-298.
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Diagnostic techniques in generalized estimating equations

Journal of Statistical Computation and Simulation, 2007
We consider herein diagnostic methods for the quasi-likelihood regression models developed by Zeger and Liang [Zeger, S. L., Liang, K.-Y., 1986, Longitudinal data analysis for discrete and conti-nuous outcomes. Biometrics, 42, 121–130.] to analyse discrete and continuous longitudinal data. Our proposal generalises well-known measures (projection matrix,
Maria Kelly Venezuela, Denise Aparecida Botter, Mônica Carneiro Sandoval   +2 more
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A priori Estimates of Generalized Solutions of the Wave Equation

Differential Equations, 2001
This paper is devoted to obtain a priori estimates for generalized solutions of the wave equation \[ u_{tt}(x,t)- u_{xx}(x,t)= 0\tag{1} \] in the spaces \(W^1_2\) and \(L_2\) with zero initial conditions \(u(x,0)=0\) and \(u_t(x,0)=0\) for \(0\leq x\leq \ell\). The author considers two cases: a) both boundary conditions \(u(0,t)= \mu(t)\), \(u(\ell,t)=
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SAGA Application for Generalized Estimating Equations Analysis

2023
Logistic regression models seek to identify the influence of different variables/factors on a response variable of interest. These are normally used in the field of medicine as it allows verifying which factors influence the presence of certain pathologies. However, most of these models do not consider the correlation between the variables under study.
Luís Moncaixa, Ana Cristina Braga
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Local Influence in Generalized Estimating Equations

Scandinavian Journal of Statistics, 2007
Abstract. We investigate the influence of subjects or observations on regression coefficients of generalized estimating equations (GEEs) using local influence. The GEE approach does not require the full multivariate distribution of the response vector. We extend the likelihood displacement to a quasi‐likelihood displacement, and propose local influence
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On the efficiency of extended generalized estimating equation approaches

Statistics & Probability Letters, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pranesh Kumar, Brajendra C. Sutradhar
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