Dissertations / Theses: 'Biostatistics'

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Silva, Tiago André dos Santos. "Internship report in biostatistics." Master's thesis, Universidade de Aveiro, 2011. http://hdl.handle.net/10773/6154.

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Mestrado em Biomedicina Farmacêutica
Este relatório descreve a minha experiência de 9 meses enquanto estagiário na Eurotrials, Consultores Científicos, uma Empresa especializada em investigação clínica e consultoria científica. Este estágio desenrolou-se em duas vertentes: formação multidisciplinar e monodisciplinar. A formação multidisciplinar envolveu alguma forma de participação activa em diferentes departamentos desta Empresa, com o objectivo de obter uma perspectiva alargada do processo multidisciplinar inerente ao desenvolvimento clínico de produtos de saúde. A formação monodisciplinar concentrou-se na área de estatística médica, sendo realizada no departamento de Bioestatística da Empresa, com o objectivo de obter conhecimentos práticos de aplicação da estatística à investigação em saúde, implicando também a interiorização de conceitos estatísticos fundamentais. Este estágio permitiu-me compreender de forma mais aprofundada o trabalho multidisciplinar necessário para a realização adequada de um projecto de investigação clínica. Permitiu-me também não só adquirir conhecimentos importantes de análise estatística, mas também compreender, de forma mais clara, o papel da estatística na investigação clínica, como ferramenta essencial no planeamento do estudo, análise e interpretação dos dados obtidos.
This report describes my experience of 9 months as an intern at Eurotrials, Scientific Consultants, a company devoted to clinical research and scientific consulting. This internship developed in two aspects: multidisciplinary and monodosciplinary training. Multidisciplinary training involved active participation in different departments of this Company, with the objective of obtaining a broad perspective on the multidisciplinary process of the clinical development of medical products. Monodisciplinary training was focused in medical statistics, being carried out in the Biostatistics department of the Company. The objective was to obtain practical knowledge for the application of statistics in health sciences. This implied the learning of fundamental statistical concepts. This internship allowed me to understand, in depth, the multidisciplinary work necessary for an adequate performance of a clinical research project. It also allowed me to acquire valuable knowledge in statistical analysis, as well as to clearly understand the role of statistics in clinical research, as an essential tool in study planning, analysis and interpretation of data obtained.

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Puza, Borek Dalibor. "Aspects of Bayesian biostatistics." Thesis, Canberra, ACT : The Australian National University, 1994. http://hdl.handle.net/1885/140911.

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Li, Yin. "Application of logistic regression in biostatistics." Thesis, McGill University, 1993. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=68201.

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The primary objective of this paper is a focused introduction to the logistic regression model and its use in methods for modeling the relationship between a dichotomous outcome variable and a set of covariates. The approach we will take is to develop the model from a regression analysis point of view. Also in this paper, an estimator of the common odds ratio in one-to-one matched case-control studies is proposed. The connection between this estimator and the James-Stein estimating procedure is highlighted through the argument of estimating functions. Comparisons are made between this estimator, the conditional maximum likelihood estimator, and the estimator ignoring the matching.

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SBROLLINI, AGNESE. "Biostatistics of Cardiac Signals: Theory & Applications." Doctoral thesis, Università Politecnica delle Marche, 2019. http://hdl.handle.net/11566/263514.

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L’obiettivo della bioingegneria è lo studio dei fenomeni delle scienze della vita. La statistica è un eccellente strumento per la modellazione, l’analisi, la caratterizzazione e l’interpretazione di questi fenomeni. Scopo di questa tesi di dottorato è quello di combinare le principali tecniche statistiche con l'elaborazione dei segnali cardiaci. L'importanza delle statistiche nella bioingegneria cardiaca può essere compresa attraverso la loro applicazione; quindi, sono state presentate quattro applicazioni reali. La prima applicazione è l'Adaptive Thresholding Identification Algorithm (AThrIA), nato per identificare le onde P elettrocardiografiche. AThrIA è l'esempio perfetto di quanto la preelaborazione statistica possa essere importante nella pratica clinica cardiaca. La seconda applicazione è CTG Analyzer, un'interfaccia che estrae automaticamente le caratteristiche cliniche cardiotocografiche. In tal caso, la statistica diventa lo strumento per valutarne la correttezza delle caratteristiche estratte. La terza applicazione è eCTG, un software per digitalizzare i segnali cardiotocografici. Combinando l’analisi delle distribuzioni e le tecniche di classificazione, eCTG è un importante esempio dell’utilizzo della statistica nell'elaborazione di immagini e segnali. Infine, la quarta applicazione è la creazione di classificatori per l’elettrocardiografia seriale basati su deep learning. Questi nuovi e innovativi classificatori rappresentano un esempio di come la classificazione statistica supporta la diagnosi clinica. In conclusione, questa tesi di dottorato sottolinea l'importanza della statistica nella bioingegneria dei segnali cardiaci. Considerando i risultati e il loro significato clinico, la combinazione di bioingegneria cardiaca e statistica è uno strumento valido per supportare la ricerca scientifica. Legati allo stesso scopo, tali scienze sono in grado di caratterizzare i fenomeni delle scienze della vita, diventando una scienza unica, la biostatistica.
Aim of bioengineering is to investigate phenomena of life sciences. Considering that statistic is an excellent tool for modeling, analyzing, characterizing and interpreting phenomena, aim of this doctoral thesis is to merge the major biostatistical techniques and the bioengineering processing of cardiac signals. The importance of statistics in cardiac bioengineering can be deeply understand through its application; thus, four real applications were presented. The first is the Adaptive Thresholding Identification Algorithm (AThrIA), born to identify/characterize electrocardiographic P waves. AThrIA is the perfect example of how much statistical preprocessing can be important in cardiac clinical practice. The second application is CTG Analyzer, an interface that automatically extracts cardiotocographic clinical features. About CTG Analyzer feature extraction, biostatistics is a fundamental instrument to evaluate its correctness. The third application is eCTG, a software to digitalize cardiotocographic signals from images, using a statistical pixel clustering procedure. Combining distributions analysis and classification, eCTG is an important example of statistics in image/signal processing. Finally, the fourth application is the creation of deep-learning serial ECG classifiers, specific neural networks to detect cardiac emerging pathology. Based on serial electrocardiography, these new and innovative classifiers represent samples of the real importance of classification in supporting clinical diagnosis. In conclusion, this doctoral thesis underlines the importance of statistic in bioengineering of cardiac signals. Considering the results and their clinical meaning, the combination of cardiac bioengineering and statistics is a valid instrument to support the scientific research. Linked by the same aim, they are able to quantitative/qualitative characterize the phenomena of life sciences, becoming a single science, biostatistics.

