Linear Mixed-effects Approach Research Articles

Decentralized Mixed Effects Modeling in COINSTAC.

Performing group analysis on magnetic resonance imaging (MRI) data with linear mixed-effects (LME) models is challenging due to its large dimensionality and inherent multi-level covariance structure. In addition, as large-scale collaborative projects become commonplace in neuroimaging, data must increasingly be stored and analyzed from different locations. In such settings, substantial overhead can occur in terms of data transfer and coordination between participating research groups. In some cases, data cannot be pooled together due to privacy or regulatory concerns. In this work, we propose a decentralized LME model to perform a large-scale analysis of data from different collaborations without data pooling. This method is efficient as it overcomes the hurdles of data sharing and has lower bandwidth and memory requirements for analysis than the centralized modeling approach. We evaluate our model using features extracted from structural magnetic resonance imaging (sMRI) data. Results highlight gray matter reductions in the temporal lobe/insula and medial frontal regions in schizophrenia, consistent with prior studies. Our analysis also demonstrates that decentralized LME models achieve similar performance compared to the models trained with all the data in one location. We also implement the decentralized LME approach in COINSTAC, an open source, decentralized platform for federating neuroimaging analysis, providing an easy to use tool for dissemination to the neuroimaging community.

Association of Impulsivity With Food, Nutrients, and Fitness in a Longitudinal Birth Cohort Study.

Impulsivity is a psychiatric vulnerability factor strongly associated with substance abuse but also with unhealthy diet. Whether these associations extend to specific nutrients is largely unknown. Therefore, we investigated the longitudinal association between diet, cardiorespiratory fitness, and 2 impulsivity dimensions in a representative sample of south Estonian adolescents and young adults. Impulsivity and dietary intake were measured 3 times in 2 birth cohorts at regular intervals in individuals aged 15 to 33 years. The sample included 2 birth cohorts of the longitudinal Estonian Children Personality Behaviour and Health Study. The analytic sample size consisted of 2883 observations (56.4% females). The primary outcomes were adaptive and maladaptive impulsivity scores measured by an original 24-item Likert-type questionnaire. Impulsivity scores were predicted from the food diaries data converted into nutrient categories. A linear mixed-effects approach was used to model the time dependence between observations. Lower maladaptive impulsivity was associated with higher cardiorespiratory fitness (β = -.07; 95% CI = -0.12; -0.03). Higher maladaptive impulsivity was associated with lower dietary intake of zinc (β = -.10; -0.15; -0.06) and vegetables (β = -.04; -0.07; -0.01) and higher intake of sodium (β = .06; 0.02; 0.10). Vitamin B6 was positively associated with adaptive impulsivity (β = .04; 0.01; 0.07). Additionally, some of the adjusted models showed significant but weak associations with selenium, alcohol, fish, and cereal products. Food choice may affect the neurochemistry and therefore regulate the manifestations of impulsivity. We identified associations between several (micro)nutrients and maladaptive impulsivity.

249-OR: Growth Trajectories of MiTy Kids' Offspring According to Intrauterine Growth Status

