Copy-number variation (CNV) has been implicated in many complex diseases. It is of great interest to detect and locate such regions through genetic association testings. However, the association testings are complicated by the fact that CNVs usually span multiple markers and thus such markers are correlated to each other. To overcome the difficulty, it is desirable to pool information across the markers. In this thesis, we propose a kernel-based method for aggregation of marker-level tests, in which first we obtain a bunch of p-values through association tests for every marker and then the association test involving CNV is based on the statistic of p-values combinations. In addition, we explore several aspects of its implementation. Since p-values among markers are correlated, it is complicated to obtain the null distribution of test statistics for kernel-base aggregation of marker-level tests. To solve the problem, we develop two proper methods that are both demonstrated to preserve the family-wise error rate of the test procedure. They are permutation based and correlation base approaches. Many implementation aspects of kernel-based method are compared through the empirical power studies in a number of simulations constructed from real data involving a pharmacogenomic study of gemcitabine. In addition, more performance comparisons are shown between permutation-based and correlation-based approach. We also apply those two approaches to the real data. The main contribution of the dissertation is the development of marker-level association testing, a comparable and powerful approach to detect phenotype-associated CNVs. Furthermore, the approach is extended to high dimension setting with high efficiency.

This paper intend to find the proper hypothesis and test statistic for testing existence of bilaterally contamination when there exists nuisance parameter. The test statistic is based on method of moments estimators. Union-Intersection test is used for testing if the distribution of population can be implemented by a bilaterally contaminated normal model with unknown variance. This paper also developed a hierarchical normal mixture model (HNM) and applied it to birth weight data. EM algorithm is employed for parameter estimation and a singular Bayesian information criterion (sBIC) is applied to choose the number components. We also proposed a singular flexible information criterion which in addition involves a data-driven penalty.

Missing data bias results if adjustments are not made accordingly. This thesis addresses this issue by exploring a scenario where data is missing at random depending on a covariate x. Four methods for comparing groups while adjusting for missingness are explored by conducting simulations: independent samples t-test with predicted mean stratification, independent samples t-test with response propensity stratification, independent samples t-test with response propensity weighting, and an analysis of covariance. Results show that independent samples t-test with response propensity weighting and analysis of covariance can appropriately adjust for bias. ANCOVA is the stronger method when the ANCOVA assumptions are met. When the ANCOVA assumptions are not met, a t-test with inverse response propensity score weighting is the superior method.

A statistician's job is to produce statistical models. When these models are precise and unbiased, we can relate them to new data appropriately. However, when data sets have missing values, assumptions to statistical methods are violated and produce biased results. The statistician's objective is to implement methods that produce unbiased and accurate results. Research in missing data is becoming popular as modern methods that produce unbiased and accurate results are emerging, such as MICE in R, a statistical software. Using real data, we compare four common imputation methods, in the MICE package in R, at different levels of missingness. The results were compared in terms of the regression coefficients and adjusted R^2 values using the complete data set. The CART and PMM methods consistently performed better than the OTF and RF methods. The procedures were repeated on a second sample of real data and the same conclusions were drawn.

Dengue fever affects over 390 million people annually worldwide and is of particu- lar concern in Southeast Asia where it is one of the leading causes of hospitalization. Modeling trends in dengue occurrence can provide valuable information to Public Health officials, however many challenges arise depending on the data available. In Thailand, reporting of dengue cases is often delayed by more than 6 weeks, and a small fraction of cases may not be reported until over 11 months after they occurred. This study shows that incorporating data on Google Search trends can improve dis- ease predictions in settings with severely underreported data. We compare penalized regression approaches to seasonal baseline models and illustrate that incorporation of search data can improve prediction error. This builds on previous research show- ing that search data and recent surveillance data together can be used to create accurate forecasts for diseases such as influenza and dengue fever. This work shows that even in settings where timely surveillance data is not available, using search data in real-time can produce more accurate short-term forecasts than a seasonal baseline prediction. However, forecast accuracy degrades the further into the future the forecasts go. The relative accuracy of these forecasts compared to a seasonal average forecast varies depending on location. Overall, these data and models can improve short-term public health situational awareness and should be incorporated into larger real-time forecasting efforts.

Das E-Learning Modul MUVE-STAT (Statistische Grundbegriffe und Grundlagen multivariater Verfahren) ermöglicht Psychologiestudierenden einen anwendungsorientierten und interaktiven Erwerb statistischer Methodenkenntnisse. Die Inhalte umfassen anschauliche Darstellungen statistischer Grundbegriffe bis hin zur Anwendung multivariater Verfahren. MUVE-STAT soll Lehrende und Studierende in unterschiedlichen, insbesondere in interdisziplinären Bachelorstudiengängen unterstützen und eine erfolgreiche Fortsetzung des Studiums im Rahmen eines konsekutiven Masterstudiengangs, wie dem Studiengang „Psychologie: Human Performance in Socio- Technical Systems” (HPSTS) an der TU Dresden, gewährleisten.

