Browsing Biostatistics by Title
Now showing items 91-110 of 215
-
Handling missing values in risk prediction modeling: a comparative simulation study on parametric and machine learning multiple imputations
Risk prediction is a critical tool in preventive medicine, enabling precision prevention for diseases. Electronic health record (EHR) data offers a rich source for constructing risk models, capturing detailed clinical ... -
Hosmer-Lemeshow goodness-of-fit test: Translations to the Cox Proportional Hazards Model
(2013-04-17)<bold>Background:</bold> The goodness of fit of a statistical model is commonly assessed by describing how well the model fits the observed data. For logistic regression the Hosmer-Lemeshow goodness-of-fit test compares ... -
Hypothesis Testing With High-Dimensional Data
In the past two decades, vast high-dimensional biomedical datasets have become mainstay in various biomedical applications from genomics to neuroscience. These high-dimensional data enable researchers to answer scientific ... -
Image analysis and signal extraction from cDNA microarrays
(2004)The emergence of microarray technology invariably leads to a discussion about data reliability amongst researchers. Many factors impact the accuracy of gene expression data gleaned from microarray experiments. These factors ... -
Impact of Covariate Adaptive Allocation Procedures on Power and Validity in Small-Scale Clinical Studies
Covariate adaptive allocation presents an attractive alternative to conventional randomization schemes, particularly in small scale clinical studies where balance in multiple prognostic factors is desired. While the approach ... -
In pursuit of automated statistical inference under minimal assumptions using machine learning tools
This dissertation consists of three projects aiming for automated statistical inference under minimal assumptions using machine learning tools. In the first project, we developed two sieve-like methods to construct ... -
Inferring Biological Networks from Time-Course Observations Using a Non-linear Vector Autoregressive Model
Cellular functions are increasingly viewed as being regulated through networks of molecules working in parallel. Discovery and characterization of these networks is fundamental to understanding diseases and developing ... -
Inferring, Estimating, and Accounting for Population and Pedigree Structure in Genetic Analyses
Genetic studies of admixed individuals with ancestry derived from multiple previously isolated populations have become more common in recent years. Statistical methodology for analyzing genetic data in the presence of ... -
Interpretable and reliable statistical models for biomedicine
This dissertation presents a collection of statistical tools to analyze modern biomedical datasets, which have been transformed by developments in high-throughput and high- content biology. Due to rapid growth in both scale ... -
Joint modeling of survival and longitudinal data measured with error, with application to assessing immune correlates of protection in vaccine efficacy trials
Assessing immune correlates of protection, the immune responses that reliably predict the vaccine efficacy on the clinical endpoint, has always been an important objective in vaccine efficacy trials. In this dissertation, ... -
Kernel Methods for Data Integration in Microbiome-Omics Studies
The human microbiome plays an important role for maintaining the external and internal environment of human health and is associated with many different health conditions and diseases. Meanwhile, other sources of omics ... -
Latent Class and Latent Profile Analysis in Medical Diagnosis and Prognosis
(2013-11-14)Evaluating test accuracy is an important topic in medical diagnosis and prognosis. Accuracy information is necessary for care-givers to make well-informed decisions; it also helps researchers to select better diagnostic ... -
Latent Continuous Time Markov Chains for Partially-Observed Multistate Disease Processes
(2014-04-30)A disease process refers to a patient's traversal over time through a disease with multiple discrete states. Multistate models are powerful tools used to describe the dynamics of disease processes. Clinical study settings ... -
Leveraging Decision Theory to Address Statistical Challenges and Regression Under Additive Nonignorable Missingness
This dissertation is composed of three mutually exclusive projects, presented in individual chapters. The first chapter summarizes a novel method for regression estimation under nonignorable missingness. The latter two ... -
Local Estimation of Patient Prognosis
Statistical methods that can provide patients and their healthcare providers with individual predictions are needed so that informed medical decisions can be made. Ideally an individual prediction would display the full ... -
A Marginal Structural Cox Model Based Analysis Of The Comparative Effectiveness Of Two Dialysis Therapies
The Institute of Medicine identified comparing the effectiveness of renal replacement therapies as the only kidney-disease related topic among the top 100 initial national priorities for comparative effectiveness research. ... -
Marginalizable mixed effect models for clustered binary, categorical and survival data
In this thesis, I propose new models for clustered data, design estimators of covariate effects, implement model inference algorithms, and show asymptotic properties of my estimators, including consistency and asymptotic ... -
Measurement Error in Microbiome Sequencing Experiments: Statistical and Scientific Considerations
Next-generation sequencing (NGS)methods have become an essential tool in the study of complex microbial communities known as microbiomes. Because their near ubiquity, such communities have been the focus of substantial ... -
Mediation Analysis with Complex Intermediate Causal Structure
My doctoral research is oriented around causal inference, specifically causal mediation anal ysis. Roughly, it can be divided into two parts: (1) understanding and resolving conceptual issues in causal problems, and (2) ...