On the Behaviour of Marginal and Conditional Akaike Information Criteria in Linear Mixed Models

In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion (AIC) have been used, based either on the marginal or on the conditional distribution. We show that the marginal AIC is no longer an asymptotically unbiased estimator of the Akaike information, and in fact favours smaller models without random effects. For the conditional AIC, we show that ignoring estimation uncertainty in the random effects covariance matrix, as is common practice, induces a bias that leads to the selection of any random effect not predicted to be exactly zero. We derive an analytic representation of a corrected version of the conditional AIC, which avoids the high computational cost and imprecision of available numerical approximations. An implementation in an R package is provided. All theoretical results are illustrated in simulation studies, and their impact in practice is investigated in an analysis of childhood malnutrition in Zambia.

Modeling multilevel sleep transitional data via Poisson log-linear multilevel models

This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming piecewise constant hazards. This relationship allows us to synthesize two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed.

RANDOM EFFECTS MODELS IN A META-ANALYSIS OF THE ACCURACY OF DIAGNOSTIC TESTS WITHIN A GOLD STANDARD IN THE PRESENCE OF MISSING DATA

In evaluating the accuracy of diagnosis tests, it is common to apply two imperfect tests jointly or sequentially to a study population. In a recent meta-analysis of the accuracy of microsatellite instability testing (MSI) and traditional mutation analysis (MUT) in predicting germline mutations of the mismatch repair (MMR) genes, a Bayesian approach (Chen, Watson, and Parmigiani 2005) was proposed to handle missing data resulting from partial testing and the lack of a gold standard. In this paper, we demonstrate an improved estimation of the sensitivities and specificities of MSI and MUT by using a nonlinear mixed model and a Bayesian hierarchical model, both of which account for the heterogeneity across studies through study-specific random effects. The methods can be used to estimate the accuracy of two imperfect diagnostic tests in other meta-analyses when the prevalence of disease, the sensitivities and/or the specificities of diagnostic tests are heterogeneous among studies. Furthermore, simulation studies have demonstrated the importance of carefully selecting appropriate random effects on the estimation of diagnostic accuracy measurements in this scenario.

Discrete element methods for asphalt concrete : development and application of user-defined microstructural models and a viscoelastic micromechanical model

As an important Civil Engineering material, asphalt concrete (AC) is commonly used to build road surfaces, airports, and parking lots. With traditional laboratory tests and theoretical equations, it is a challenge to fully understand such a random composite material. Based on the discrete element method (DEM), this research seeks to develop and implement computer models as research approaches for improving understandings of AC microstructure-based mechanics. In this research, three categories of approaches were developed or employed to simulate microstructures of AC materials, namely the randomly-generated models, the idealized models, and image-based models. The image-based models were recommended for accurately predicting AC performance, while the other models were recommended as research tools to obtain deep insight into the AC microstructure-based mechanics. A viscoelastic micromechanical model was developed to capture viscoelastic interactions within the AC microstructure. Four types of constitutive models were built to address the four categories of interactions within an AC specimen. Each of the constitutive models consists of three parts which represent three different interaction behaviors: a stiffness model (force-displace relation), a bonding model (shear and tensile strengths), and a slip model (frictional property). Three techniques were developed to reduce the computational time for AC viscoelastic simulations. It was found that the computational time was significantly reduced to days or hours from years or months for typical three-dimensional models. Dynamic modulus and creep stiffness tests were simulated and methodologies were developed to determine the viscoelastic parameters. It was found that the DE models could successfully predict dynamic modulus, phase angles, and creep stiffness in a wide range of frequencies, temperatures, and time spans. Mineral aggregate morphology characteristics (sphericity, orientation, and angularity) were studied to investigate their impacts on AC creep stiffness. It was found that aggregate characteristics significantly impact creep stiffness. Pavement responses and pavement-vehicle interactions were investigated by simulating pavement sections under a rolling wheel. It was found that wheel acceleration, steadily moving, and deceleration significantly impact contact forces. Additionally, summary and recommendations were provided in the last chapter and part of computer programming codes wree provided in the appendixes.

