The inverse sinh transformation on the Woolf interval is used to calculate the confidence interval for the odds ratio and the relative risk in a 2 x 2 table. According to Robert Newcombe, the new interval should improve the coverage probabilities and shorten the width of the confidence interval for these ratios, but the new interval requires evaluation of coverage properties. In this thesis, we will evaluate the exact coverage properties of this modified interval in extreme cases. Also, we will compare the coverage properties of this new interval to other widely-used adjusted intervals. Through comparisons of exact coverage probabilities and interval widths, we will discover if Newcombe's inverse sinh transformation provides better coverage properties than the adjusted methods.

This paper develops a Bayesian ordinal regression model for the maladaptive subscales of the Inventory for Client and Agency Planning (ICAP). Because the maladaptive behavior section of the ICAP contains ordinal data, current analysis strategies combine all the subscales into three indices, making the data more interval in nature. Regular MANOVA tools are subsequently used to create a regression model for these indices. This paper uses ordinal regression to analyze each original scale separately. The sample consists of applicants for aid from Utah's Division of Services for Persons with Disabilities. Each applicant fills out the Scales of Independent Behavior"”Revised (SIB-R) portion of the ICAP that measures eight different maladaptive behaviors. This project models the frequency and severity of each of these eight problem behaviors with separate ordinal regression models. Gender, ethnicity, primary disability, and mental retardation are used as explanatory variables to calculate the odds ratios for a higher maladaptive behavior score in each model. This type of analysis provides a useful tool to any researcher using the ICAP to measure maladaptive behavior.

The corona virus disease of 2019 (Covid-19) is a deadly viral infection rampaging the world since 2019. Health practitioners have identified co-morbidities as one of the factors contributing to the severity of the disease among patients, with diabetes being one of the leading co-morbid. A systematic search was performed on PubMed, Google Scholar and Science hub databases to obtain articles that have addressed the link between diabetes and severity in Covid-19 infection. A meta-analysis to obtain a pooled effect of the effect of diabetes on the severity of Covid-19 (odd ratio OR) was calculated using R programming language and a funnel plot to check for publication bias was also plotted. Twelve studies with 3,180,125 diabetic patients with confirmed cases of Covid-19 (out of 61,820,553 confirmed Covid-19 participants) were included for the meta-analysis. The obtained pooled effect of diabetes on the severity of Covid-19 infection was (OR=1.47; 95% CI 1.33-1.63). From the meta-analysis results; Age, diabetic complications and drugs for the treatment of diabetes were identified as possible co-factors to the diabetic effect on Covid-19 infection, as diabetes was seen to be significantly related to its severity but not mortality. It is therefore very important for diabetic patients to adhere strictly to every laid down regulation regarding Covid-19. More clinical research on alternative diabetic therapy is needed as this will reduce the negative effect of insulin usage.

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Syftet med följande examensarbete är att undersöka teorier och få fram idéer om hur man kan bygga en bot som spelar poker. Ett viktigt ämne som studeras är artificiell intelligens och hur ett AI kan utvecklas hos en bot som ska ersätta en mänsklig pokerspelare spelandes i ett nätverk. Studien ger en inblick om spelregler för Texas Hold’em och går även in på teori om betydelsefull statistik, sannolikhet och odds. Resultatet av denna undersökning består av framtagna algoritmer som kan användas vid utveckling av en bot som spelar poker på ett bord med tio spelare. / The aim of the following thesis is to explore and develop ideas on how to build a bot that plays poker. An important topic that is studied is artificial intelligence and how an AI is implemented for a bot that replaces a human poker player playing in a network.The study provides insight of the playing rules for Texas Hold’em and theory of meaningful statistics, probability and odds will be used.The results of this study consist of algorithms that can be used in the development of a bot that plays poker on a table with ten players.

No / Medical statistics is a vast and ever-growing field of academic endeavour, with direct application to developing the robustness of the evidence base in all areas of medicine. Although the complexity of available statistical techniques has continued to increase, fuelled by the rapid data processing capabilities of even desktop/laptop computers, medical practitioners can go a long way towards creating, critically evaluating and assimilating this evidence with an understanding of just a few key statistical concepts. While the concepts of statistics and ethics are not common bedfellows, it should be emphasised that a statistically flawed study is also an unethical study.[1] This review will outline some of these key concepts and explain how to interpret the output of some commonly used statistical analyses. Examples will be confined to two-group tests on independent samples, using both a continuous and a dichotomous/binary outcome measure.

