Poisson mixture distribution analysis for North Carolina SIDS counts using information criteria

  Mixture distribution analysis provides us with a tool for identifying unlabeled clusters that naturally arise in a data set.  In this paper, we demonstrate how to use the information criteria AIC and BIC to choose the optimal number of clusters for a given set of univariate Poisson data.  We give an empirical comparison between minimum Hellinger distance (MHD) estimation and EM estimation for finding parameters in a mixture of Poisson distributions with artificial data.  In addition, we discuss Bayes error in the context of classification problems with mixture of 2, 3, 4, and 5 Poisson models.  Finally, we provide an example with real data, taken from a study that looked at sudden infant death syndrome (SIDS) count data from 100 North Carolina counties (Symons et al., 1983).  This gives us an opportunity to demonstrate the advantages of the proposed model framework in comparison with the original analysis

Stability Analysis of FitzHugh–Nagumo with Smooth Periodic Forcing

Alan Lloyd Hodgkin and Andrew Huxley received the 1963 Nobel Prize in Physiology for their work describing the propogation of action potentials in the squid giant axon. Major analysis of their system of differential equations was performed by Richard FitzHugh, and later on by Jin-ichi Nagumo who created a tunnel diode circuit based upon FitzHugh\u27s work. The subsequent differential equation model, known as the FitzHugh-Nagumo (FH-N) oscillator, represents a simplification of the Hodgkin-Huxley (H-H) model, but still replicates the original neuronal dynamics. This thesis begins by providing a thorough grounding in the physiology behind the equations. We continue by proving some of the results postulated by Tanya Kostova et al. for FH-N without forcing. Finally, this sets up our own exploration into stimulating the system with smooth periodic forcing. Subsequent quantification of the chaotic phase portraits using a Lyapunov exponent are discussed, as well as the relevance of these results to electrocardiography

Variable selection via penalized regression and the genetic algorithm using information complexity, with applications for high-dimensional -omics data

This dissertation is a collection of examples, algorithms, and techniques for researchers interested in selecting influential variables from statistical regression models. Chapters 1, 2, and 3 provide background information that will be used throughout the remaining chapters, on topics including but not limited to information complexity, model selection, covariance estimation, stepwise variable selection, penalized regression, and especially the genetic algorithm (GA) approach to variable subsetting. In chapter 4, we fully develop the framework for performing GA subset selection in logistic regression models. We present advantages of this approach against stepwise and elastic net regularized regression in selecting variables from a classical set of ICU data. We further compare these results to an entirely new procedure for variable selection developed explicitly for this dissertation, called the post hoc adjustment of measured effects (PHAME). In chapter 5, we reproduce many of the same results from chapter 4 for the first time in a multinomial logistic regression setting. The utility and convenience of the PHAME procedure is demonstrated on a set of cancer genomic data. Chapter 6 marks a departure from supervised learning problems as we shift our focus to unsupervised problems involving mixture distributions of count data from epidemiologic fields. We start off by reintroducing Minimum Hellinger Distance estimation alongside model selection techniques as a worthy alternative to the EM algorithm for generating mixtures of Poisson distributions. We also create for the first time a GA that derives mixtures of negative binomial distributions. The work from chapter 6 is incorporated into chapters 7 and 8, where we conclude the dissertation with a novel analysis of mixtures of count data regression models. We provide algorithms based on single and multi-target genetic algorithms which solve the mixture of penalized count data regression models problem, and demonstrate the usefulness of this technique on HIV count data that were used in a previous study published by Gray, Massaro, et al. (2015) as well as on time-to-event data taken from the cancer genomic data sets from earlier

Stability Analysis of FitzHugh-Nagumo with Smooth Periodic Forcing

Alan Lloyd Hodgkin and Andrew Huxley received the 1963 Nobel Prize in Physiology for their work describing the propagation of action potentials in the squid giant axon. Major analysis of their system of differential equations was performed by Richard FitzHugh, and later by Jin-Ichi Nagumo who created a tunnel diode circuit based upon FitzHugh’s work. The resulting differential model, known as the FitzHugh-Nagumo (FH-N) oscillator, represents a simplification of the Hodgkin-Huxley (H-H) model, but still replicates the original neuronal dynamics (Izhikevich, 2010). We begin by providing a thorough grounding in the physiology behind the equations, then continue by introducing some of the results established by Kostova et al. for FH-N without forcing (Kostova et al., 2004). Finally, this sets up our own exploration into stimulating the system with smooth periodic forcing. Subsequent quantification of the chaotic phase portraits using a Lyapunov exponent are discussed, as well as the relevance of these results to electrocardiography

