Biostatistics Commons^™

Causal Inference For The Effect Of Continuous Treatment On Time-To-Event Outcomes And Mediation Analysis On Health Disparities In Observational Studies., Triparna Poddar

Electronic Theses and Dissertations

The dissertation comprises two projects related to causal inference based on observational data. In healthcare research, where abundant observational data such as claims data and electronic records are available, researchers often aim to study the treatment effect and the pathway of that effect. However, estimating treatment effects in observational data presents challenges due to confounding factors. The first project focuses on estimating continuous treatment effects for survival outcomes, while the second concentrates on mediation analysis, allowing the exploration of the pathway of the causal effect. Both projects involve addressing confounding variables. In the first project, I investigate estimation of the …

Inverse Probability Weighting In Survival Analysis And Network Analysis, Yukun Lu

Doctoral Dissertations

Inverse probability weighting is a popular technique to accommodate selection bias due to non-random sampling and missing data. In the first chapter, we develop an inverse probability weighted estimator and an augmented inverse probability weighted estimator of regression coefficients for a linear model with randomly censored covariates, when the censoring mechanism may be dependent on the outcome. We investigate the asymptotic properties of both estimators and evaluate their finite sample performance through extensive simulation studies. We apply the proposed methods to an Alzheimer’s disease study. In the second chapter, we present an application of network analysis in a study of …

Striving For Appropriate Antibiotic Use: A Biomarker Initiative, And Outcomes Associated With Azithromycin Exposure, Amanda Gusovsky

Theses and Dissertations--Pharmacy

The introduction of antibiotics into clinical practice is considered the greatest medical breakthrough of the 20^thcentury. However, the use of antibiotics can contribute to the development of resistance. In the United States (U.S.), approximately 2.8 million people are infected with antibiotic-resistant bacteria each year, and more than 35,000 people die as a result. Moreover, some antibiotics are known to cause cardiac side effects including QT prolongation, hypotension, and ventricular arrythmias. The U.S. Centers for Disease Control and Prevention (CDC) defines appropriate antibiotic use as the effort to use “the right antibiotic, at the right dose, for the right …

Regression Modeling Of Complex Survival Data Based On Pseudo-Observations, Rong Rong

Statistical Science Theses and Dissertations

The restricted mean survival time (RMST) is a clinically meaningful summary measure in studies with survival outcomes. Statistical methods have been developed for regression analysis of RMST to investigate impacts of covariates on RMST, which is a useful alternative to the Cox regression analysis. However, existing methods for regression modeling of RMST are not applicable to left-truncated right-censored data that arise frequently in prevalent cohort studies, for which the sampling bias due to left truncation and informative censoring induced by the prevalent sampling scheme must be properly addressed. Meanwhile, statistical methods have been developed for regression modeling of the cumulative …

Statistical Methods For Personalized Treatment Selection And Survival Data Analysis Based On Observational Data With High-Dimensional Covariates., Don Ramesh Dinendra Sudaraka Tholkage

Electronic Theses and Dissertations

Due to the wide availability of functional data from multiple disciplines, the studies of functional data analysis have become popular in the recent literature. However, the related development in censored survival data has been relatively sparse. In Chapter 2, we consider the problem of analyzing time-to-event data in the presence of functional predictors. We develop a conditional generalized Kaplan Meier (KM) estimator that incorporates functional predictors using kernel weights and rigorously establishes its asymptotic properties. In addition, we propose to select the optimal bandwidth based on a time-dependent Brier score. We then carry out extensive numerical studies to examine the …

Factors Affecting Time To Recovery: A Covid-19 Survival Analysis, Fernanda Montoya

Honors Capstones

This project is focused on the recovery rates of patients diagnosed with COVID-19 after different clinical trial drug treatments. Data for the clinical trial studied was obtained from the National Institute of Allergy and Infectious Diseases for the primary purpose of a survival analysis on patient time to recovery under a placebo and therapeutic drug treatment. Specifically, patients in this clinical trial were randomly selected to receive remdesivir, an antiviral drug, in combination with a placebo or baricitinib, a janus kinase inhibitor drug. Cox PH models were used to identify how the different treatment drugs affect time to recovery and …