Background: In the MiTy trial [pregnant T2D randomized to metformin vs. placebo], infants exposed to metformin in-utero had a lower risk of large-for-gestational-age (LGA) but were at higher risk of small-for-gestational-age (SGA) . To date, there are limited data on the effect of in-utero exposure to metformin on infant/child growth in T2D, according to the intrauterine growth status. The MiTy Kids study followed the offspring of MiTy participants to 24 months of age to examine the effect of metformin exposure in-utero on child growth. Aim: Examine the weight and body mass index (BMI) trajectories of children of women with T2D, according to the intrauterine growth status [appropriate for gestational age (AGA) , LGA, and SGA] and metformin exposure. Methods: The study population included offspring of MiTy trial participants. Height and weight measurements were collected at 3, 6, 12, 18 and 24 months of age. Analysis of data was conducted using a fractional polynomial linear mixed effects approach to compare growth trajectories. Results: Of the 283 children who participated in MiTy Kids (46.3% female) , 194 children were AGA at birth (90 metformin and 1placebo) , 65 were LGA (29 metformin and 36 placebo) , and 24 were SGA (16 metformin and 8 placebo) . In children born AGA, both sexes in the metformin arm had a lower weight gain trajectory than those in the placebo group (p=0.008) . Treatment and sex did not have an effect on the growth trajectories of children born LGA. In SGA children, in both sexes, SGA children in the metformin group had a lower weight gain trajectory (p=<0.001) and BMI trajectory (p=0.042) than children in the placebo group, and achieved a lower weight than the WHO growth standard at 24 months. Conclusion: Metformin exposure in-utero was associated with lower weight gain trajectories and BMI trajectories in children of both sexes born SGA and lower weight gain trajectory in AGA children. Further follow-up will determine subsequent growth in these children. Disclosure J. Sanchez: None. J. Hamilton: Advisory Panel; Novo Nordisk Canada Inc. Research Support; Mead Johnson & Company, LLC. G. Tomlinson: None. E. Asztalos: None. K. Murphy: None. B. Zinman: Advisory Panel; Abbott Diabetes, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Canada Inc., Sanofi K.K. D. Simmons: Research Support; Abbott, Hitachi, Ltd., Novo Nordisk. Speaker's Bureau; Sanofi. Other Relationship; Elsevier. A. Haqq: None. I.G. Fantus: None. A. Armson: None. J.F.R. Barrett: None. L.E. Donovan: Other Relationship; Dexcom, Inc., Inner Analytics, Medtronic, Tandem Diabetes Care, Inc. P. Karanicolas: Research Support; Baxter. Y. Jiang: None. S. Tobin: None. K. Mangoff: None. G. Klein: None. D. Feig: Advisory Panel; Novo Nordisk. Research Support; Apotex. Funding Canadian Institutes of Health Research

Development of Mixed-Effects Individual-Tree Diameter Increment Model for Casuarina Equisetifolia Considering the Effects of Tree-Size Diversity, Tree Density Reduction, and Climate

ABSTRACT Based on the data provided by the 6th (1998), 7th (2003), and 8th (2008) National Forest Inventory (NFI) in Hainan Province, southern China, we developed an individual-tree diameter increment model considering tree-size diversity, tree density reduction, and climate for Casuarina equisetifolia. Since the data was longitudinal and had a nested structure, we used a linear mixed-effects approach to construct the mixed-effects model based on sample plot effects. And we applied the method of 10-fold cross-validation to test the basic model without random effects and final mixed-effects model. The results indicate that natural logarithm of initial DBH (logDBH), sum of basal area of trees larger than objective tree (BAL), soil thickness (ST), Gini coefficient of DBH diversity for residual trees (GCres), and mean coolest month temperature (MCMT) had significant impact on the individual-tree diameter increment for Casuarina equisetifolia. Comparing with basic model, the final mixed-effects model performance was greatly improved. In the model validation, the mixed-effects model also showed a better fitting goodness. The individual-tree diameter increment models of Casuarina equisetifolia developed in this study will provide a good basis for estimating and predicting growth of Casuarina equisetifolia forests over larger areas.

Predicting Alzheimer’s conversion in mild cognitive impairment patients using longitudinal neuroimaging and clinical markers

Patients with mild cognitive impairment (MCI) have a high risk for conversion to Alzheimer's disease (AD). Early diagnose of AD in MCI subjects could help to slow or halt the disease progression. Selecting a set of relevant markers from multimodal data to predict conversion from MCI to probable AD has become a challenging task. The aim of this paper is to quantify the impact of longitudinal predictive models with single- or multisource data for predicting MCI-to-AD conversion and identifying a very small subset of features that are highly predictive of conversion. We developed predictive models of MCI-to-AD progression that combine magnetic resonance imaging (MRI)-based markers (cortical thickness and volume of subcortical structures) with neuropsychological tests. These models were built with longitudinal data and validated using baseline values. By using a linear mixed effects approach, we modeled the longitudinal trajectories of the markers. A set of longitudinal features potentially discriminating between MCI subjects who convert to dementia and those who remain stable over a period of 3 years was obtained. Classifier were trained using the marginal longitudinal trajectory residues from the selected features. Our best models predicted conversion with 77% accuracy at baseline (AUC = 0.855, 84% sensitivity, 70% specificity). As more visits were available, longitudinal predictive models improved their predictions with 84% accuracy (AUC = 0.912, 83% sensitivity, 84% specificity). The proposed approach was developed, trained and evaluated using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with a total of 2491 visits from 610 subjects.