Antithrombotic Therapy after Acute Coronary Syndrome or PCI in Atrial Fibrillation

Appropriate antithrombotic regimens for patients with atrial fibrillation who have an acute coronary syndrome or have undergone percutaneous coronary intervention (PCI) are unclear.In an international trial with a two-by-two factorial design, we randomly assigned patients with atrial fibrillation who had an acute coronary syndrome or had undergone PCI and were planning to take a P2Y12 inhibitor to receive apixaban or a vitamin K antagonist and to receive aspirin or matching placebo for 6 months. The primary outcome was major or clinically relevant nonmajor bleeding. Secondary outcomes included death or hospitalization and a composite of ischemic events.Enrollment included 4614 patients from 33 countries. There were no significant interactions between the two randomization factors on the primary or secondary outcomes. Major or clinically relevant nonmajor bleeding was noted in 10.5% of the patients receiving apixaban, as compared with 14.7% of those receiving a vitamin K antagonist (hazard ratio, 0.69; 95% confidence interval [CI], 0.58 to 0.81; P<0.001 for both noninferiority and superiority), and in 16.1% of the patients receiving aspirin, as compared with 9.0% of those receiving placebo (hazard ratio, 1.89; 95% CI, 1.59 to 2.24; P<0.001). Patients in the apixaban group had a lower incidence of death or hospitalization than those in the vitamin K antagonist group (23.5% vs. 27.4%; hazard ratio, 0.83; 95% CI, 0.74 to 0.93; P = 0.002) and a similar incidence of ischemic events. Patients in the aspirin group had an incidence of death or hospitalization and of ischemic events that was similar to that in the placebo group.In patients with atrial fibrillation and a recent acute coronary syndrome or PCI treated with a P2Y12 inhibitor, an antithrombotic regimen that included apixaban, without aspirin, resulted in less bleeding and fewer hospitalizations without significant differences in the incidence of ischemic events than regimens that included a vitamin K antagonist, aspirin, or both. (Funded by Bristol-Myers Squibb and Pfizer; AUGUSTUS ClinicalTrials.gov number, NCT02415400.)

Validation of the Hidradenitis Suppurativa Investigator Global Assessment

Importance Few simplified instruments exist for use in hidradenitis suppurativa (HS) trials. Objective To assess psychometric properties of the Hidradenitis Suppurativa Investigator Global Assessment (HS-IGA) score using a clinical trial data set. Design, Setting, and Participants This retrospective analysis of a phase 2 randomized double-blind, placebo-controlled, active-reference arm trial (UCB HS0001) included adults with moderate-to-severe HS. Exposures Trial participants were randomized at baseline to receive bimekizumab, adalimumab, or placebo. Main Outcomes and Measures The HS-IGA score at prespecified time points up to 12 weeks after randomization. Results The HS-IGA score showed strong convergent validity with IHS4 and HS-PhGA scores at baseline (Spearman correlation, 0.86 [P < .001] and 0.74 [P < .001], respectively) and at week 12 (Spearman correlation, 0.73 [P < .001] and 0.64 [P < .001], respectively). The HS-IGA scores assessed during predosing visits at screening and baseline showed good test-retest reliability (intraclass correlation coefficient [ICC] = 0.92). At week 12, HS-IGA responders were significantly associated with HiSCR-(50/75/90) responders (χ2 = 18.45; P < .001; χ2 = 18.11; P < .001; and χ2 = 20.83; P < .001, respectively). The HS-IGA score was predictive of HiSCR-50/75/90 and HS-PhGA response at week 12 (AUC, 0.69, 0.73, 0.85, and 0.71, respectively). However, the HS-IGA as a measure of disease activity showed low predictive validity with patient-reported outcomes at week 12. Conclusions and Relevance The HS-IGA score demonstrated good psychometric properties compared with existing measures and may be considered for use as an end point in clinical trials for HS

Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts

Alzheimer’s disease (AD) is the most common cause of dementia. The mechanism of disease development and progression is not well understood, but increasing evidence suggests multifactorial etiology, with a number of genetic, environmental, and aging-related factors. There is a growing body of evidence that metabolic defects may contribute to this complex disease. To interrogate the relationship between system level metabolites and disease susceptibility and progression, the AD Metabolomics Consortium (ADMC) in partnership with AD Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for patients in the ADNI1 cohort. We used the Biocrates Bile Acids platform to evaluate the association of metabolic levels with disease risk and progression. We detail the quantitative metabolomics data generated on the baseline samples from ADNI1 and ADNIGO/2 (370 cognitively normal, 887 mild cognitive impairment, and 305 AD). Similar to our previous reports on ADNI1, we present the tools for data quality control and initial analysis. This data descriptor represents the third in a series of comprehensive metabolomics datasets from the ADMC on the ADNI

Changes in quality of life, cognition and functional status following catheter ablation of atrial fibrillation

Objective To investigate changes in quality of life (QoL), cognition and functional status according to arrhythmia recurrence after atrial fibrillation (AF) ablation. Methods We compared QoL, cognition and functional status in patients with recurrent atrial tachycardia (AT)/AF versus those without recurrent AT/AF in the AXAFA-AFNET 5 clinical trial. We also sought to identify factors associated with improvement in QoL and functional status following AF ablation by overall change scores with and without analysis of covariance (ANCOVA). Results Among 518 patients who underwent AF ablation, 154 (29.7%) experienced recurrent AT/AF at 3 months. Patients with recurrent AT/AF had higher mean CHA(2)DS(2)-VASc scores (2.8 vs 2.3, p Conclusions Patients without recurrent AT/AF appear to experience greater improvement in functional status but similar QoL as those with recurrent AT/AF after AF ablation

The contribution of visual information to the perception of speech in noise with and without informative temporal fine structure

Understanding what is said in demanding listening situations is assisted greatly by looking at the face of a talker. Previous studies have observed that normal-hearing listeners can benefit from this visual information when a talker’s voice is presented in background noise. These benefits have also been observed in quiet listening conditions in cochlear-implant users, whose device does not convey the informative temporal fine structure cues in speech, and when normal-hearing individuals listen to speech processed to remove these informative temporal fine structure cues. The current study (1) characterised the benefits of visual information when listening in background noise; and (2) used sine-wave vocoding to compare the size of the visual benefit when speech is presented with or without informative temporal fine structure. The accuracy with which normal-hearing individuals reported words in spoken sentences was assessed across three experiments. The availability of visual information and informative temporal fine structure cues was varied within and across the experiments. The results showed that visual benefit was observed using open- and closed-set tests of speech perception. The size of the benefit increased when informative temporal fine structure cues were removed. This finding suggests that visual information may play an important role in the ability of cochlear-implant users to understand speech in many everyday situations. Models of audio-visual integration were able to account for the additional benefit of visual information when speech was degraded and suggested that auditory and visual information was being integrated in a similar way in all conditions. The modelling results were consistent with the notion that audio-visual benefit is derived from the optimal combination of auditory and visual sensory cues

Professional practice models for nursing: A review of the literature and synthesis of key components

This review aimed to synthesise literature describing the development and/or implementation and/or evaluation of a professional practice model to determine the key model components. A professional practice model depicts nursing values and defines the structures and processes that support nurses to control their own practice and to deliver quality care. A review of English language papers published up to August 2014 identified 51 articles that described 38 professional practice models. Articles were subjected to qualitative analysis to identify the concepts common to all professional practice models. Key elements of professional practice models were theoretical foundation and six common components: leadership; nurses' independent and collaborative practice; environment; nurse development and reward; research/innovation; and patient outcomes. A professional practice model provides the foundations for quality nursing practice. This review is an important resource for nurse leaders who seek to advance their organisation in a journey for excellence through the implementation of a professional practice model. This summary of published professional practice models provides a guide for nurse leaders who seek to develop a professional practice model. The essential elements of a professional practice model; theoretical foundation and six common components, are clearly described. These elements can provide the starting point for nurse leaders' discussions with staff to shape a professional practice model that is meaningful to direct care nurses