Flexible Modelling Of Time-Dependent Covariate Effects With Correlated Competing Risks: Application To Hereditary Breast And Ovarian Cancer Families, Seungwoo Lee

Electronic Thesis and Dissertation Repository

This thesis aims to develop a flexible approach for modelling time-dependent covariate effects on event risk using B-splines in the presence of correlated competing risks. The performance of the proposed model was evaluated via simulation in terms of the bias and precision of the estimation of the parameters and penetrance functions. In addition, we extended the concordance index to account for time-dependent effects and competing events simultaneously and demonstrated its inference procedures. We applied our proposed methods to data rising from the BRCA1 mutation families from the breast cancer family registry to evaluate the time-dependent effects of mammographic screening and …

Approximate Likelihood Based Estimations For Joint Models With Intractable Likelihoods, Karl Stessy M. Bisselou

Theses & Dissertations

This dissertation focuses on the development of approximation approaches for the joint modeling (JM) of repeated measures data and time-to-event data in the presence of analytically or numerically intractable likelihoods. Current likelihood-based inferences for JMs show several limitations including (i) intractability of integrals during marginal likelihood derivations due to the complexity in computations, and (ii) the large number of nuisance parameters (unobserved) posing a problem with convergence. The h-likelihood (HL) and synthetic likelihood (SL) are two computationally efficient estimation approaches that overcome these challenges.

In the presence of extremely high censoring rates, the HL can produce bias parameter estimates. We …

Addressing Bias In Non-Experimental Studies Assessing Treatment Outcomes In Prostate Cancer, David E. Guy

Electronic Thesis and Dissertation Repository

We evaluated the ability of matching techniques to balance baseline characteristics between treatment groups using non-experimental data. We identified a set of balance diagnostics that assessed key differences in baseline covariates with potential for confounding. These diagnostics were used in a novel systematic approach to developing and evaluating models for use in propensity score matching that optimized balance and data retention. We then compared the performance of propensity score and coarsened exact matching strategies in optimizing balance and data retention, using non-experimental data from a pan-Canadian prostate cancer database. Both matching techniques balanced baseline covariates adequately and retained approximately 70% …

Extension Of The Two-Step Approach For Informative Dropout In Survival Analysis, Cristina Murray-Krezan

Mathematics & Statistics ETDs

Chronic kidney disease (CKD) in children is known to result in poor growth and quality of life, and frequently results in kidney failure. The Chronic Kidney Disease in Children study (CKiD) is a prospective cohort study enrolling children ages 1 to 16 to assess health outcomes in children with CKD including the effects of declining glomerular filtration rate and the resulting consequences of growth failure on morbidity. Quantification of the magnitude of the risk for decreased kidney function and, ultimately, failure has been achieved through a variety of studies, often including cohort studies such as the CKiD study. Longitudinal studies …

Innovative Statistical Models In Cancer Immunotherapy Trial Design, Jing Wei

Theses and Dissertations--Statistics

A challenge arising in cancer immunotherapy trial design is the presence of non-proportional hazards (NPH) patterns in survival curves. We considered three different NPH patterns caused by delayed treatment effect, cure rate and responder rate of treatment group in this dissertation. These three NPH patterns would violate the proportional hazard model assumption and ignoring any of them in an immunotherapy trial design will result in substantial loss of statistical power.

In this dissertation, four models to deal with NPH patterns are discussed. First, a piecewise proportional hazards model is proposed to incorporate delayed treatment effect into the trial design consideration. …

Causal Inference And Prediction On Observational Data With Survival Outcomes, Xiaofei Chen

Statistical Science Theses and Dissertations

Infants with hypoplastic left heart syndrome require an initial Norwood operation, followed some months later by a stage 2 palliation (S2P). The timing of S2P is critical for the operation’s success and the infant’s survival, but the optimal timing, if one exists, is unknown. We attempt to estimate the optimal timing of S2P by analyzing data from the Single Ventricle Reconstruction Trial (SVRT), which randomized patients between two different types of Norwood procedure. In the SVRT, the timing of the S2P was chosen by the medical team; thus with respect to this exposure, the trial constitutes an observational study, and …

Estimation Of The Treatment Effect With Bayesian Adjustment For Covariates, Li Xu

