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.

Modeling crash outcome probabilities at rural intersections: Application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.

Understanding IT value : a multilevel, complex, and adaptive system perspective

One remaining difficulty in the Information Technology (IT) business value evaluation domain is the direct linkage between IT value and the underlying determinants of IT value or surrogates of IT value. This paper proposes a research that examines the interacting effects of the determinants of IT value, and their influences on IT value. The overarching research question is how those determinants interact with each other and affect the IT value at organizational value. To achieve this, this research embraces a multilevel, complex, and adaptive system view, where the IT value emerges from the interacting of underlying determinants. This research is theoretically grounded on three organizational theories – multilevel theory, complex adaptive systems theory, and adaptive structuration theory. By integrating those theoretical paradigms, this research proposes a conceptual model that focuses on the process where IT value is created from interactions of those determinants. To answer the research question, agent-based modeling technique is used in this research to build a computational representation based on the conceptual model. Computational experimentation will be conducted based on the computational representation. Validation procedures will be applied to consolidate the validity of this model. In the end, hypotheses will be tested using computational experimentation data.

Towards a multiple-goal pursuit and multilevel view of continuing information technology usage : a process model integration and a research agenda

The value of information technology (IT) is often realized when continuously being used after users’ initial acceptance. However, previous research on continuing IT usage is limited for dismissing the importance of mental goals in directing users’ behaviors and for inadequately accommodating the group context of users. This in-progress paper offers a synthesis of several literature to conceptualize continuing IT usage as multilevel constructs and to view IT usage behavior as directed and energized by a set of mental goals. Drawing from the self-regulation theory in the social psychology, this paper proposes a process model, positioning continuing IT usage as multiple-goal pursuit. An agent-based modeling approach is suggested to further explore causal and analytical implications of the proposed process model.

The Coxian phase-type distribution as a contribution to the multilevel model of in-hospital mortality

This paper presents multilevel models that utilize the Coxian phase-type distribution in order to be able to include a survival component in the model. The approach is demonstrated by modeling patient length of stay and in-hospital mortality in geriatric wards in Italy. The multilevel model is used to provide a means of controlling for the existence of possible intra-ward correlations, which may make patients within a hospital more alike in terms of experienced outcome than patients coming from different hospitals, everything else being equal. Within this multilevel model we introduce the use of the Coxian phase-type distribution to create a covariate that represents patient length of stay or stage (of hospital care). Results demonstrate that the use of the multilevel model for representing the in-patient mortality is successful and further enhanced by the inclusion of the Coxian phase-type distribution variable (stage covariate).

Modeling, control and simulation of full-power converter wind turbines equipped with permanent magnet synchronous generator

In this paper, two wind turbines equipped with a permanent magnet synchronous generator (PMSG) and respectively with a two-level or a multilevel converter are simulated in order to access the malfunction transient performance. Three different drive train mass models, respectively, one, two and three mass models, are considered in order to model the bending flexibility of the blades. Moreover, a fractional-order control strategy is studied comparatively to a classical integer-order control strategy. Computer simulations are carried out, and conclusions about the total harmonic distortion (THD) of the electric current injected into the electric grid are in favor of the fractional-order control strategy.

Modeling spatial distribution of cancer incidence in Cordoba, Argentina

Background: Cancer is the second leading cause of death in Argentina, and there is little knowledge about its incidence. The first study based on population-based cancer registry described spatial incidence and indicated that there existed at least county-level aggregation. The aim of the present work is to model the incidence patterns for the most incidence cancer in Córdoba Province, Argentina, using information from the Córdoba Cancer Registry by performing multilevel mixed model approach to deal with dependence and unobserved heterogeneity coming from the geo-reference cancer occurrence. Methods: Standardized incidence rates (world standard population) (SIR) by sex based on 5-year age groups were calculated for 109 districts nested on 26 counties for the most incidence cancers in Cordoba using 2004 database. A Poisson twolevel random effect model representing unobserved heterogeneity between first level-districts and second level-counties was fitted to assess the spatial distribution of the overall and site specific cancer incidence rates. Results: SIR cancer at Córdoba province shown an average of 263.53±138.34 and 200.45±98.30 for men and women, respectively. Considering the ratio site specific mean SIR to the total mean, breast cancer ratio was 0.25±0.19, prostate cancer ratio was 0.12±0.10 and lower values for lung and colon cancer for both sexes. The Poisson two-level random intercepts model fitted for SIR data distributed with overdispersion shown significant hierarchical structure for the cancer incidence distribution. Conclusions: a strong spatial-nested effect for the cancer incidence in Córdoba was observed and will help to begin the study of the factors associated with it.