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Nam, In-Sun. "Contributions to the theory and practice of Biostatistics." Thesis, Queensland University of Technology, 2000. https://eprints.qut.edu.au/105691/1/T%28S%29%20737%20Contributions%20to%20the%20theory%20and%20practice%20of%20biostatistics.pdf.

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Biostatistics is the application of statistical concepts and methods to medicine and the biological sciences. The objectives of this work are twofold: 1. To develop suitable multivariate meta analysis methods which will contribute to the theory of biostatistics and to apply them to a new dataset on passive smoking and health outcomes (asthma and lower respiratory disease) in children, a topic of current interest (NHMRC report). 2. To provide case studies and details of statistical advice given to clinicians at the Prince Charles Hospital with the aim to conduct statistically sound trials and analyses. The first section of this work is devoted to the development of methodology for multivariate meta analysis; First I define meta analysis and discuss the initial motivation for the work. I then explain how the example dataset was constructed. Moreover, the conventional meta analysis methods are described and applied to the example data. Finally I introduce two Bayesian multivariate meta analysis models and these are also applied to the example dataset and the results are compared to the previous ones. The distinctive benefit of the multivariate analysis is that it is possible to evaluate the overall effect of passive smoking to childhood respiratory health as well as its effects on single events. The author's experience as a statistical consultant in a number of areas at the Prince Charles Hospital is explained in the second section. It also summarises common areas of concern arising from this experience and gives advice on general approaches to statistical analyses.

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Barcella, William. "Covariate dependent random measures with applications in biostatistics." Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/10037679/.

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In Bayesian nonparametrics, the specification of suitable (for practical purposes) stochastic processes whose realisations are discrete probability measures plays a crucial role. Recently, real world applications have motivated the extension of these stochastic processes to incorporate covariate information in the realisations with the aim of constructing infinite mixture models having weights and/or component-specific parameters which depend on covariates. This work presents four different modelling strategies motivated by practical problems involving stochastic processes over covariate dependent random measures. After presenting the main concepts in Bayesian nonparametrics and reviewing relevant literature, we develop two Bayesian models which are extensions of augmented response mixture models. In particular, we construct a semi-parametric non-linear regression model for zero-inflated discrete distributions and propose techniques to perform variable selection in cluster-specific regression models. The third contribution presents a generalisation of Dirichlet Process for random probability measures to include covariate information via Beta regression. Properties of this new stochastic process are discussed and two illustrations are presented for dealing with spatially correlated observations and grouped longitudinal data. The last part of the thesis proposes a modelling strategy for time-evolving correlated binary vectors, which relies on latent variables. The distribution of these latent variables is assumed to be a convolution of Gaussian kernels with covariate dependent random probability measures. These four modelling strategies are motivated by datasets that come from medical studies involving lower urinary tract symptoms and acute lymphoblastic leukaemia as well as from publicly available data about primary schools evaluations in London.

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Shi, Jing. "Biostatistics and bioinformatics methods for analysis of pathways and gene expression /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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Karlsson, Andreas. "Estimation and Inference for Quantile Regression of Longitudinal Data : With Applications in Biostatistics." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7186.

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Zhao, Sihai. "Survival Analysis with High-Dimensional c\Covariates, with Applications to Cancer Genomics." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10245.