A common problem in regression analysis is that some covariates are measured with errors. In this dissertation we present simulation-based approach to estimation in two popular regression models with a categorical response variable and classical measurement errors in covariates. The first model is the regression model with a binary response variable. The second one is the proportional odds regression with an ordinal response variable. In both regression models we consider method of moments estimators for therein unknown parameters that are defined via minimizing respective objective functions. The later functions involve multiple integrals and make obtaining of such estimators unfeasible. To overcome this computational difficulty, we propose Simulation-Based Estimators (SBE). This method does not require parametric assumptions for the distributions of the unobserved covariates and error components. We prove consistency and asymptotic normality of the proposed SBE's under some regularity conditions. We also examine the performance of the SBE's in finite-sample situations through simulation studies and two real data sets: the data set from the AIDS Clinical Trial Group (ACTG175) study for our logistic and probit regression models and one from the Adult Literacy and Life Skills (ALL) Survey for our regression model with the ordinal response variable and mismeasured covariates. / October 2016

The prevalence of respiratory symptoms indicative of asthma in children from Latin America has been largely ignored. As part of the International Study of Asthma and Allergies in Childhood (ISAAC), 17 centers in 9 different Latin American countries participated in the study, and data from 52,549 written questionnaires (WQ) in children aged 13-14 years and from 36,264 WQ in 6-7 year olds are described here. In children aged 13-14 years, the prevalence of asthma ever ranged from 5.5-28%, and the prevalence of wheezing in the last 12 months from 6.6-27%. In children aged 6-7 years, the prevalence of asthma ever ranged from 4.1-26.9%, and the prevalence of wheezing in the last 12 months ranged from 8.6-32.1%. The lower prevalence in centers with higher levels of atmospheric pollution suggests that chronic inhalation of polluted air in children does not contribute to asthma. Furthermore, the high figures for asthma in a region with a high level of gastrointestinal parasite infestation, and a high burden of acute respiratory infections occurring early in life, suggest that these factors, considered as protective in other regions, do not have the same effect in this region. The present study indicates that the prevalence of asthma and related symptoms in Latin America is as high and variable as described in industrialized or developed regions of the world. / Revisión por pares

Escalonamento com controle de overdose (EWOC-PH, escalation with overdose control proporcional hazards) é um método bayesiano com controle de overdose que estima a dose máxima tolerada (MTD, maximum tolerated dose) assumindo que o tempo que um paciente leva para apresentar toxicidade segue o modelo de riscos proporcionais. Neste trabalho analisamos quais são as consequências em adotarmos um método que se baseia no modelo de riscos proporcionais quando o tempo até toxicidade segue o modelo de chances de sobrevivência proporcionais. A fim de buscar responder se teríamos uma superestimativa ou uma subestimativa da MTD foram feitas simulações em que consideramos dados de chances de sobrevivência proporcionais e aplicação do método EWOC-PH para analisarmos a MTD. Como uma extensão do método EWOC-PH, propomos o método EWOC-POS que assume que os tempos seguem o modelo de chances de sobrevivência proporcionais. / Escalation with overdose control proportional hazards is a Bayesian method with overdose control that estimates the maximum tolerated dose (MTD) assuming that the time a patient takes to show toxicity follows the proportional hazards model. In this work, we analyse the consequences of adopting a method based on the proportional hazard model when the time until toxicity follows the proportional survival model. In order to seek to answer if we would have an overestimate or an underestimate of MTD, simulations were performed in which we considered proportional odds survival data and application of the EWOC-PH method. As an extension of the EWOC-PH method, we propose the EWOC-POS method which assumes that time until toxicity follows the proportional odds survival model.

<p>This thesis proposes a simplification of the model for dependent Bernoulli variables presented in Cox and Snell (1989). The simplified model, referred to as the simplified Cox model, is developed for identically distributed and dependent Bernoulli variables.</p><p>Properties of the model are presented, including expressions for the loglikelihood function and the Fisher information. The special case of a bivariate symmetric model is studied in detail. For this particular model, it is found that the number of design points in a locally D-optimal design is determined by the log-odds ratio between the variables. Under mutual independence, both a general expression for the restrictions of the parameters and an analytical expression for locally D-optimal designs are derived.</p><p>Focusing on the bivariate case, score tests and likelihood ratio tests are derived to test for independence. Numerical illustrations of these test statistics are presented in three examples. In connection to testing for independence, an E-optimal design for maximizing the local asymptotic power of the score test is proposed.</p><p>The simplified Cox model is applied to a dental data. Based on the estimates of the model, optimal designs are derived. The analysis shows that these optimal designs yield considerably more precise parameter estimates compared to the original design. The original design is also compared against the E-optimal design with respect to the power of the score test. For most alternative hypotheses the E-optimal design provides a larger power compared to the original design.</p>