An empirical comparison of methods for meta-analysis of diagnostic accuracy showed hierarchical models are necessary

OBJECTIVE: Meta-analysis of studies of the accuracy of diagnostic tests currently uses a variety of methods. Statistically rigorous hierarchical models require expertise and sophisticated software. We assessed whether any of the simpler methods can in practice give adequately accurate and reliable results. STUDY DESIGN AND SETTING: We reviewed six methods for meta-analysis of diagnostic accuracy: four simple commonly used methods (simple pooling, separate random-effects meta-analyses of sensitivity and specificity, separate meta-analyses of positive and negative likelihood ratios, and the Littenberg-Moses summary receiver operating characteristic [ROC] curve) and two more statistically rigorous approaches using hierarchical models (bivariate random-effects meta-analysis and hierarchical summary ROC curve analysis). We applied the methods to data from a sample of eight systematic reviews chosen to illustrate a variety of patterns of results. RESULTS: In each meta-analysis, there was substantial heterogeneity between the results of different studies. Simple pooling of results gave misleading summary estimates of sensitivity and specificity in some meta-analyses, and the Littenberg-Moses method produced summary ROC curves that diverged from those produced by more rigorous methods in some situations. CONCLUSION: The closely related hierarchical summary ROC curve or bivariate models should be used as the standard method for meta-analysis of diagnostic accuracy.

Bayesian change-point analysis in linear regression model with scale mixtures of normal distributions

In this thesis, we consider Bayesian inference on the detection of variance change-point models with scale mixtures of normal (for short SMN) distributions. This class of distributions is symmetric and thick-tailed and includes as special cases: Gaussian, Student-t, contaminated normal, and slash distributions. The proposed models provide greater flexibility to analyze a lot of practical data, which often show heavy-tail and may not satisfy the normal assumption. As to the Bayesian analysis, we specify some prior distributions for the unknown parameters in the variance change-point models with the SMN distributions. Due to the complexity of the joint posterior distribution, we propose an efficient Gibbs-type with Metropolis- Hastings sampling algorithm for posterior Bayesian inference. Thereafter, following the idea of [1], we consider the problems of the single and multiple change-point detections. The performance of the proposed procedures is illustrated and analyzed by simulation studies. A real application to the closing price data of U.S. stock market has been analyzed for illustrative purposes.

Neglect-like visual exploration behaviour after theta burst transcranial magnetic stimulation of the right posterior parietal cortex

The right posterior parietal cortex (PPC) is critically involved in visual exploration behaviour, and damage to this area may lead to neglect of the left hemispace. We investigated whether neglect-like visual exploration behaviour could be induced in healthy subjects using theta burst repetitive transcranial magnetic stimulation (rTMS). To this end, one continuous train of theta burst rTMS was applied over the right PPC in 12 healthy subjects prior to a visual exploration task where colour photographs of real-life scenes were presented on a computer screen. In a control experiment, stimulation was also applied over the vertex. Eye movements were measured, and the distribution of visual fixations in the left and right halves of the screen was analysed. In comparison to the performance of 28 control subjects without stimulation, theta burst rTMS over the right PPC, but not the vertex, significantly decreased cumulative fixation duration in the left screen-half and significantly increased cumulative fixation duration in the right screen-half for a time period of 30 min. These results suggest that theta burst rTMS is a reliable method of inducing transient neglect-like visual exploration behaviour.

Compressive strength of interbody cages in the lumbar spine: the effect of cage shape, posterior instrumentation and bone density

One goal of interbody fusion is to increase the height of the degenerated disc space. Interbody cages in particular have been promoted with the claim that they can maintain the disc space better than other methods. There are many factors that can affect the disc height maintenance, including graft or cage design, the quality of the surrounding bone and the presence of supplementary posterior fixation. The present study is an in vitro biomechanical investigation of the compressive behaviour of three different interbody cage designs in a human cadaveric model. The effect of bone density and posterior instrumentation were assessed. Thirty-six lumbar functional spinal units were instrumented with one of three interbody cages: (1) a porous titanium implant with endplate fit (Stratec), (2) a porous, rectangular carbon-fibre implant (Brantigan) and (3) a porous, cylindrical threaded implant (Ray). Posterior instrumentation (USS) was applied to half of the specimens. All specimens were subjected to axial compression displacement until failure. Correlations between both the failure load and the load at 3 mm displacement with the bone density measurements were observed. Neither the cage design nor the presence of posterior instrumentation had a significant effect on the failure load. The loads at 3 mm were slightly less for the Stratec cage, implying lower axial stiffness, but were not different with posterior instrumentation. The large range of observed failure loads overlaps the potential in vivo compressive loads, implying that failure of the bone-implant interface may occur clinically. Preoperative measurements of bone density may be an effective tool to predict settling around interbody cages.