A MULTILEVEL MODEL TO ADDRESS BATCH EFFECTS IN COPY NUMBER ESTIMATION USING SNP ARRAYS

Submicroscopic changes in chromosomal DNA copy number dosage are common and have been implicated in many heritable diseases and cancers. Recent high-throughput technologies have a resolution that permits the detection of segmental changes in DNA copy number that span thousands of basepairs across the genome. Genome-wide association studies (GWAS) may simultaneously screen for copy number-phenotype and SNP-phenotype associations as part of the analytic strategy. However, genome-wide array analyses are particularly susceptible to batch effects as the logistics of preparing DNA and processing thousands of arrays often involves multiple laboratories and technicians, or changes over calendar time to the reagents and laboratory equipment. Failure to adjust for batch effects can lead to incorrect inference and requires inefficient post-hoc quality control procedures that exclude regions that are associated with batch. Our work extends previous model-based approaches for copy number estimation by explicitly modeling batch effects and using shrinkage to improve locus-specific estimates of copy number uncertainty. Key features of this approach include the use of diallelic genotype calls from experimental data to estimate batch- and locus-specific parameters of background and signal without the requirement of training data. We illustrate these ideas using a study of bipolar disease and a study of chromosome 21 trisomy. The former has batch effects that dominate much of the observed variation in quantile-normalized intensities, while the latter illustrates the robustness of our approach to datasets where as many as 25% of the samples have altered copy number. Locus-specific estimates of copy number can be plotted on the copy-number scale to investigate mosaicism and guide the choice of appropriate downstream approaches for smoothing the copy number as a function of physical position. The software is open source and implemented in the R package CRLMM available at Bioconductor (http:www.bioconductor.org).

Multilevel Latent Class Models with Dirichlet Mixing Distribution

Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social sciences and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this paper, we develop multilevel latent class model, in which subpopulation mixing probabilities are treated as random effects that vary among clusters according to a common Dirichlet distribution. We apply the Expectation-Maximization (EM) algorithm for model fitting by maximum likelihood (ML). This approach works well, but is computationally intensive when either the number of classes or the cluster size is large. We propose a maximum pairwise likelihood (MPL) approach via a modified EM algorithm for this case. We also show that a simple latent class analysis, combined with robust standard errors, provides another consistent, robust, but less efficient inferential procedure. Simulation studies suggest that the three methods work well in finite samples, and that the MPL estimates often enjoy comparable precision as the ML estimates. We apply our methods to the analysis of comorbid symptoms in the Obsessive Compulsive Disorder study. Our models' random effects structure has more straightforward interpretation than those of competing methods, thus should usefully augment tools available for latent class analysis of multilevel data.

The enduring effects of psychodynamic treatments vis-à-vis alternative treatments: A multilevel longitudinal meta-analysis

Although evidence suggests that the benefits of psychodynamic treatments are sustained over time, presently it is unclear whether these sustained benefits are superior to non-psychodynamic treatments. Additionally, the extant literature comparing the sustained benefits of psychodynamic treatments compared to alternative treatments is limited with methodological shortcomings. The purpose of the current study was to conduct a rigorous test of the growth of the benefits of psychodynamic treatments relative to alternative treatments across distinct domains of change (i.e., all outcome measures, targeted outcome measures, non-targeted outcome measures, and personality outcome measures). To do so, the study employed strict inclusion criteria to identify randomized clinical trials that directly compared at least one bona fide psychodynamic treatment and one bona fide non-psychodynamic treatment. Hierarchical linear modeling (Raudenbush, Bryk, Cheong, Congdon, & du Toit, 2011) was used to longitudinally model the impact of psychodynamic treatments compared to non-psychodynamic treatments at post-treatment and to compare the growth (i.e., slope) of effects beyond treatment completion. Findings from the present meta-analysis indicated that psychodynamic treatments and non-psychodynamic treatments were equally efficacious at post-treatment and at follow-up for combined outcomes (k=20), targeted outcomes (k=19), non-targeted outcomes (k=17), and personality outcomes (k=6). Clinical implications, directions for future research, and limitations are discussed.