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Recent technological advances have given cancer researchers the ability to gather vast amounts of genetic and genomic data from individual patients. These offer tantalizing possibilities for, for example, basic cancer biology, tailored therapies, and personalized risk predictions. At the same time, they have also introduced many analytical difficulties that cannot be properly addressed with current statistical procedures, because the number of genomic covariates in these datasets is often larger than the sample size. In this dissertation we study methods for addressing this so-called high-dimensional issue when genomic data are used to analyze time-to-event outcomes, so common to clinical cancer studies. In Chapter 1, we propose a regularization method for sparse estimation for estimating equations. Our method can be used even when the number of covariates exceeds the number of samples, and can be implemented using well-studied algorithms from the non-linear constrained optimization literature. Furthermore, for certain estimating equations and certain regularizers, including the lasso and group lasso, we prove a ﬁnite-sample probability bound on the accuracy of our estimator. However, it is well-known that these types of regularization methods can achieve better performance if a quick and simple procedure is ﬁrst used to reduce the number of covariates. In Chapter 2, we propose and theoretically justify a principled method for reducing dimensionality in the analysis of censored data by selecting only the important covariates. Our procedure involves a tuning parameter that has a simple interpretation as the desired false positive rate of this selection. Similar types of model-based screening methods have also been proposed, but only for a few speciﬁc models. Model-free screening methods have also recently been studied, but can have lower power to detect important covariates. In Chapter 3 we propose a screening procedure that can be used with any model that can be ﬁt using estimating equations, and provide uniﬁed results on its ﬁnite-sample screening performance. We thus generalize many recently proposed model-based and model-free screening procedures. We also propose an iterative version of our method and show that it is closely related to a recently studied boosting method for estimating equations.

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Snavely, Anna Catherine. "Multivariate Data Analysis with Applications to Cancer." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10371.

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Multivariate data is common in a wide range of settings. As data structures become increasingly complex, additional statistical tools are required to perform proper analyses. In this dissertation we develop and evaluate methods for the analysis of multivariate data generated from cancer trials. In the first chapter we consider the analysis of clustered survival data that can arise from multicenter clinical trials. In particular, we review and compare marginal and conditional models numerically through simulations and discuss model selection techniques. A multicenter clinical trial of children with acute lymphoblastic leukemia is used to illustrate the findings. The second and third chapters both address the setting where multiple outcomes are collected when the outcome of interest cannot be measured directly. A head and neck cancer trial in which multiple outcomes were collected to measure dysphagia was the particular motivation for this part of the dissertation. Specifically, in the second chapter we propose a semiparametric latent variable transformation model that incorporates measurable outcomes of mixed types, including censored outcomes. This method extends traditional approaches by allowing the relationship between the measurable outcomes and latent variable to be unspecified, rendering more robust inference. Using this approach we can directly estimate the treatment (or other covariate) effect on the unobserved latent variable, enhancing interpretation. In the third chapter, the basic model from the second chapter is maintained, but additional parametric assumptions are made. This model still has the advantages of allowing for censored measurable outcomes and being able to estimate a treatment effect on the latent variable, but has the added advantage of good performance in a small data set. Together the methods proposed in the second and third chapters provide a comprehensive approach for the analysis of complex multiple outcomes data.

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White, Richard. "Novel Statistical Methods Applied in Clinical Trials and Gut Microbiota." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10587.

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Ethical clinical trials need both societal and personal equipoise. Recently, personal equipoise has been disturbed by the introduction of interim analyses; after an interim analysis has been performed the study administrators have additional information about the treatments, which is withheld from new recruits. For true informed consent, this information should be given to new study recruits to use in making a personal decision about their desired treatment. We present a method (and the rationale behind the method) that provides unbiased estimates of hazard ratios when new recruits are given information from interim analyses and allowed to choose their own treatments. We then developed a novel procedure that allows for the identiﬁcation of longitudinal gut microbiota patterns (corresponding to the gut ecosystem evolving), which are associated with an outcome of interest, while appropriately controlling for the false discovery rate. Finally, using novel statistical models, we investigated the impact of POPs (in particular, non-dioxin-like polychlorinated biphenyl, IUPAC no.: 153; ”PCB153”) on human health through the disruption of natural gut microbiota establishment in infants. We created novel distributed lag two-part models to account for the cumulative exposure of POPs.

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Li, Shuli. "Estimating and Testing Treatment Effects and Covariate by Treatment Interaction Effects in Randomized Clinical Trials with All-or-Nothing Compliance." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10554.