Theses and Dissertations--Statistics

The Bayesian adjustment for confounding (BAC) is a Bayesian model averaging method to select and adjust for confounding factors when evaluating the average causal effect of an exposure on a certain outcome. We extend the BAC method to time-to-event outcomes. Specifically, the posterior distribution of the exposure effect on a time-to-event outcome is calculated as a weighted average of posterior distributions from a number of candidate proportional hazards models, weighing each model by its ability to adjust for confounding factors. The Bayesian Information Criterion based on the partial likelihood is used to compare different models and approximate the Bayes factor. …

Effects Of Perioperative Hyperglycemia In Patients With Diabetes Compared To Patients Without Diabetes: A Retrospective Study Of Treatment And Outcomes, Matthew Anderson

Capstone Experience

The main goal of this project was to examine the differences in perioperative hyperglycemia treatment received by patients with a diagnosis of diabetes mellitus (DM) and patients without a diagnosis of diabetes (NDM); and how these treatment differences can affect the length of hospital stay. Studies have revealed that, when comparing DM and NDM patients with the same degree of perioperative hyperglycemia, NDM patients suffer worse outcomes. It has been suggested in previous research that this may be because NDM patients receive treatment that does not measure up to the standard of care treatment that DM patients receive. In this …

Methods For Evaluating Dropout Attrition In Survey Data, Camille J. Hochheimer

Theses and Dissertations

As researchers increasingly use web-based surveys, the ease of dropping out in the online setting is a growing issue in ensuring data quality. One theory is that dropout or attrition occurs in phases that can be generalized to phases of high dropout and phases of stable use. In order to detect these phases, several methods are explored. First, existing methods and user-specified thresholds are applied to survey data where significant changes in the dropout rate between two questions is interpreted as the start or end of a high dropout phase. Next, survey dropout is considered as a time-to-event outcome and …

Variable Selection In Accelerated Failure Time (Aft) Frailty Models: An Application Of Penalized Quasi-Likelihood, Sarbesh R. Pandeya

Electronic Theses and Dissertations

Variable selection is one of the standard ways of selecting models in large scale datasets. It has applications in many fields of research study, especially in large multi-center clinical trials. One of the prominent methods in variable selection is the penalized likelihood, which is both consistent and efficient. However, the penalized selection is significantly challenging under the influence of random (frailty) covariates. It is even more complicated when there is involvement of censoring as it may not have a closed-form solution for the marginal log-likelihood. Therefore, we applied the penalized quasi-likelihood (PQL) approach that approximates the solution for such a …

Quantile Regression For Survival Data With Delayed Entry, Boqin Sun

Doctoral Dissertations

Delayed entry arises frequently in follow-up studies for survival outcomes, where additional study subjects enter during the study period. We propose a quantile regression model to analyze survival data subject to delayed entry and right-censoring. Such a model offers flexibility in assessing covariate effects on survival outcome and the regression coefficients are interpretable as direct effects on the event time. Under the conditional independent censoring assumption, we proposed a weighted martingale-based estimating equation, and formulated the solution finding as a $\ell_1$-type convex optimization problem, which was solved through a linear programming algorithm. We established uniform consistency and weak convergence of …

Real-Time Dengue Forecasting In Thailand: A Comparison Of Penalized Regression Approaches Using Internet Search Data, Caroline Kusiak

Masters Theses

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 …

Regression Analysis For Ordinal Outcomes In Matched Study Design: Applications To Alzheimer's Disease Studies, Elizabeth Austin

Masters Theses

Alzheimer's Disease (AD) affects nearly 5.4 million Americans as of 2016 and is the most common form of dementia. The disease is characterized by the presence of neurofibrillary tangles and amyloid plaques [1]. The amount of plaques are measured by Braak stage, post-mortem. It is known that AD is positively associated with hypercholesterolemia [16]. As statins are the most widely used cholesterol-lowering drug, there may be associations between statin use and AD. We hypothesize that those who use statins, specifically lipophilic statins, are more likely to have a low Braak stage in post-mortem analysis.