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach.

Here, we developed an individual-tree model of 5-year basal area increments based on a dataset including 21898 Picea asperata trees from 779 sample plots located in Xinjiang Province, northwest China. To prevent high correlations among observations from the same sampling unit, we developed the model using a linear mixed-effects approach with random plot effect to account for stochastic variability. Various tree- and stand-level variables, such as indices for tree size, competition, and site condition, were included as fixed effects to explain the residual variability. In addition, heteroscedasticity and autocorrelation were described by introducing variance functions and autocorrelation structures. The optimal linear mixed-effects model was determined by several fit statistics: Akaike's information criterion, Bayesian information criterion, logarithm likelihood, and a likelihood ratio test. The results indicated that significant variables of individual-tree basal area increment were the inverse transformation of diameter at breast height, the basal area of trees larger than the subject tree, the number of trees per hectare, and elevation. Furthermore, errors in variance structure were most successfully modeled by the exponential function, and the autocorrelation was significantly corrected by first-order autoregressive structure (AR(1)). The performance of the linear mixed-effects model was significantly improved relative to the model using ordinary least squares regression.

Development of a linear mixed-effects individual-tree basal area increment model for masson pine in Hunan Province, South-central China

ABSTRACT An individual-tree basal area increment model was developed for masson pine based on 26276 observations of 13,138 trees in 987 sample plots from the 7th (2004), 8th (2009), and 9th (2014) Chinese National Forest Inventory in Hunan Province, South-central China. The model was built using a linear mixed-effects approach with sample plots included as random effects since the data have a hierarchical stochastic structure and biased estimates of the standard error of parameter estimates could be a consequence of applying ordinary least square (OLS) for regression. In addition, within-plot heteroscedasticity and autocorrelation were also considered. The final mixed-effects model was determined according to the Akaike information criterion (AIC), Bayesian information criterion (BIC), log-likelihood (Loglik), and the likelihoodratio test (LRT). The results revealed that initial diameter (DBH), the sum of the basal area (m2/ha) in trees with DBHs larger than the DBH of the subject tree (BAL), number of trees per hectare (NT), and elevation (EL) had a significant impact on individual-tree basal area increment. The mixed-effects model performed much better than the basic model produced using OLS. Additionally, the variance structure of the model errors was successfully modeled using the power function. However, the autocorrelation structures were not defined because there was no autocorrelation amongst the data. It is believed that the final model will contribute to the scientific management of the masson pine.

Development of a Mixed-Effects Individual-Tree Basal Area Increment Model for Oaks (Quercus spp.) Considering Forest Structural Diversity

In the context of uneven-aged mixed-species forest management, an individual-tree basal area increment model considering forest structural diversity was developed for oaks (Quercus spp.) using data collected from 11,860 observations in 845 sample plots from the 7th (2004), 8th (2009), and 9th (2014) Chinese National Forest Inventory in Hunan Province, south-central China. Since the data was longitudinal and had a nested structure, we used a linear mixed-effects approach to construct the model. We also used the variance function and an autocorrelation structure to describe within-plot heteroscedasticity and autocorrelation. Finally, the optimal mixed-effects model was determined based on the Akaike information criterion (AIC), Bayesian information criterion (BIC), log-likelihood (Loglik) and the likelihood ratio test (LRT). The results indicate that the reciprocal transformation of initial diameter at breast height (1/DBH), relative density index (RD), number of trees per hectare (NT), elevation (EL) and Gini coefficient (GC) had a significant impact on the individual-tree basal area increment. In comparison to the basic model developed using least absolute shrinkage and selection operator (LASSO) regression, the mixed-effects model performance was greatly improved. In addition, we observed that the heteroscedasticity was successfully removed by the exponent function and autocorrelation was significantly corrected by AR(1). Our final model also indicated that forest structural diversity significantly affected tree growth and hence should not be neglected. We hope that our final model will contribute to the scientific management of oak-dominated forests.

