Working‐correlation‐structure identification in generalized estimating equations

Selecting an appropriate working correlation structure is pertinent to clustered data analysis using generalized estimating equations (GEE) because an inappropriate choice will lead to inefficient parameter estimation. We investigate the well‐known criterion of QIC for selecting a working correlation structure, and have found that performance of the QIC is deteriorated by a term that is theoretically independent of the correlation structures but has to be estimated with an error. This leads us to propose a correlation information criterion (CIC) that substantially improves the QIC performance. Extensive simulation studies indicate that the CIC has remarkable improvement in selecting the correct correlation structures. We also illustrate our findings using a data set from the Madras Longitudinal Schizophrenia Study. Copyright © 2008 John Wiley & Sons, Ltd.     

增龄过程中心脏径限的变化及其影响因素

为探讨年龄增长及伴随的一些生理性变化对心脏径限的影响,对300余例20—81岁(平均46±16岁)的正常成人进行了超声心动图研究。多元逐步回归及协方差分析表明,左室壁厚度随年龄俱增,而左室舒张未期内径变化不大;室间隔/左室后壁厚度比值亦渐有增长,反映了室间隔的随龄增长稍快于左室后壁,故在增龄过程中,左室流出道稍有缩小;左房、主动脉根部和右室内径均有随年龄而增长的变化。二尖瓣舒张期 EF 斜率随年龄增长渐有下降,可能反映了左室顺应性的降低及二尖瓣弹性的减退。此外,一些心脏测值有性别差异,不少径限尚与体格大小或血压水平有相关关系。对于不同年龄组间心脏径限差异的原因,以及心脏径限彼此间的关系进行了分析和讨论,认为在临床上评价心脏径限正常与否和异常程度时,应考虑年龄、性别、体格大小、血压等因素的影响。     

Simultaneous genetic analysis of longitudinal means and covariance structure in the simplex model using twin data

A longitudinal model based on the simplex model is presented to analyze simultaneously means and covariance structure using univariate longitudinal twin data. The objective of the model is to decompose the mean trend into components which can be attributed to those genetic and environmental factors which give rise to phenotypic individual differences and a component of unknown constitution which does not involve individual differences. Illustrations are given using simulated data and repeatedly measured weight obtained in a sample of 82 female twin pairs on six occasions.     

Mixed General Linear Model Analysis of Quantitative Electroencephalographic (qEEG) Data

ABSTRACT

This paper describes a mixed general linear analysis of the quantitative electroencephalogram (qEEG). The modeling is similar to regression, which builds a regression or ‘best-fit’ model for the data structure but, in addition, provides for correlations between observations. A mixed linear model states that data consists of two parts: fixed effects and random effects. Fixed effects determine the expected values of the observations, while random effects account for the stochastic deviations from these expected values both between and within individuals. Since errors are independent between subjects, the deviations from the expected values may also be modeled using a repeated measures approach. The term ‘repeated measures’ in this model refers to data with multiple observations from one specific source. It is reasonable to assume that these observations from the same source are correlated, even if only slightly, in some measurable way. Consequently, statistical analysis of repeated measures data gives a more accurate prediction capability when the issue of covariation between these measures is addressed.

With mixed model methodology now available (e.g., the mixed procedure [Mixed PROC] of the SAS® system), the covariance structure can be incorporated into the statistical model. Disregarding potential random effects not specific to single individuals and absorbing potential within-subject random effects into the covariance matrix allows one to work with a simplified model.

The use of a mixed procedure and its method of modeling the data structure appear to provide an accurate and objective method of analysis resulting in quantifiable equations for testing predictions. Essentially, this method allows the physiological pattern of each individual in the study, not related to any other variable, to be represented and accounted for in the model. Several comparative examples will be used to highlight the information that can be hidden in data structures depending on the type of statistical analysis used.     

Periods of rest in fMRI contain individual spontaneous events which are related to slowly fluctuating spontaneous activity

fMRI studies of brain activity at rest study slow (<0.1 Hz) intrinsic fluctuations in the blood‐oxygenation‐level‐dependent (BOLD) signal that are observed in a temporal scale of several minutes. The origin of these fluctuations is not clear but has previously been associated with slow changes in rhythmic neuronal activity resulting from changes in cortical excitability or neuronal synchronization. In this work, we show that individual spontaneous BOLD events occur during rest, in addition to slow fluctuations. Individual spontaneous BOLD events were identified by deconvolving the hemodynamic impulse response function for each time point in the fMRI time series, thus requiring no information on timing or a‐priori spatial information of events. The patterns of activation detected were related to the motor, visual, default‐mode, and dorsal attention networks. The correspondence between spontaneous events and slow fluctuations in these networks was assessed using a sliding window, seed‐correlation analysis, where seed regions were selected based on the individual spontaneous event BOLD activity maps. We showed that the correlation varied considerably over time, peaking at the time of spontaneous events in these networks. By regressing spontaneous events out of the fMRI signal, we showed that both the correlation strength and the power in spectral frequencies <0.1 Hz decreased, indicating that spontaneous activation events contribute to low‐frequency fluctuations observed in resting state networks with fMRI. This work provides new insights into the origin of signals detected in fMRI studies of functional connectivity. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.     

Surgical myectomy versus alcohol septal ablation for obstructive hypertrophic cardiomyopathy: A propensity score–matched cohort

Objectives

In patients with hypertrophic cardiomyopathy, obstruction of the left ventricular outflow tract can be relieved by surgical septal myectomy or alcohol septal ablation, but uncertainty remains regarding long-term results and comparative effectiveness of alcohol septal ablation. This study aims to compare short- and long-term outcomes of the 2 procedures.

Decreased centrality of cortical volume covariance networks in autism spectrum disorders

Autism spectrum disorders (ASD) are a group of neurodevelopmental conditions characterized by atypical structural and functional brain connectivity. Complex network analysis has been mainly used to describe altered network-level organization for functional systems and white matter tracts in ASD. However, atypical functional and structural connectivity are likely to be also linked to abnormal development of the correlated structure of cortical gray matter. Such covariations of gray matter are particularly well suited to the investigation of the complex cortical pathology of ASD, which is not confined to isolated brain regions but instead acts at the systems level. In this study, we examined network centrality properties of gray matter networks in adults with ASD (n = 84) and neurotypical controls (n = 84) using graph theoretical analysis. We derived a structural covariance network for each group using interregional correlation matrices of cortical volumes extracted from a surface-based parcellation scheme containing 68 cortical regions. Differences between groups in closeness network centrality measures were evaluated using permutation testing. We identified several brain regions in the medial frontal, parietal and temporo-occipital cortices with reductions in closeness centrality in ASD compared to controls. We also found an association between an increased number of autistic traits and reduced centrality of visual nodes in neurotypicals. Our study shows that ASD are accompanied by atypical organization of structural covariance networks by means of a decreased centrality of regions relevant for social and sensorimotor processing. These findings provide further evidence for the altered network-level connectivity model of ASD.     

Differences in the Impact and Management of Heart Disease Between Older Women and Men

Findings are presented from an exploratory study of differences in the impact and mana ernent of heart disease between men and wornen aged 60 and over (N = 323). The sample consisted of ambulatory, non-institutionalized, cardiac patients who had agreed to participate in a self-management intervention study. Differences between men and women were found regarding sym tom experience, physical functioning, exercise compliance, use oPprescribed drugs, and physicianlclinic visits. Differences in emotional well-being and psychosocial functioning were not found; however, women reported more stress and limitat~on in the area of household management. Implications for future research and practice are discussed.