"generalized estimating equations"
83463958
Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data
Background Stress levels among college students have been on the rise for the last few decades. Currently, rates of reported stress among college students are at an all time high. Traditionally, the dominant way to assess stress levels has been through pen and paper surveys. Objective The aim of this study is to use passive sensing data collected via mobile phones to obtain a rich and potentially less biased source of data that can be used to help better understand stressors in the college experience. Methods We used a mobile sensing app, StudentLife, in tandem with a pictorial mobile phone based measure of stress, the Mobile Photographic Stress Meter, to investigate the situations and contexts that are more likely to precipitate stress. Results Using recently developed methods for handling high dimensional longitudinal data, penalized generalized estimating equations, we identified a set of mobile sensing features (absolute values of beta >0.001 and robust z>1.96) across the domains of social activity, movement, location, and ambient noise that were predictive of student stress levels. Conclusions By combining recent statistical methods and mobile phone sensing, we have been able to study stressors in the college experience in a way that is more objective, detailed, and less intrusive than past research. Future work can leverage information gained from passive sensing and use that to develop real time, targeted interventions for students experiencing a stressful time.
JMIR mHealth and uHealth
Alex W DaSilva Jeremy F Huckins Rui Wang Weichen Wang Dylan D Wagner Andrew T Campbell
55258195
TENSOR GENERALIZED ESTIMATING EQUATIONS FOR LONGITUDINAL IMAGING ANALYSIS.
Longitudinal neuroimaging studies are becoming increasingly prevalent, where brain images are collected on multiple subjects at multiple time points. Analyses of such data are scientifically important, but also challenging. Brain images are in the form of multidimensional arrays, or tensors, which are characterized by both ultrahigh dimensionality and a complex structure. Longitudinally repeated images and induced temporal correlations add a further layer of complexity. Despite some recent efforts, there exist very few solutions for longitudinal imaging analyses. In response to the increasing need to analyze longitudinal imaging data, we propose several tensor generalized estimating equations (GEEs) The proposed GEE approach accounts for intra subject correlation, and an imposed low rank structure on the coefficient tensor effectively reduces the dimensionality. We also propose a scalable estimation algorithm, establish the asymptotic properties of the solution to the tensor GEEs, and investigate sparsity regularization for the purpose of region selection. We demonstrate the proposed method using simulations and by analyzing a real data set from the Alzheimer's Disease Neuroimaging Initiative.
Statistica Sinica
Xiang Zhang Lexin Li Hua Zhou Yeqing Zhou Dinggang Shen
7862441
Akaike's information criterion in generalized estimating equations.
Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model selection criteria available in GEE. The well known Akaike Information Criterion (AIC) cannot be directly applied since AIC is based on maximum likelihood estimation while GEE is nonlikelihood based. We propose a modification to AIC, where the likelihood is replaced by the quasi likelihood and a proper adjustment is made for the penalty term. Its performance is investigated through simulation studies. For illustration, the method is applied to a real data set.
Biometrics
Wei Pan
29157915
Modelling correlated data: Multilevel models and generalized estimating equations and their use with data from research in developmental disabilities.
BACKGROUND The use of Multilevel Models (MLM) and Generalized Estimating Equations (GEE) for analysing clustered data in the field of intellectual and developmental disability (IDD) research is still limited. METHOD We present some important features of MLMs and GEEs: main function, assumptions, model specification and estimators, sample size and power. We provide an overview of the ways MLMs and GEEs have been used in IDD research. RESULTS While MLMs and GEEs are both appropriate for longitudinal and/or clustered data, they differ in the assumptions they impose on the data, and the inferences made. Estimators in MLMs require appropriate model specification, while GEEs are more resilient to misspecification at the expense of model complexity. Studies on sample size seem to suggest that Level 1 coefficients are robust to small samples/clusters, with any higher level coefficients less so. MLMs have been used more frequently than GEEs in IDD research, especially for fitting developmental trajectories. CONCLUSIONS Clustered data from research in the IDD field can be analysed flexibly using MLMs and GEEs. These models would be more widely used if journals required the inclusion of technical specification detail, simulation studies examined power for IDD study characteristics, and researchers developed core skills during basic studies.