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In this dissertation, we develop and evaluate methods for adjusting for treatment non-compliance in a randomized clinical trial with time-to-event outcome within the proportional hazards framework. Adopting the terminology in Cuzick et al. [2007], we assume the patient population consists of three (possibly) latent groups: the ambivalent group, the insisters and the refusers, and we are interested in analyzing the treatment effect, or the covariate by treatment interaction effect, within the ambivalent group. In Chapter 1, we propose a weighted per-protocol (Wtd PP) approach, and motivated by the pseudo likelihood (PL) considered in Cuzick et al. [2007], we also consider a full likelihood (FL) approach and for both likelihood methods, we propose an EM algorithm for estimation. In Chapter 2, we consider a biomarker study conducted within a clinical trial with non-compliance, where the interest is to estimate the interaction effect between the biomarker and the treatment but it is only feasible to collect the biomarker information from a selected sample of the patients enrolled on the trial. We propose a weighted likelihood (WL) method, a weighted pseudo likelihood (WPL) method and a doubly weighted per-protocol (DWtd PP) method by weighting the corresponding estimating equations in Chapter 1. In Chapter 3, we explore the impact of various assumptions of non-compliance on the performance of the methods considered in Chapter 1 and the commonly used intention-to-treat (ITT), as-treated (AT) and the per-protocol (PP) methods. Results from the first two chapters show that the likelihood methods and the weighted likelihood methods are unbiased, when the underlying model is correctly specified in the likelihood specification, and they are more efficient than the Wtd PP method and the DWtd PP method when the number of risk parameters is moderate. The Wtd PP method and the DWtd PP method are potentially more robust to outcome model misspecifications among the insisters and the refusers. Results from Chapter 3 suggest that when treatment non-compliance is present, careful considerations need to be given to the design and analysis of a clinical trial, and various methods could be considered given the specific setting of the trial.

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Cefalu, Matthew Steven. "Statistical Methods for Effect Estimation in Biomedical Research: Robustness and Efficiency." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10850.

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Practical application of statistics in biomedical research is predicated on the notion that one can readily return valid effect estimates of the health consequences of treatments (exposures) that are being studied. The goal as statisticians should be to provide results that are scientifically useful, to use the available data as efficiently as possible, to avoid unnecessary assumptions, and, if necessary, develop methods that are robust to incorrect assumptions. In this dissertation, I provide methods for effect estimation that meet these goals. I consider three scenarios: (1) clustered binary outcomes; (2) continuous outcomes with a binary treatment; and (3) continuous outcomes with potentially missing continuous exposure. In each of these settings, I discuss the shortfalls of current statistical methods for effect estimation available in the literature and propose new and innovative methods that meet the previously stated goals. The validity of each proposed estimator is theoretically verified using asymptotic arguments, and the finite sample behavior is studied through simulation.

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Sharkey, Brian Joseph. "Statistical Methods for the Assessment of Safety and Efficacy in HIV Clinical Trials." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10903.

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Evaluation of different treatments for HIV should take into account the relative balance of safety and efficacy for each treatment. Often time in HIV clinical trials the primary efficacy outcome measure is time to virologic failure, analyzed in an intention-to-treat manner ignoring the changes from the randomized regimens which occur in a reasonable proportion of study participants, often due to treatment limiting adverse events. Clinically, there is therefore considerable interest in also comparing regimens with respect to the competing outcomes of virologic failure and treatment-limiting adverse events leading to discontinuation of the initial randomized regimen. In Chapter 1, we propose an estimator of the cumulative incidence function in the presence of multiple types of censoring mechanisms. In a controlled clinical trial, it is quite reasonable to assume that censoring can occur for several reasons: some noninformative, others informative. We rely on semi-parametric theory to derive an augmented inverse probability of censoring weighted (AIPCW) estimator of the cumulative incidence function. We apply our method to evaluate the safety and efficacy of two antiHIV regimens in a study conducted by the AIDS Clinical Trial Group, ACTG A5095. In Chapter 2, we provide a detailed example of the use of competing risks methods to an application in which there is similar interest in more than one of the competing risks. Specifically, we are interested in evaluating treatment failure in HIV, where the competing risks of failure are failure due to treatment-limiting adverse events or failure due to virologic failure because of lack or loss of suppression of viral load. In Chapter 3, we develop a framework for analyzing competing risks when there is interest in more than one competing risk. This framework is developed in the context of a randomized clinical trial where the familywise error rate must be controlled. We tailor our approach to the HIV example in Chapter 2. We present several different methods for evaluating composite hypotheses and evaluate their performance under null and alternative hypotheses.

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Braun, Danielle. "Statistical Methods to Adjust for Measurement Error in Risk Prediction Models and Observational Studies." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11273.

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The first part of this dissertation focuses on methods to adjust for measurement error in risk prediction models. In chapter one, we propose a nonparametric adjustment for measurement error in time to event data. Measurement error in time to event data used as a predictor will lead to inaccurate predictions. This arises in the context of self-reported family history, a time to event covariate often measured with error, used in Mendelian risk prediction models. Using validation data, we propose a method to adjust for measurement error in this setting. We estimate the measurement error process using a nonparametric smoothed Kaplan-Meier estimator, and use Monte Carlo integration to implement the adjustment. We apply our method to simulated data in the context of Mendelian risk prediction models and multivariate survival prediction models, and illustrate our method using a data application for Mendelian risk prediction models. Results show our adjusted method corrects for measurement error mainly in two aspects; by improving calibration and total accuracy. In some scenarios discrimination is also improved. In chapter two, we use the methods proposed in chapter one to extend Mendelian risk prediction models to handle misreported family history.

