In this paper, we demonstrate the importance of conducting well-thought-out sensitivity analyses for handling clustered data (data in which individuals are grouped into higher order units, such as students in schools) that arise from cluster randomized controlled trials (RCTs). This is particularly relevant given the rise in rigorous impact evaluations that use cluster randomized designs across...

GEEs (generalized estimating equations) provide a framework for flexibly modeling clustered data. Generalized linear model (GLM) components are used to specify marginal mean and variance functions, and “working” covariance models specify multivariate structure. The indefiniteness of the estimation and inference framework is a basis for criticism from theoretical quarters (Crowder, Bka 1995), bu...

The empirical (sandwich or robust) covariance matrix, usually used in conjunction with Generalized Estimating Equations (GEE) and Generalized Linear Mixed Models (GLMM), is biased towards zero for small sample sizes. As a consequence of this bias Type I error rates are usually higher than the nominal levels. Several small sample bias corrections have been proposed in the literature. The purpose...

Longitudinal data sets are comprised of repeated observations of an outcome and a set of covariates for each of many subjects. One objective of statistical analysis is to describe the marginal expectation of the outcome variable as a function of the covariates while accounting for the correlation among the repeated observations for a given subject. This paper proposes a unifying approach to suc...

It is a privilege to be able to peruse the fine article by Ziegler and Vens [1], as well as the contributions made by nine discussants [2]. Over the last 25 years, generalized estimating equations (GEE) have seen an ever further spreading use. Nonetheless, it is a technique confronted with confusion and, at times, misunderstanding. The user must carefully read the technique’s manual. Let us hig...

Many surveys are often complex cross-sectional studies that involve clustered data. Such can have the additional complexity of measurement error problem. Ignoring problem and clustering aspect may lead to incorrect inferences conclusions. The purpose this study was demonstrate application regression calibration correct for covariate in a survey generalized estimating equations (GEE) framework. ...

Electroencephalographic (EEG) recording provides a powerful measure of neural dynamics underlying human cognition, such as working memory. However, the analysis of multidimensional EEG data is challenging because it requires the modeling of temporal and spatial correlations in order to determine the EEG features most predictive of memory performance. Standard techniques, such as generalized est...

normal 0 false false false en-us x-none ar-sa microsoftinternetexplorer4 background: e pilepsy is a common, chronic neurological disorder that affects more than 40 million people worldwide. epilepsy is characterized by interictal and ictal functional disturbances. the presence of interictal epileptiform discharges (ieds) can help to confirm a clinical diagnosis of epilepsy, and their location a...

INTRODUCTION The objective of this study was to determine whether perceptions of the home and school food environments are related to food and beverage intakes of postpartum teens. METHODS Our study was a baseline, cross-sectional analysis of 853 postpartum teens enrolled in a weight-loss intervention study across 27 states from 2007 through 2009. Eight-item scales assessed perceived accessib...

COMPARISON OF DIFFERENT METHODS FOR LONGITUDINAL DATA WITH MISSING OBSERVATIONS Lin Sun July 27, 2010 Longitudinal studies occupy an important role in scientific researches and clinical trials. When taking the analysis of longitudinal data, investigators are often confronted with missing data which will produce potential biases, even in well-controlled condition. In the literature, missing data...