Book Review of “Human Behaviour and the Social Environment: Models, Metaphors, and Maps for Applying Theoretical Perspectives to Practice”. 640 pages, Thomson Brooks/Cole, 2007, by James A. Forte

This voluminous book which draws on almost 1000 references provides an important theoretical base for practice. After an informative introduction about models, maps and metaphors, Forte provides an impressive presentation of several perspectives for use in practice; applied ecological theory, applied system theory, applied biology, applied cognitive science, applied psychodynamic theory, applied behaviourism, applied symbolic interactionism, applied social role theory, applied economic theory, and applied critical theory. Finally he completes his book with a chapter on “Multi theory practice and routes to integration.”

Maxillary sinus floor extension and posterior tooth inclination in adolescent patients with Class II Division 1 malocclusion treated with maxillary first molar extractions

INTRODUCTION Our objective was to investigate potential associations between maxillary sinus floor extension and inclination of maxillary second premolars and second molars in patients with Class II Division 1 malocclusion whose orthodontic treatment included maxillary first molar extractions. METHODS The records of 37 patients (18 boys, 19 girls; mean age, 13.2 years; SD, 1.62 years) treated between 1998 and 2004 by 1 orthodontist with full Begg appliances were used in this study. Inclusion criteria were white patients with Class II Division 1 malocclusion, sagittal overjet of ≥4 mm, treatment plan including extraction of the maxillary first permanent molars, no missing teeth, and no agenesis. Maxillary posterior tooth inclination and lower maxillary sinus area in relation to the palatal plane were measured on lateral cephalograms at 3 time points: at the start and end of treatment, and on average 2.5 years posttreatment. Data were analyzed for the second premolar and second molar inclinations by using mixed linear models. RESULTS The analysis showed that the second molar inclination angle decreased by 7° after orthodontic treatment, compared with pretreatment values, and by 11.5° at the latest follow-up, compared with pretreatment. There was evidence that maxillary sinus volume was negatively correlated with second molar inclination angle; the greater the volume, the smaller the inclination angle. For premolars, inclination increased by 15.4° after orthodontic treatment compared with pretreatment, and by 8.1° at the latest follow-up compared with baseline. The volume of the maxillary sinus was not associated with premolar inclination. CONCLUSIONS We found evidence of an association between maxillary second molar inclination and surface area of the lower sinus in patients treated with maxillary first molar extractions. Clinicians who undertake such an extraction scheme in Class II patients should be aware of this potential association and consider appropriate biomechanics to control root uprighting.

Ontologies and Methods for Interoperability of Engineering Analysis Models (EAMS) in an E-Design Environment