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Lin, Xinyi (Cindy). "Statistical Methods for High-Dimensional Data in Genetic Epidemiology." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11326.

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Recent technological advancements have enabled us to collect an unprecedented amount of genetic epidemiological data. The overarching goal of these genetic epidemiology studies is to uncover the underlying biological mechanisms so that improved strategies for disease prevention and management can be developed. To efficiently analyze and interpret high-dimensional biological data, it is imperative to develop novel statistical methods as conventional statistical methods are generally not applicable or are inefficient. In this dissertation, we introduce three novel, powerful and computationally efficient kernel machine set-based association tests for analyzing high-throughput genetic epidemiological data. In the first chapter, we construct a test for identifying common genetic variants that are predictive of a time-to-event outcome. In the second chapter, we develop a test for identifying gene-environment interactions for common genetic variants. In the third chapter, we propose a test for identifying gene-environment interactions for rare genetic variants.

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Zhang, Yifan. "Bayesian Adaptive Clinical Trials." Thesis, Harvard University, 2014. http://nrs.harvard.edu/urn-3:HUL.InstRepos:13070079.

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Bayesian adaptive designs are emerging as popular approach to develop adaptive clinical trials. In this dissertation, I describe the mathematical steps for computing the theoretical optimal adaptive designs in biomarker-integrated trials and in trials with survival outcomes. Section 1 discusses the optimal design in personalized medicine. The optimal design maximizes the expected trial utility given any pre-specified utility function, though the discussion here focuses on maximizing responses within a given patient horizon. This work provides absolute benchmark for the evaluation of trial designs in targeted therapy with binary treatment outcomes. While treatment efficacy can be measured by a short-term binary outcome in many phase II and phase III trials, patients' progression-free survival time is with significant importance in cancer clinical trials. However, it is often difficult to make a design adaptive to survival outcomes because of the long observation time. In Section 2, an optimal adaptive design is developed so that treatment assignment decision for later patients can be made with complete or partial survival outcomes of early patients. The design also maximizes the expected trial utility given any pre-specified utility function that is of clinical importance. In this section, the focus is on maximizing the expected progression-free survival time. Both Sections1 and 2 include examples of comparing adaptive designs, such as the bayesian adaptive randomization and the play-the-winner rule, in terms of the expected trial utility with respect to the best achievable result. In Section 3, a simulation-based p-value is proposed and can be used to conduct frequentist analysis of Bayesian adaptive clinical trials. The optimal Bayesian design is compared to the equal randomization design in terms of the Type I error and the statistical power. With a fixed trial size and Type I error, the power of the equal randomization design depends on the difference in treatment efficacy, meanwhile the power of the optimal Bayesian design also depends on the size of the patient horizon.

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Tran, Linh Mai. "Comparative Causal Effect Estimation and Robust Variance for Longitudinal Data Structures with Applications to Observational HIV Treatment." Thesis, University of California, Berkeley, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10150887.

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This dissertation discusses the application and comparative performance of double robust estimators for estimating the intervention specific mean outcome in longitudinal settings with time-dependent confounding as well as the corresponding estimator variances. (Abstract shortened by ProQuest.)

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Zhu, Min. "Is Complete Case Analysis Appropriate For Cox Regression with Missing Covariate Data?" Thesis, The University of Arizona, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10817621.

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Purpose: Complete case analysis of survival datasets with missing covariates in Cox proportional hazards model relies heavily on strong and usually unverifiable missing mechanism assumptions such as missing completely at random (MCAR) to produce reasonable parameter estimates. Based on the nature of survival data, missing at random (MAR) for missing covariates can be further decomposed into 1) censoring ignorable missing at random (CIMAR) and 2) failure ignorable missing at random (FIMAR). Unlike MCAR and MAR, there are procedures to assess whether missingness of covariates in survival data are consistent with CIMAR or FIMAR. In my thesis, I investigate the performances of the complete case analysis under various missing mechanisms in Cox model and demonstrate the procedures for checking consistency with CIMAR or FIMAR.

Experimental design: For research involving missing data, simulation studies are especially useful while studying the performance of some estimation (e.g. complete case analysis) as all parameters are pre-specified and known. I simulate survival data with missing covariates under various missing data mechanisms including MCAR, missing at random (MAR), missing not at random (MNAR), CIMAR and FIMAR. I then perform complete case Cox regression on simulated datasets and compare results to determine which missingness mechanisms produce reasonable parameter estimates. Finally, I perform a two-step procedure to check whether covariate missingness is consistent with CIMAR or FIMAR on a real dataset as outlined by Rathouz (2006).

Results: This simulation study illustrates that when covariate missingness is FIMAR but not CIMAR, complete case Cox regression produces reasonable parameter estimates similar to when missingness is MCAR. When covariate missingness is CIMAR, complete case Cox regression produces biased parameter estimates. The two-step procedure suggests covariate missingness in the Stanford heart transplant data is consistent with FIMAR.