Statistical research can be more difficult to plan than other kinds of projects, since the research must adapt as knowledge is gained. This dissertation establishes a formal language and methodology for designing experimental research strategies with limited resources. It is a mathematically rigorous extension of a sequential and adaptive form of statistical research called response surface methodology. It uses sponsor-given information, conditions, and resource constraints to decompose an overall project into individual stages. At each stage, a "parent" decision-maker determines what design of experimentation to do for its stage of research, and adapts to the feedback from that research's potential "children", each of whom deal with a different possible state of knowledge resulting from the experimentation of the "parent". The research of this dissertation extends the real-world rigor of the statistical field of design of experiments to develop an deterministic, adaptive algorithm that produces deterministically generated, reproducible, testable, defendable, adaptive, resource-constrained multi-stage experimental schedules without having to spend physical resource.

Syndromic surveillance is defined generally as the collection and statistical analysis of data which are believed to be leading indicators for the presence of deleterious activities developing within a system. Conceptually, syndromic surveillance can be applied to any discipline in which it is important to know when external influences manifest themselves in a system by forcing it to depart from its baseline. Comparing syndromic surveillance systems have led to mixed results, where models that dominate in one performance metric are often sorely deficient in another. This results in a zero-sum trade off where one performance metric must be afforded greater importance for a decision to be made. This thesis presents a dynamic pooling technique which allows for the combination of competing syndromic surveillance models in such a way that the resulting detection algorithm offers a superior combination of sensitivity and specificity, two of the key model metrics, than any of the models individually. We then apply this methodology to a simulated data set in the context of detecting outbreaks of disease in an animal population. We find that this dynamic pooling methodology is robust in the sense that it is capable of superior overall performance with respect to sensitivity, specificity, and mean time to detection under varying conditions of baseline data behavior, e.g. controlling for the presence or absence of various levels of trend and seasonality, as well as in simulated out-of-sample performance tests.

Inflammation is often a protective reaction against harmful foreign agents. However, in many disease conditions, the mechanisms behind the inflammatory response are poorly understood. Often times, the inflammation causes adverse effects, such as joint pain, abdominal pain, fever, fatigue, and loss of appetite. Thus, many treatments aim to inhibit the inflammatory response in order to control adverse symptoms. Such treatments include TNFα inhibitors. However, a major risk associated with drugs inhibiting tumor necrosis factor alpha (TNFα) is serious infection, including tuberculosis (TB). Anti-TNFα therapy is used to treat patients with Crohn’s disease, for which the risk of tuberculosis may be even more concerning. Recent literature suggests Crohn’s might involve Mycobacterium avium subspecies paratuberculosis (MAP), an intracellular TB-like bacterium. This study seeks to investigate the risk of developing TB in patients with Crohn’s disease treated with TNFα inhibitors. A meta-analysis synthesized existing evidence. Evidence came from published randomized, double-masked, placebo-controlled trials of TNFα inhibitors for treatment of adult Crohn’s disease. Twenty-three trials were identified, including 5,669 patients. The risk of tuberculosis was significantly increased in anti-TNFα treated patients, with a risk difference of 0.028 (95% confidence interval [CI], 0.0011-0.055). The odds ratio was 4.85 (95% CI, 1.02-22.99) when all studies were included and 5.85 (95% CI, 1.13-30.38) when studies reporting zero tuberculosis cases were excluded. The risk of tuberculosis is increased in patients with Crohn’s disease treated with TNFα inhibitors. The medical community should be alerted about this risk and the potential for TNFα inhibitor usage favoring granulomatous infections and worsening the patient condition.

<p>This thesis investigates nonparametric inference under multiple independent samples with various modes of censoring, and also presents results concerning Pitman Closeness under Progressive Type-II right censoring. For the nonparametric inference with multiple independent samples, the case of Type-II right censoring is first considered. Two extensions to this are then discussed: doubly Type-II censoring, and Progressive Type-II right censoring. We consider confidence intervals for quantiles, prediction intervals for order statistics from a future sample, and tolerance intervals for a population proportion. Benefits of using multiple samples over one sample are discussed. For each of these scenarios, we consider simulation as an alternative to exact calculations. In each case we illustrate the results with data from the literature. Furthermore, we consider two problems concerning Pitman Closeness and Progressive Type-II right censoring. We derive simple explicit formulae for the Pitman Closeness probabilities of the order statistics to population quantiles. Various tables are given to illustrate these results. We then use the Pitman Closeness measure as a criterion for determining the optimal censoring scheme for samples drawn from the exponential distribution. A general result is conjectured, and demonstrated in special cases</p> / Doctor of Philosophy (PhD)