Estimation and Validation of Flour Exposure in Bakeries in Alberta, Canada.

In epidemiological studies of work-related ill-health only current exposures can, at best, be measured. Previous exposures may be estimated using contemporaneous hygiene records or published data. This study aimed to create a job exposure matrix for exposure in bakers for use in an ongoing cohort study. A systematic review was conducted of English language publications on exposures in bakeries. All publications that appeared to contain quantitative measures of exposure to flour dust, wheat allergen, or fungal α-amylase were read independently by two investigators and relevant data extracted. A third investigator reviewed these data and publications were retained that reported full-shift exposures to inhalable dust, wheat allergen, or α-amylase, and for which geometric means (GMs) were given or could be estimated. For each study, the number of sampling results contributing to each GM was recorded together with information on task, bakery size, product, filter type, sampling head, the country in which the study was conducted, and the estimated year of sampling. Multivariable models were elaborated for each exposure using a linear mixed effects approach. The predictive capacity of the model for inhaled particles was tested against samples collected in eight Alberta bakeries. The capacity of exposure intensity, estimated from each of the three models, to predict sensitization was tested against skin prick testing (SPT) for bakery allergens in bakers currently employed in Alberta. One thousand three hundred and ninety-seven publications were identified through the systematic search, of which 27 had data used to create one or more of the predictive models. Weighted GMs were used as outcome variables. For inhalable dust, task, bakery size, type of sampling head, and year of sampling contributed to the final model. For wheat allergen and α-amylase, task, bakery size, sampling head, and year of sampling again contributed. Product (bread rather than confectionary or mixed products) was also important in these two latter measures. The model for inhalable dust was used to predict the concentration in 33 samples from Alberta bakeries. Overall, 91% of observed samples had 95% confidence intervals (CIs) overlapping the 95% CIs of the predicted values. A model including the Alberta samples found no effect attributable to Alberta provenance. Using this model for inhalable dust and the models developed solely from the literature for wheat allergen and α-amylase, a positive SPT for bakery allergens in 57 bakers recruited for this study was significantly related to log cumulative exposure for each of the three outcome variables and to log exposure intensity for wheat allergen and α-amylase. The exposure models developed from the literature provide useful estimates of exposure. Calibration of the models against locally collected samples may be useful for countries poorly represented in the modeling dataset.

Abstract P-114: PHARMACOKINETIC ANALYSIS OF UNFRACTIONATED HEPARIN IN CRITICALLY ILL CHILDREN DURING EXTRACORPOREAL MEMBRANE OXYGENATION: DO WE ACHIEVE THE TARGET?

Aims & Objectives: We described the unfractionated heparin dosing regimens and resulting anti-Xa activities in children during the first week of ExtraCorporeal Membrane Oxygenation (ECMO). Methods We included children who were under ECMO for refractory hemodynamic failure related to (i) myocarditis or (ii) septic shock. Anti-Xa activity time-courses were analyzed using a non linear mixed effects approach with Monolix version 2016R. Results Au total of 12 children were included (septic shock, n = 6; myocarditis; n = 6) with a median age of 42 months (0–197), a median weight of 17.7 Kg (4.9–69). Bleeding occurred in 2 children and thrombosis in 4. Initial dosing ranging from 15 IU/kg/h to 215 IU/kg/h. Anti-Xa activity was in the target (0.4 to 0.6 IU/mL) in 96 samples (45 %), < 0,4 IU/mL in 81 samples (38 %) and > 0,6 IU/mL in 37 samples (17 %). All children observed at least one time an anti-Xa activity out of the target. Anti-Xa activity time-courses were well described by a one-compartment open model with first order elimination. Body weight (BW) was the main covariate explaining clearance (CL) and volume of distribution (V) between subject variabilities. Parameter estimates were CLi = CLTYP*(BW/70)3/4; Vi = VTYP*(BW(t)/70)1; where typical clearance (CLTYP) and typical volume of distribution (VTYP) were 2.42 L.h-1 and 5.1 L, respectively. Conclusions Unfractionated heparin standard dosing regimen and empirical adjustments are not appropriate to reach and maintain the target. PK modeling may help the physician to achieve the target range from the initial dose and during the maintenance doses.