Conclusions: Survival data with missing covariates that are FIMAR are appropriate for complete case analysis in Cox models. Survival data with missing covariates that are CIMAR are not appropriate for complete case analysis in Cox models. Under independent censoring, it should be possible for researchers to check the consistency of missing covariates in survival data with FIMAR and CIMAR assumptions. If missingness is consistent with FIMAR, complete case Cox regression should produce reasonable estimates. If missingness is consistent with CIMAR or if the data is inconsistent with both CIMAR and FIMAR, complete case Cox regression may produce biased estimates and researchers should consider sensitivity analyses.

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Hoehn, Jonathan. "Regression/Decision Trees to Predict the Severity of Intervention Needed for COVID-19 Positive Patients Using Baseline Emergency Department Vitals at Presentation." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613745329872462.

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Liu, Yiying. "SEMIPARAMETRIC QUASI-BAYESIAN BOOTSTRAP PROCEDURES FOR DICHOTOMOUS OUTCOMES." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case158697570309321.

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Mccready, Carlyle. "Latent Variable Models for Longitudinal Outcomes from a Parenting Intervention Study." Master's thesis, Faculty of Science, 2019. https://hdl.handle.net/11427/31822.

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This research project analysed data collected with the use of self-reporting questionnaires and observational video scores in order to determine the level of success achieved by the Sinovuyo Caring Families Programme (SCFP). The SCFP aimed to reduce harsh parenting practices and child behavioural problems in high-risk South African families. This research project examined the use of structural equation modelling (SEM) for longitudinal profiles and latent growth mediation modelling. Improved behaviour was observed in terms of reported child behaviour problems and reported harsh parenting with differences between the intervention and control groups directly after the completion of the 3-month intervention program. Improved behaviour was also observed in terms of reported positive parenting with differences between the intervention and control groups directly after the completion of the 3- month intervention program and at the 12-month follow-up occasion. No improvement in observed child behaviour was mediated through reported positive parenting or reported harsh parenting. Furthermore, the intervention program led to improved positive parenting behaviour directly after the 3-month intervention period, however the improved behaviour of the parent did not act as a mediating variable and no improvement in child behaviour was observed as a result.

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Van, Biljon Noëlle. "Longitudinal analysis of Brain Metabolite levels for HIV infected Children from ages five to eleven." Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32370.

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HIV infected (HIV+) children initiate antiretroviral therapy (ART) early in life and remain on it lifelong. However, the long-term impact of ART and HIV on the maturing brain is not well documented and longitudinal neuroimaging studies are rare, especially in developing countries most heavily impacted by HIV/AIDS where access to imaging resources are limited. We have examined HIV related changes in metabolite level trajectories from 5-11 years in three brain regions using Magnetic Resonance Spectroscopy (MRS). We used univariate linear mixed effect models to identify independent profiles of the metabolites measured in each region of the brain. To explore the metabolite trends in a multivariate setting we generated multilevel mixed effects models, and correlated response models. There was an element of confounding introduced through the change of MRI scanner during the follow-up period and we compare different methods to resolve this issue. Consequently, we did observe differences in metabolite profiles from HIV+ children compared to HIV uninfected (HIV-) controls. This suggests that while these children are on ART treatment, there is still some underlying effect on their neurochemistry which sets their development apart from the normal healthy profiles we expect.

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Peng, Jin. "Count Data Models for Injury Data from the National Health Interview Survey (NHIS)." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1365780835.

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FENG, I. JUNG. "Dynamic Adjustment of Stimuli in Real-Time Functional Magnetic Resonance Imaging." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1373025879.

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Won, Sungho. "Improving Genetic Analysis of Case-Control Studies." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1212774902.

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27

Wang, Xuefeng. "Assessing the Effects of Multiple Markers in Human Genetic Association Studies." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1314387746.

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28

Ding, Jie. "Monte Carlo Pedigree Disequilibrium Test with Missing Data and Population Structure." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218475579.

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29

Li, Dongmei. "Resampling-based Multiple Testing with Applications to Microarray Data Analysis." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1243993319.

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30

Yang, Jingyuan. "Likelihood Approaches for Detecting Imprinting and Maternal Effects in Family-Based Association Studies." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1275426657.

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31

Niu, Liang. "STATISTICAL MODELING AND ANALYSIS OF CHROMATIN INTERACTIONS." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1338345967.

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32

Sullivan, Danielle M. "A Hot Deck Imputation Procedure for Multiply Imputing Nonignorable Missing Data: The Proxy Pattern-Mixture Hot Deck." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1387301284.

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33

Hinton, Alice M. "Contributions to Discriminant Analysis of Cross-Sectional and Longitudinal Data with Applications." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1390479004.

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34

Hanook, Sharoon. "Analysis of Removable Interaction." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1413761250.

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35

zhang, lu. "THE PROBABILITY OF SNPS ASSOCIATED WITH A DISEASE." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1413540577.

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HAN, XIAOZHEN. "Evaluating the Correlation Coefficient of Bivariate Failure Times: A Copula-based Approach." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1427993855.

