Browsing by FAS Department "Biostatistics"
Now showing items 1-20 of 72
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Adjustment for Population Stratification in Sequencing Association Studies and Model Averaged Matching Estimator
(2017-05-11)In chapter 1, we develop a Markov random field-embedded linear mixed model to correct for population stratification induced by a combination of sharp spatial phenotypic distribution and geographically localized rare genetic ... -
An Analysis of Using Pedigrees in Family Based Studies and an Exploration of Cancer Risk and Cancer Resistance UsingTwin Studies
(2017-01-18)In the first section of this thesis, we explore the use of family pedigrees in association analysis. Family pedigrees were successfully used in linkage analysis to discover many genes for Mendelian traits, but less successful ... -
Bayesian Causal Inference for Estimating Impacts of Air Pollution Exposure
(2019-05-17)Estimation of the causal effect of air pollution exposure on population health measures poses unique challenges. One commonly used method for estimating causal effects on such data is propensity score analysis (PSA), which ... -
Bayesian Causal Inference With Intermediates
(2019-05-21)Causal inference from observational data can be complicated for a number of reasons, including complex functional forms for covariates, partially missing or wholly unmeasured confounders, and truncating events which obscure ... -
Bayesian Methods for Multi-Outcome Analysis and a Study of Gender Bias in Medical Articles
(2020-05-13)In Chapter 1, we present Multi-Outcome Regression with Tree-structured Shrinkage (MOReTreeS), a novel framework for Bayesian multi-response regression when the outcomes are related according to a known tree or hierarchy ... -
Bayesian Statistical Framework for High-Dimensional Count Data and its Application in Microbiome Studies
(2017-05-10)High-dimensional count data arising from multinomial sampling is ubiquitous in microbiome studies. This dissertation aims to develop flexible Bayesian framework to model high-dimensional count data, which provides reliable ... -
Biological Insights From Population Differentiation
(2017-05-13)Population genetics studies the genetic variation within and between populations to gain understanding of human history and insight into underlying biological processes. My dissertation introduces three distinct methods: ... -
Breaking the MAR Paradigm: Estimation, Bounding, and Sensitivity When Data Are Missing Not at Random
(2020-05-14)Statistical methods for unobserved, or missing data often rely on an assumption defined over 40 years ago; namely, that the data are missing at random (MAR). Simply put, MAR is when the probability of a missing value is ... -
Causal Inference Methods in Air Pollution Research
(2018-05-08)While the air pollution concentrations in the United States continue to decrease, one important and politically charged question remains: Is long-term exposure to low levels of air pollution still harmful? Several approaches ... -
Computational Methods for the Analysis of Single-Cell Transcriptomic Data and Their Applications to Cancer
(2018-08-17)Single-cell sequencing methods have allowed for a closer view into the heterogeneity of cell populations, down to the level of the individual cell. In particular, single-cell transcriptomic data provides a detailed map ... -
Contributions to Evolutionary Dynamics and Causal Inference
(2018-05-11)In this dissertation, we investigate topics in two different quantitative disciplines, both of which have profound impact in biomedical sciences. The first area is evolutionary dynamical systems to model biological systems; ... -
Contributions to Missing Data Methods in Single-Cell Genomics and Survival Analysis
(2019-05-17)Missing data occurs when individual data values are not recorded for an observation of interest within a sample. Such events may significantly bias subsequent analyses if ignored. This dissertation discusses solutions to ... -
Contributions to Semiparametric Methods for Incomplete Data
(2017-05-10)Abstract Chapter 1: The effect of treatment on the treated (ETT) is a common parameter of interest in causal inference. Identification of ETT typically relies on an assumption of no unobserved confounding. When information ... -
Correcting for Biases Arising in Epidemiologic Research
(2017-09-13)In chapter 1, we explore the performance of naive least squares estimators for latency parameters in linear models in the presence of measurement error. We prove that in many scenarios under a general measurement error ... -
Design and Analysis of Nested Case-Control Studies in the Presence of a Terminal Event
(2018-05-11)Various methods are available for analyzing data in which multiple outcomes, including a terminal event, are of interest. For example, under the semi-competing risks setting, one may fit an illness-death model to cohort ... -
Efficient Assessment of Individualized Disease Risk and Treatment Response via Augmentation
(2017-05-10)T-year survival, defined as the survival status by a pre-specified time point t, is of great interest in many medical research areas. When the t-year survival is the outcome of interest in the individualized medicine, ... -
Estimating Causal Effects in Pragmatic Settings With Imperfect Information
(2018-05-12)Precision medicine seeks to identify the optimal treatment for each individual based on his or her unique features. This invariably involves some form of estimation of causal effects for different patient subgroups to ... -
Functional Data Methods for Environmental Epidemiology and Bayesian Experimental Design for Inferring Causal Structure
(2020-05-12)In this dissertation, we explore topics in two different statistical areas. The first comprises multivariate methods for analyzing functional data; that is, data where the unit of measurement is a curve finely sampled over ... -
Hypothesis Testing and Model Selection for Complex Data
(2017-05-15)In this dissertation, we propose methodology for hypothesis testing in statistical genetics and model selection in networks. In chapters 1 and 2, we introduce new methods to tackle difficulties in hypothesis testing for ... -
Leveraging Functional Annotations and Multiethnic Data to Improve Polygenic Risk Prediction
(2018-09-25)Polygenic risk prediction is a widely-investigated topic because of its potential clinical application as well as its utility to have a better understanding of the genetic architecture of complex traits. Methods to perform ...