Data-Enabled Building Energy Savings (D-E BES)

Building sector energy consumption represents a significant fraction of the overall energy consumption in urban communities. While there has been increasing focus on the development of smart environments to support energy savings in urban commercial buildings, the development of smart environments for the residential sector has been less common. Instead, urban residential energy-saving efforts have focused on improving a building’s physical infrastructure, introducing energy-saving appliances, and motivating residents to adopt energy conservation practices. This paper illustrates that creating an affinity between a building resident’s thermal preferences and a building apartment’s unregulated thermal environment represents an alternative means of generating an energy-efficient environment for multi-family, residential buildings. Two years of 15-min interval summer data, obtained from smart cyber-physical systems installed in 310 apartments across two New York City buildings, is used to develop data-enabled (D-E) models of resident thermal preference and unregulated apartment temperatures. Both of these models use a linear mixed-effects approach. The alignment of optimal resident-apartment pairs is then formulated as an integer-programming problem to explore the building energy saving (BES) potential associated with minimizing the difference between unregulated apartment temperature and the residents’ thermal preference based on summer cooling loads. The work shows that the energy saving potential vary with a building’s physical characteristics as well as the variation in residents’ thermal preferences. The work further demonstrates that both the unregulated temperature and the resident’s preferred temperature in a given apartment are not only affected by outside temperature and external relative humidity, but also strongly affected by the apartment’s geographic orientation. For the specific buildings considered, up to 28% cooling energy saving is possible with full alignment. The paper provides all building monitoring data as an open source data set.

Early Prediction of Alzheimer's Disease Using Null Longitudinal Model-Based Classifiers.

Incipient Alzheimer’s Disease (AD) is characterized by a slow onset of clinical symptoms, with pathological brain changes starting several years earlier. Consequently, it is necessary to first understand and differentiate age-related changes in brain regions in the absence of disease, and then to support early and accurate AD diagnosis. However, there is poor understanding of the initial stage of AD; seemingly healthy elderly brains lose matter in regions related to AD, but similar changes can also be found in non-demented subjects having mild cognitive impairment (MCI). By using a Linear Mixed Effects approach, we modelled the change of 166 Magnetic Resonance Imaging (MRI)-based biomarkers available at a 5-year follow up on healthy elderly control (HC, n = 46) subjects. We hypothesized that, by identifying their significant variant (vr) and quasi-variant (qvr) brain regions over time, it would be possible to obtain an age-based null model, which would characterize their normal atrophy and growth patterns as well as the correlation between these two regions. By using the null model on those subjects who had been clinically diagnosed as HC (n = 161), MCI (n = 209) and AD (n = 331), normal age-related changes were estimated and deviation scores (residuals) from the observed MRI-based biomarkers were computed. Subject classification, as well as the early prediction of conversion to MCI and AD, were addressed through residual-based Support Vector Machines (SVM) modelling. We found reductions in most cortical volumes and thicknesses (with evident gender differences) as well as in sub-cortical regions, including greater atrophy in the hippocampus. The average accuracies (ACC) recorded for men and women were: AD-HC: 94.11%, MCI-HC: 83.77% and MCI converted to AD (cAD)-MCI non-converter (sMCI): 76.72%. Likewise, as compared to standard clinical diagnosis methods, SVM classifiers predicted the conversion of cAD to be 1.9 years earlier for females (ACC:72.5%) and 1.4 years earlier for males (ACC:69.0%).