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37

Wang, Qin. "Short Term Trend Analysis of Hospital Admissions Due to Red Blood Cell Disorders: Big Data Perspective." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1428070351.

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38

Zhang, Fangyuan. "Detecting Genomic Imprinting and Maternal Effects in Family-Based Association Studies." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429820748.

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39

Ding, Siyang. "A Prevalence Analysis of Hospital Admissions of Chronic Obstructive Pulmonary Disease in 2012 and 2013." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1481032211809499.

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40

Gao, Tianming. "Bayesian Causal Mediation Analysis with Multiple Mediators." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1512649229134385.

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41

Sui, Yihan Sui. "Analyzing Spatial Longitudinal Incidence Patterns Using Dynamic Multivariate Poisson Models." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1527715776935844.

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42

Liu, Xiaobo. "Optimal Timing to Start Treatment Using Structural Failure Time Models." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1532132805119808.

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43

Shao, Mingyuan. "Multivariate Hierarchical Global Rank Test." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1544627557687073.

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44

Xi, Wenna. "Community Structure in Co-Location Networks." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1566156023255678.

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45

ZHANG, YUYANG. "Heterogeneous Treatment Effect Estimation in Observational Studies using Tree-based methods." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587037857042995.

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46

Dong, Ranran. "Stepped Wedge Cluster Randomized Controlled Trials for Three-Level Data: Design and Evaluation." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1514492341281384.

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47

Galadima, Hadiza I. "Controlling for Confounding when Association is Quantified by Area Under the ROC Curve." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3905.

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In the medical literature, there has been an increased interest in evaluating association between exposure and outcomes using nonrandomized observational studies. However, because assignments to exposure are not done randomly in observational studies, comparisons of outcomes between exposed and non-exposed subjects must account for the effect of confounders. Propensity score methods have been widely used to control for confounding, when estimating exposure effect. Previous studies have shown that conditioning on the propensity score results in biased estimation of odds ratio and hazard ratio. However, there is a lack of research into the performance of propensity score methods for estimating the area under the ROC curve (AUC). In this dissertation, we propose AUC as measure of effect when outcomes are continuous. The AUC is interpreted as the probability that a randomly selected non-exposed subject has a better response than a randomly selected exposed subject. The aim of this research is to examine methods to control for confounding when association between exposure and outcomes is quantified by AUC. We look at the performance of the propensity score, including determining the optimal choice of variables for the propensity score model. Choices include covariates related to exposure group, covariates related to outcome, covariates related to both exposure and outcome, and all measured covariates. Additionally, we compare the propensity score approach to that of the conventional regression approach to adjust for AUC. We conduct a series of simulations to assess the performance of the methodology where the choice of the best estimator depends on bias, relative bias, mean squared error, and coverage of 95% confidence intervals. Furthermore, we examine the impact of model misspecification in conventional regression adjustment for AUC by incorrectly modelling the covariates in the data. These modelling errors include omitting covariates, dichotomizing continuous covariates, modelling quadratic covariates as linear, and excluding interactions terms from the model. Finally, a dataset from the shock research unit at the University of Southern California is used to illustrate the estimation of the adjusted AUC using the proposed approaches.

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48

Dixon, Cheryl Annette. "Power Analysis for the Mixed Linear Model." VCU Scholars Compass, 1996. http://scholarscompass.vcu.edu/etd/4525.

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Power analysis is becoming standard in inference based research proposals and is used to support the proposed design and sample size. The choice of an appropriate power analysis depends on the choice of the research question, measurement procedures, design, and analysis plan. The "best" power analysis, however, will have many features of a sound data analysis. First, it addresses the study hypothesis, and second, it yields a credible answer. Power calculations for standard statistical hypotheses based on normal theory have been defined for t-tests through the univariate and multivariate general linear models. For these statistical methods, the approaches to power calculations have been presented based on the exact or approximate distributions of the test statistics in question. Through the methods proposed by O'Brien and Muller (1993), the noncentrality parameter for the noncentral distribution of the test statistics for the univariate and multivariate general linear models is expressed in terms of its distinct components. This in tum leads to methods for calculating power which are efficient and easy to implement. As more complex research questions are studied, more involved methods have been proposed to analyze data. One such method includes the mixed linear model. This research extends the approach to power calculation used for the general linear model to the mixed linear model. Power calculations for the mixed linear model will be based on the approximate F statistic for testing the mixed model's fixed effects proposed by Helms (1992). The noncentrality parameter of the approximate noncentral F for the mixed model will be written in terms of its distinct components so that a useful and efficient method for calculating power in the mixed model setting will be achieved. In this research, it has been found that the rewriting of the noncentrality parameter varies depending on study design. Thus, the noncentrality parameter for three specific cases of study design are derived.

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49

Gunsolley, John C. "False positive rates encountered in the detection of changes in periodontal attachment level." VCU Scholars Compass, 1987. http://scholarscompass.vcu.edu/etd/4684.