ABSTRACT ONTOLOGIES AND METHODS FOR INTEROPERABILITY OF ENGINEERING ANALYSIS MODELS (EAMS) IN AN E-DESIGN ENVIRONMENT SEPTEMBER 2007 NEELIMA KANURI, B.S., BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCES PILANI INDIA M.S., UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor Ian Grosse Interoperability is the ability of two or more systems to exchange and reuse information efficiently. This thesis presents new techniques for interoperating engineering tools using ontologies as the basis for representing, visualizing, reasoning about, and securely exchanging abstract engineering knowledge between software systems. The specific engineering domain that is the primary focus of this report is the modeling knowledge associated with the development of engineering analysis models (EAMs). This abstract modeling knowledge has been used to support integration of analysis and optimization tools in iSIGHT FD , a commercial engineering environment. ANSYS , a commercial FEA tool, has been wrapped as an analysis service available inside of iSIGHT-FD. Engineering analysis modeling (EAM) ontology has been developed and instantiated to form a knowledge base for representing analysis modeling knowledge. The instances of the knowledge base are the analysis models of real world applications. To illustrate how abstract modeling knowledge can be exploited for useful purposes, a cantilever I-Beam design optimization problem has been used as a test bed proof-of-concept application. Two distinct finite element models of the I-beam are available to analyze a given beam design- a beam-element finite element model with potentially lower accuracy but significantly reduced computational costs and a high fidelity, high cost, shell-element finite element model. The goal is to obtain an optimized I-beam design at minimum computational expense. An intelligent KB tool was developed and implemented in FiPER . This tool reasons about the modeling knowledge to intelligently shift between the beam and the shell element models during an optimization process to select the best analysis model for a given optimization design state. In addition to improved interoperability and design optimization, methods are developed and presented that demonstrate the ability to operate on ontological knowledge bases to perform important engineering tasks. One such method is the automatic technical report generation method which converts the modeling knowledge associated with an analysis model to a flat technical report. The second method is a secure knowledge sharing method which allocates permissions to portions of knowledge to control knowledge access and sharing. Both the methods acting together enable recipient specific fine grain controlled knowledge viewing and sharing in an engineering workflow integration environment, such as iSIGHT-FD. These methods together play a very efficient role in reducing the large scale inefficiencies existing in current product design and development cycles due to poor knowledge sharing and reuse between people and software engineering tools. This work is a significant advance in both understanding and application of integration of knowledge in a distributed engineering design framework.

A BAYESIAN APPROACH TO DOSE-RESPONSE ASSESSMENT AND DRUG-DRUG INTERACTION ANALYSIS: APPLICATION TO IN VITRO STUDIES

The considerable search for synergistic agents in cancer research is motivated by the therapeutic benefits achieved by combining anti-cancer agents. Synergistic agents make it possible to reduce dosage while maintaining or enhancing a desired effect. Other favorable outcomes of synergistic agents include reduction in toxicity and minimizing or delaying drug resistance. Dose-response assessment and drug-drug interaction analysis play an important part in the drug discovery process, however analysis are often poorly done. This dissertation is an effort to notably improve dose-response assessment and drug-drug interaction analysis. The most commonly used method in published analysis is the Median-Effect Principle/Combination Index method (Chou and Talalay, 1984). The Median-Effect Principle/Combination Index method leads to inefficiency by ignoring important sources of variation inherent in dose-response data and discarding data points that do not fit the Median-Effect Principle. Previous work has shown that the conventional method yields a high rate of false positives (Boik, Boik, Newman, 2008; Hennessey, Rosner, Bast, Chen, 2010) and, in some cases, low power to detect synergy. There is a great need for improving the current methodology. We developed a Bayesian framework for dose-response modeling and drug-drug interaction analysis. First, we developed a hierarchical meta-regression dose-response model that accounts for various sources of variation and uncertainty and allows one to incorporate knowledge from prior studies into the current analysis, thus offering a more efficient and reliable inference. Second, in the case that parametric dose-response models do not fit the data, we developed a practical and flexible nonparametric regression method for meta-analysis of independently repeated dose-response experiments. Third, and lastly, we developed a method, based on Loewe additivity that allows one to quantitatively assess interaction between two agents combined at a fixed dose ratio. The proposed method makes a comprehensive and honest account of uncertainty within drug interaction assessment. Extensive simulation studies show that the novel methodology improves the screening process of effective/synergistic agents and reduces the incidence of type I error. We consider an ovarian cancer cell line study that investigates the combined effect of DNA methylation inhibitors and histone deacetylation inhibitors in human ovarian cancer cell lines. The hypothesis is that the combination of DNA methylation inhibitors and histone deacetylation inhibitors will enhance antiproliferative activity in human ovarian cancer cell lines compared to treatment with each inhibitor alone. By applying the proposed Bayesian methodology, in vitro synergy was declared for DNA methylation inhibitor, 5-AZA-2'-deoxycytidine combined with one histone deacetylation inhibitor, suberoylanilide hydroxamic acid or trichostatin A in the cell lines HEY and SKOV3. This suggests potential new epigenetic therapies in cell growth inhibition of ovarian cancer cells.