Untangling the relatedness among correlations, Part II: Inter-subject correlation group analysis through linear mixed-effects modeling

It has been argued that naturalistic conditions in FMRI studies provide a useful paradigm for investigating perception and cognition through a synchronization measure, inter-subject correlation (ISC). However, one analytical stumbling block has been the fact that the ISC values associated with each single subject are not independent, and our previous paper (Chen et al., 2016) used simulations and analyses of real data to show that the methodologies adopted in the literature do not have the proper control for false positives. In the same paper, we proposed nonparametric subject-wise bootstrapping and permutation testing techniques for one and two groups, respectively, which account for the correlation structure, and these greatly outperformed the prior methods in controlling the false positive rate (FPR); that is, subject-wise bootstrapping (SWB) worked relatively well for both cases with one and two groups, and subject-wise permutation (SWP) testing was virtually ideal for group comparisons. Here we seek to explicate and adopt a parametric approach through linear mixed-effects (LME) modeling for studying the ISC values, building on the previous correlation framework, with the benefit that the LME platform offers wider adaptability, more powerful interpretations, and quality control checking capability than nonparametric methods. We describe both theoretical and practical issues involved in the modeling and the manner in which LME with crossed random effects (CRE) modeling is applied. A data-doubling step further allows us to conveniently track the subject index, and achieve easy implementations. We pit the LME approach against the best nonparametric methods, and find that the LME framework achieves proper control for false positives. The new LME methodologies are shown to be both efficient and robust, and they will be publicly available in AFNI (http://afni.nimh.nih.gov).

Everyday Activities: Beneficial Effects of Eudaimonic and Hedonic Motivation on Subjective Well-Being

The study focuses on the impact of time and motives for everyday activities on mood and daily satisfaction. One hundred twenty-one undergraduate students participated in study using experience sampling method. The short PANAS scale, one item momentary satisfaction measure and state orientation to happiness scale were used. The measures were administered five times a day for 7 days via hand-held devices. The data were modeled using a linear mixed-effects approach. The models fitted the data reasonably well, especially for satisfaction and positive affect (PA) sharing similar parameter estimates. Both showed a nonlinear increase through the week while PA showed a similarly shaped circa-diurnal rhythm too. Negative affect (NA) showed almost no rhythm at all. Both hedonic and eudaimonic motives increased satisfaction and PA. Hedonic motives had a negative impact on NA. Generally, on momentary level, hedonic motives have a greater impact while longer intervals seem to emphasize the importance of eudaimonic motives.

The Effect of Combined Glenoid and Humeral Head Defects on Glenohumeral Translation

Objectives:Bone loss in anterior glenohumeral instability has significant clinical implications and is responsible for surgical failure. Previous work has focused on glenoid and humeral head defects separately. There is no prior biomechanical work evaluating the combined effect of these lesions. The purpose of this study is to determine the effect of the combination of humeral head and glenoid bone loss on glenohumeral joint translation in a bipolar bone loss model with a Bankart lesion and after Bankart repair. Our hypothesis is that when both humeral head and glenoid defects occur together, the amount of bone loss required to compromise soft tissue Bankart repair is less than compared to when glenoid and humeral head lesions occur in isolation.Methods:Eight cadaveric shoulders were dissected to expose the intact capsule and glenohumeral joint. The set-up and testing was as described by Itoi ,Sekiya and Yamamoto1,2,3.The humeral shaft and scapula were potted and the shoulder mounted on a custom shoulder testing device permitting 6 degrees of freedom. The test positions were 60 degrees of glenohumeral abduction and 60 degrees of external rotation. A 50N compressive load was applied to the glenohumeral joint. A MTS 858 Servohydraulic test system (MTS Systems, Eden Prairie, MN) was used to translate the humeral head anteriorly 10mm at a rate of 2.0mm/sec. The peak force required to translate the humeral head 10mm was recorded. Three trials were performed in each condition, and the mean value was used for data analysis. All Bankart lesions were made sharply from the 2 o’clock to 6 o’clock position for a right shoulder. Bankart repair was made with transosseous tunnels using high strength suture. A digital micrometer was used to measure and create glenoid defects with a saw parallel to the anterior glenoid. Hill-Sachs lesions were made from 3D models created from a clinical database of computerized tomographic images of 142 patients with shoulder instability (Figure 1). A bell-shape curve was created and the 50th percentile lesion for size and location was selected, 1.47 cm3. Testing was conducted in the following sequence for each specimen:(1) intact (2)posterior capsulotomy, (3)bankart lesion, (4)bankart repair, (5) 2mm glenoid defect with Bankart lesion, (6)bankart repair, (3) Hill Sachs (1.47cm3) lesion, bankart lesion and 2mm defect, (7)bankart repair, (8) 4mm defect with Hill Sachs defect and Bankart lesion,(9)bankart repair, (10)6mm glenoid defect with Hill Sachs, defect and Bankart lesion (11)bankart repair.Results:A linear mixed-effects approach was used to examine force needed to translate for each condition. A statistically significant reduction in force needed to translate was observed with Bankart repair performed with a 1.47 cm3 Hill-Sachs defect and a 2mm (p=0.01) and 4mm (p=0.04) glenoid bone loss compared to Bankart repair with glenoid bone loss alone (Figure 2). A 2mm glenoid defect with the Hill-Sachs defect resulted in a 25% reduction in stability. A 6mm glenoid defect with the Hill-Sachs lesion resulted in a 50% reduction in load to translation compared to a Bankart repair with no bone loss.Conclusion:As little as a 2mm glenoid defect together with a Hill-Sachs lesion of the dimensions observed in 50% of an actual clinical practice will compromise a soft tissue Bankart repair alone. Combined lesions , even when small, may require surgical strategies that address bone defects to optimize outcomes.

Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models

Longitudinal neuroimaging (LNI) studies are rapidly becoming more prevalent and growing in size. Today, no standardized computational tools exist for the analysis of LNI data and widely used methods are sub-optimal for the types of data encountered in real-life studies. Linear Mixed Effects (LME) modeling, a mature approach well known in the statistics community, offers a powerful and versatile framework for analyzing real-life LNI data. This article presents the theory behind LME models, contrasts it with other popular approaches in the context of LNI, and is accompanied with an array of computational tools that will be made freely available through FreeSurfer - a popular Magnetic Resonance Image (MRI) analysis software package. Our core contribution is to provide a quantitative empirical evaluation of the performance of LME and competing alternatives popularly used in prior longitudinal structural MRI studies, namely repeated measures ANOVA and the analysis of annualized longitudinal change measures (e.g. atrophy rate). In our experiments, we analyzed MRI-derived longitudinal hippocampal volume and entorhinal cortex thickness measurements from a public dataset consisting of Alzheimer's patients, subjects with mild cognitive impairment and healthy controls. Our results suggest that the LME approach offers superior statistical power in detecting longitudinal group differences.

InterSCOPE Study: Associations Between Esophageal Squamous Cell Carcinoma and Human Papillomavirus Serological Markers

The role of human papillomavirus (HPV) in the causation of esophageal squamous cell carcinoma is unclear. We examined the associations between esophageal squamous cell carcinoma and 28 centrally measured HPV serological markers in serum from six existing case-control studies conducted in regions with differing background risks of esophageal cancer. We used centralized multiplex serology to test serum samples from 1561 case subjects and 2502 control subjects from six case-control studies for antibodies to the major HPV capsid protein (L1) and/or the early proteins E6 and/or E7 of eight high-risk, two low-risk, and four cutaneous HPV types. Study-specific odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were estimated using conditional logistic regression with adjustment for smoking, alcohol consumption, and other potential confounders. Pooled odds ratios and 95% confidence intervals were calculated using either a linear mixed-effects approach or a joint fixed-effects approach. All statistical tests were two-sided. We found statistically significant associations between esophageal squamous cell carcinoma and antibodies to E6 for HPV16 (OR = 1.89, 95% CI = 1.09 to 3.29, P = .023) and HPV6 (OR = 2.53, 95% CI = 1.51 to 4.25, P < .001) but not for other tested HPV types. There were no statistically significant associations between esophageal squamous cell carcinoma and antibodies to E7 for any of the tested HPV types. Simultaneous seropositivity for HPV16 E6 and E7 was rare (four case subjects, two control subjects; OR = 5.57, 95% CI = 0.90 to 34.35; P = .064). We also found statistically significant associations between esophageal squamous cell carcinoma and capsid antibodies for the high-risk mucosal type HPV33 L1 (OR = 1.30, 95% CI = 1.00 to 1.69; P = .047) and the low-risk mucosal types HPV6 (OR = 1.22, 95% CI = 1.05 to 1.42; P = .010) and HPV11 (OR = 1.30, 95% CI = 1.09 to 1.56, P = .0036). We found limited serological evidence of an association between esophageal squamous cell carcinoma and HPV in the populations studied. Although HPV does not appear to be an important risk factor for esophageal squamous cell carcinoma, we cannot exclude the possibility that certain HPV types may be involved in a small subset of cancers.