Multilevel analysis for a longitudinal clinical trial

This study applies the multilevel analysis technique to longitudinal data of a large clinical trial. The technique accounts for the correlation at different levels when modeling repeated blood pressure measurements taken throughout the trial. This modeling allows for closer inspection of the remaining correlation and non-homogeneity of variance in the data. Three methods of modeling the correlation were compared. ^

Multilevel analyses in marketing research:differentiating analytical outcomes

Marketing scholars are increasingly recognizing the importance of investigating phenomena at multiple levels. However, the analyses methods that are currently dominant within marketing may not be appropriate to dealing with multilevel or nested data structures. We identify the state of contemporary multilevel marketing research, finding that typical empirical approaches within marketing research may be less effective at explicitly taking account of multilevel data structures than those in other organizational disciplines. A Monte Carlo simulation, based on results from a previously published marketing study, demonstrates that different approaches to analysis of the same data can result in very different results (both in terms of power and effect size). The implication is that marketing scholars should be cautious when analyzing multilevel or other grouped data, and we provide a discussion and introduction to the use of hierarchical linear modeling for this purpose.

Control of second-life hybrid battery energy storage system based on modular boost-multilevel buck converter

To fully utilize second-life batteries on the grid system, a hybrid battery scheme needs to be considered for several reasons: the uncertainty over using a single source supply chain for second-life batteries, the differences in evolving battery chemistry and battery configuration by different suppliers to strive for greater power levels, and the uncertainty of degradation within a second-life battery. Therefore, these hybrid battery systems could have widely different module voltage, capacity, and initial state of charge and state of health. In order to suitably integrate and control these widely different batteries, a suitable multimodular converter topology and an associated control structure are required. This paper addresses these issues proposing a modular boost-multilevel buck converter based topology to integrate these hybrid second-life batteries to a grid-tie inverter. Thereafter, a suitable module-based distributed control architecture is introduced to independently utilize each converter module according to its characteristics. The proposed converter and control architecture are found to be flexible enough to integrate widely different batteries to an inverter dc link. Modeling, analysis, and experimental validation are performed on a single-phase modular hybrid battery energy storage system prototype to understand the operation of the control strategy with different hybrid battery configurations.

Impact of high-performance work systems on individual- and branch-level performance:test of a multilevel model of intermediate linkages

We proposed and tested a multilevel model, underpinned by empowerment theory, that examines the processes linking high-performance work systems (HPWS) and performance outcomes at the individual and organizational levels of analyses. Data were obtained from 37 branches of 2 banking institutions in Ghana. Results of hierarchical regression analysis revealed that branch-level HPWS relates to empowerment climate. Additionally, results of hierarchical linear modeling that examined the hypothesized cross-level relationships revealed 3 salient findings. First, experienced HPWS and empowerment climate partially mediate the influence of branch-level HPWS on psychological empowerment. Second, psychological empowerment partially mediates the influence of empowerment climate and experienced HPWS on service performance. Third, service orientation moderates the psychological empowerment-service performance relationship such that the relationship is stronger for those high rather than low in service orientation. Last, ordinary least squares regression results revealed that branch-level HPWS influences branch-level market performance through cross-level and individual-level influences on service performance that emerges at the branch level as aggregated service performance. © 2011 American Psychological Association.

Design of multilevel amplitude regenerative system

We propose a scheme for multilevel (nine or more) amplitude regeneration based on a nonlinear optical loop mirror (NOLM) and demonstrate through numerical modeling its efficiency and cascadability on circular 16-, 64-, and 256- symbol constellations. We show that the amplitude noise is efficiently suppressed. The design is flexible and enables variation of the number of levels and their positioning. The scheme is compatible with phase regenerators. Also, compared to the traditional single-NOLM configuration scheme, new features, such as reduced and sign-varied power-dependent phase shift, are available. The model is simple to implement, as it requires only two couplers in addition to the traditional NOLM, and offers a vast range of optimization parameters. © 2014 Optical Society of America.