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This thesis demonstrates that the assumption of normality used by Goodson results in the underestimation of the type I error rate of the tolerance method by a factor of 10. This underestimation is due to the positive kurtosis demonstrated in the distribution of replicate differences. Therefore, the assumption of normality does not seem warranted. It is shown here that a resampling technique more accurately estimates the type I error rate. The estimates of false positive rates have important implications in the field of periodontics. When diagnostic decisions are based on single measurements, false positive rates are high. Even when thresholds as high as 3 mm. are used, over 3 out of 10 sites identified as "changed" have not changed. Unfortunately, in the clinical practice of periodontics, single measurements are commonly used. Therefore, clinicians who make treatment decisions based on attachment level measurements, may be treating a large percentage of sites that have not undergone destructive periodontal disease. Clinical periodontists generally regard a loss of attachment of 3 mm. or more as evidence of progressively worsening disease requiring additional therapy. The consequences of treating areas that are erroneously concluded as having progressed have to be compared to the consequences of not treating areas that are progressing. If a clinician treats sites when a change of 3 mm. in attachment level is detected, it is likely that as many as 32% of the sites may not have progressed. However, if the change in attachment level is real and the site is not treated, a significant proportion of the attachment may be lost. Changes of 3 mm. are large compared to the length of the root of the tooth. Weine (1982, p. 208-209), using Black's (1902) description of tooth anatomy, presents average root length of 13 categories of teeth. Average root lengths range from 12 to 16.5 mm. for the 13 categories. If a tooth with a root of 14 mm. (near the middle of the range of average tooth length) has a change in attachment level measurements of 3 mm., the clinician is faced with a dilemma as to whether the site should be treated. The dilemma is increased if prior to the change of 3 mm., the site had already lost 50% of its attachment. In this situation the 3 mm. change represents nearly half of the remaining attachment. For these reasons, better measurement techniques would be beneficial in the clinical practice of periodontics. A controversy exists in the periodontal literature on the ability of single attachment level measurements to find actual change in attachment level. Two recent reports are in general agreement with this study. Imrey (1986) evaluates the ability of single measurements of attachment level to find change in attachment level. He concludes: "If true disease is uncommon and sensitivity to it is not high, these false positives may exceed in number the true positives detected" (p. 521). Ralls and Cohen (1986) reach similar conclusions: "the major issue is that 'bursts' of change can be explained by chance events which arise from measurement error and which occur at low but theoretically expected levels" (p. 751). The results of the present research demonstrate that a large percentage of the perceived change in attachment level is due to measurement error, but not to the degree that Imrey (1986) and Ralls and Cohen (1986) suggest. These researchers attribute almost all the attachment level changes to measurement error. In contrast, Aeppli, D. M., Boen, J. R., and Bandt, C. L. (1984) reach a different conclusion: "using an observed increase of greater than 1 mm. as a diagnostic rule leads to high sensitivity and yet satisfactorily high specificity" (p. 264). All three of the above referenced studies base their conclusions on estimates of sensitivity and specificity. The methods of obtaining estimates of sensitivity and specificity vary between the studies. Aeppli, D. M., Boen, J. R., and Bandt, C. L. base their estimates of specificity and sensitivity on a calibration study involving 34 patients and 3 examiners. Their distribution of differences in replicated measurements is similar to the distribution that Goodson (1986) reports. Irnrey (1986) and Ralls and Cohen ( 1986), instead of using actual data, simulate the distribution of differences by using a normal approximation with standard deviations of 1.125 mm. and 1 mm. respectively. Even though the methods of obtaining data vary, all the reports obtain high values of specificity (Table 6). However, estimates of sensitivity vary both within and among the three studies. Table 6 demonstrates that for similar thresholds the studies obtain a wide range of estimates of sensitivity. Within each study estimates of sensitivity are shown to be highly dependent on the assumed magnitude of actual change and the threshold used to detect the change. As the threshold decreases or the assumed attachment level change increases, sensitivity increases. The possible wide range of estimates that can be obtained within a study is demonstrated by Ralls and Cohen (1986). Their estimates of sensitivity range from .0668 to .9772. As discussed in chapter 1, the broad range of estimates of sensitivity and those estimates' basis on arbitrary assumptions brings to question their value.

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50

Lu, Jiandong. "The Standardized Influence Matrix and Its Applications to Generalized Linear Models." VCU Scholars Compass, 1994. http://scholarscompass.vcu.edu/etd/4941.

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The standardized inﬂuence matrix is a generalization of the standardized inﬂuence function and Cook’s approach to local inﬂuence. It provides a general and uniﬁed approach to judge the suitability of statistical inference based on parametric models. It characterizes the local inﬂuence of data deviations from parametric models on various estimators, including generalized linear models. Its use for both robustness measures and diagnostic procedures has been studied. With global robust estimators, diagnostic statistics are proposed and shown to be useful in detecting inﬂuential points for linear regression and logistic regression models. Robustness of various estimators is compared via. the standardized inﬂuence matrix and a new robust estimator for logistic regression models is presented.

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Dissertations / Theses on the topic 'Biostatistics'

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