How Noisy is Lexical Decision?

Lexical decision is one of the most frequently used tasks in word recognition research. Theoretical conclusions are typically derived from a linear model on the reaction times (RTs) of correct word trials only (e.g., linear regression and ANOVA). Although these models estimate random measurement error for RTs, considering only correct trials implicitly assumes that word/non-word categorizations are without noise: words receive a yes-response because they have been recognized, and they receive a no-response when they are not known. Hence, when participants are presented with the same stimuli on two separate occasions, they are expected to give the same response. We demonstrate that this not true and that responses in a lexical decision task suffer from inconsistency in participants’ response choice, meaning that RTs of “correct” word responses include RTs of trials on which participants did not recognize the stimulus. We obtained estimates of this internal noise using established methods from sensory psychophysics (Burgess and Colborne, 1988). The results show similar noise values as in typical psychophysical signal detection experiments when sensitivity and response bias are taken into account (Neri, 2010). These estimates imply that, with an optimal choice model, only 83–91% of the response choices can be explained (i.e., can be used to derive theoretical conclusions). For word responses, word frequencies below 10 per million yield alarmingly low percentages of consistent responses (near 50%). The same analysis can be applied to RTs, yielding noise estimates about three times higher. Correspondingly, the estimated amount of consistent trial-level variance in RTs is only 8%. These figures are especially relevant given the recent popularity of trial-level lexical decision models using the linear mixed-effects approach (e.g., Baayen et al., 2008).

New Worlds of Social Network Research

to build accurate models of relational data. Carter Butts discusses the conditions under which simple distance is a major predictor of total network structure in large and spatially embedded networks. Peter Hoff sets out a generalized linear mixed effects approach to modeling social networks in which depen dence among ties is attributed to unobserved attributes of vertices, which relate preferen tially to vertices with similar attribute values. Michael Ward, Peter Hoff, and Corey Lofdahl give an extended application of Hoff's mod eling approach to international relations among Central Asian countries with specific attention given to how the analysis can be used to impute missing network linkages. Finally, Nosh Contractor and Peter Monge's chapter reviews theories and theoretical mechanisms that have been proposed to gov em the formation of relations and embeds that review in a multi-theoretical, multi-level mod eling approach focused on networks of orga nizations that compete for similar resources (adversarial networks). It is worth noting that the workshop was held in 2002 and the edited volume of papers published in 2003. Research since then has advanced along many of the fronts discussed in the papers, in particular, the statistical mod eling of networks using exponential random graph models. Nevertheless, the volume remains valuable as an overview of a wide ranging set of research concerns that com poses the field of modern social network analysis. At the same time, the Office of Naval Research got its money's worth: there were papers that specifically addressed questions driven by national security concerns and the final comments by Kathleen Carley set out a clear road map by which social network analysis could better contribute to the solution of national security problems, providing new tools and techniques of analysis and, just as important, clear understanding of their limita tions in field situations.

Linear-mixed effects models for feature selection in high-dimensional NMR spectra

Feature selection in metabolomics can identify important metabolite features that play a significant role in discriminating between various conditions among samples. In this paper, we propose an efficient feature selection method for high-resolution nuclear magnetic resonance (NMR) spectra obtained from time-course experiments. Our proposed approach combines linear-mixed effects (LME) models with a multiple testing procedure based on a false discovery rate. The proposed LME approach is illustrated using NMR spectra with 574 metabolite features obtained for an experiment to examine metabolic changes in response to sulfur amino acid intake. The experimental results showed that classification models constructed with the features selected by the proposed approach resulted in lower rates of misclassification than those models with full features. Furthermore, we compared the LME approach with the two-sample t-test approach that oversimplifies the time-course factor.