Research in developmental disabilities
Dimitrios Vagenas Vasiliki Totsika
90027095
Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences
Stano Pekar Marek Brabec
85544794
A Comparison of Correlation Structure Selection Penalties for Generalized Estimating Equations
ABSTRACT Correlated data are commonly analyzed using models constructed using population averaged generalized estimating equations (GEEs) The specification of a population averaged GEE model includes selection of a structure describing the correlation of repeated measures. Accurate specification of this structure can improve efficiency, whereas the finite sample estimation of nuisance correlation parameters can inflate the variances of regression parameter estimates. Therefore, correlation structure selection criteria should penalize, or account for, correlation parameter estimation. In this article, we compare recently proposed penalties in terms of their impacts on correlation structure selection and regression parameter estimation, and give practical considerations for data analysts. Supplementary materials for this article are available online.
The American statistician
Philip M Westgate Woodrow Burchett
4053117
Selection of working correlation structure in generalized estimating equations.
The well known generalized estimating equations is a very popular approach for analyzing longitudinal data. Selecting an appropriate correlation structure in the generalized estimating equations framework is a key step for estimating parameters efficiently and deriving reliable statistical inferences. We present two new criteria for selecting the best among the candidates with any arbitrary structures, even for irregularly timed measurements. The simulation results demonstrate that the new criteria perform more similarly to EAIC and EBIC as the sample size becomes large. However, their performance is much enhanced when the sample size is small and the number of measurements is large. Finally, three real datasets are used to illustrate the proposed criteria. Copyright (c) 2017 John Wiley Sons, Ltd.
Statistics in medicine
You-Gan Wang Liya Fu
22142647
One Step Generalized Estimating Equations With Large Cluster Sizes
ABSTRACT Medical studies increasingly involve a large sample of independent clusters, where the cluster sizes are also large. Our motivating example from the 2010 Nationwide Inpatient Sample (NIS) has 8,001,068 patients and 1049 clusters, with average cluster size of 7627. Consistent parameter estimates can be obtained naively assuming independence, which are inefficient when the intra cluster correlation (ICC) is high. Efficient generalized estimating equations (GEE) incorporate the ICC and sum all pairs of observations within a cluster when estimating the ICC. For the 2010 NIS, there are 92.6 billion pairs of observations, making summation of pairs computationally prohibitive. We propose a one step GEE estimator that (1) matches the asymptotic efficiency of the fully iterated GEE; (2) uses a simpler formula to estimate the ICC that avoids summing over all pairs; and (3) completely avoids matrix multiplications and inversions. These three features make the proposed estimator much less computationally intensive, especially with large cluster sizes. A unique contribution of this article is that it expresses the GEE estimating equations incorporating the ICC as a simple sum of vectors and scalars.
Journal of computational and graphical statistics a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
Stuart R Lipsitz Garrett M Fitzmaurice Debajyoti Sinha Nathanael D Hevelone Jim C Hu Louis L Nguyen
14678825
Statistical analysis of correlated data using generalized estimating equations: an orientation.
The method of generalized estimating equations (GEE) is often used to analyze longitudinal and other correlated response data, particularly if responses are binary. However, few descriptions of the method are accessible to epidemiologists. In this paper, the authors use small worked examples and one real data set, involving both binary and quantitative response data, to help end users appreciate the essence of the method. The examples are simple enough to see the behind the scenes calculations and the essential role of weighted observations, and they allow nonstatisticians to imagine the calculations involved when the GEE method is applied to more complex multivariate data.
American journal of epidemiology
James A Hanley Abdissa Negassa Michael D deB Edwardes Janet Forrester
9296752
Covariance estimators for generalized estimating equations (GEE) in longitudinal analysis with small samples.
Generalized estimating equations (GEE) is a general statistical method to fit marginal models for longitudinal data in biomedical studies. The variance covariance matrix of the regression parameter coefficients is usually estimated by a robust "sandwich" variance estimator, which does not perform satisfactorily when the sample size is small. To reduce the downward bias and improve the efficiency, several modified variance estimators have been proposed for bias correction or efficiency improvement. In this paper, we provide a comprehensive review on recent developments of modified variance estimators and compare their small sample performance theoretically and numerically through simulation and real data examples. In particular, Wald tests and t tests based on different variance estimators are used for hypothesis testing, and the guideline on appropriate sample sizes for each estimator is provided for preserving type I error in general cases based on numerical results. Moreover, we develop a user friendly R package "geesmv" incorporating all of these variance estimators for public usage in practice.
Statistics in medicine
Ming Wang Lan Kong Zheng Li Lijun Zhang