In order to address this hypothesis, …

On The Estimation Of Penetrance In The Presence Of Competing Risks With Family Data, Daniel Prawira

Electronic Thesis and Dissertation Repository

In family studies, we are interested in estimating the penetrance function of the event of interest in the presence of competing risks. Failure to account for competing risks may lead to bias in the estimation of the penetrance function. In this thesis, three statistical challenges are addressed: clustering, missing data, and competing risks. We proposed the cause-specific model with shared frailty and ascertainment correction to account for clustering and competing risks along with ascertainment of families into study. Multiple imputation is used to account for missing data. The simulation study showed good performance of our proposed model in estimating the …

Statistical Methods For High Dimensional Data Arising From Large Epidemiological Studies, Hui Xu

Doctoral Dissertations

In this thesis, we propose statistical models for addressing commonly encountered data types and study designs in large epidemiologic investigations aimed at understanding the molecular basis of complex disorders. The motivating applications come from diverse disease areas in Women's Health, including the study of type II diabetes in the Women's Health Initiative (WHI), invasive breast cancer in the Nurses' Health Study and the study of the metabolomic underpinnings of cardiovascular disease in the WHI. We have also put significant effort into making the implementation of the proposed methods accessible through freely available, user-friendly software packages in R. The first chapter …

Statistical Methods For Two Problems In Cancer Research: Analysis Of Rna-Seq Data From Archival Samples And Characterization Of Onset Of Multiple Primary Cancers, Jialu Li

Dissertations & Theses (Open Access)

My dissertation is focused on quantitative methodology development and application for two important topics in translational and clinical cancer research.

The first topic was motivated by the challenge of applying transcriptome sequencing (RNA-seq) to formalin-fixation and paraffin-embedding (FFPE) tumor samples for reliable diagnostic development. We designed a biospecimen study to directly compare gene expression results from different protocols to prepare libraries for RNA-seq from human breast cancer tissues, with randomization to fresh-frozen (FF) or FFPE conditions. To comprehensively evaluate the FFPE RNA-seq data quality for expression profiling, we developed multiple computational methods for assessment, such as the uniformity and continuity …

Comparison Of Survival Curves Between Cox Proportional Hazards, Random Forests, And Conditional Inference Forests In Survival Analysis, Brandon Weathers

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Survival analysis methods are a mainstay of the biomedical fields but are finding increasing use in other disciplines including finance and engineering. A widely used tool in survival analysis is the Cox proportional hazards regression model. For this model, all the predicted survivor curves have the same basic shape, which may not be a good approximation to reality. In contrast the Random Survival Forests does not make the proportional hazards assumption and has the flexibility to model survivor curves that are of quite different shapes for different groups of subjects. We applied both techniques to a number of publicly available …

Joint Modelling In Liver Transplantation, Elizabeth M. Renouf

Electronic Thesis and Dissertation Repository

In the setting of liver transplantation, clinical trials and transplant registries regularly collect repeated measurements of clinical biomarkers which may be strongly associated with a time-to-event such as graft failure or disease recurrence. Multiple time-to-event outcomes are routinely collected. However, joint models are rarely used. This thesis will describe important considerations for joint modelling in the setting of liver transplantation. We will focus on transplant registry data from the United States. We develop a new tool for joint modelling in the context where a critical health event can be tracked in the longitudinal biomarker and often presents as a non-linear …

Statistical Methods For Environmental Exposure Data Subject To Detection Limits, Yuchen Yang

Theses and Dissertations--Statistics

In this dissertation, we develop unified and efficient nonparametric statistical methods for estimating and comparing environmental exposure distributions in presence of detection limits. In the first part, we propose a kernel-smoothed nonparametric estimator for the exposure distribution without imposing any independence assumption between the exposure level and detection limit. We show that the proposed estimator is consistent and asymptotically normal. Simulation studies demonstrate that the proposed estimator performs well in practical situations. A colon cancer study is provided for illustration. In the second part, we develop a class of test statistics to compare exposure distributions between two groups by using …

The Association Between The Il-1 Pathway, Isaac C. Wun

Dissertations & Theses (Open Access)

Cutaneous malignant melanoma (CMM) is a potentially lethal malignancy that warrants attention and further research, as it is known to that there is an increasing rate of incidence in theUnited States, and it is also known that exposure to UV light is its most crucial risk factor, and family history of melanoma is also an important risk factor. Melanoma is an aggressive and lethal cancer in humans. There are an estimated new 132,000 melanoma cases annually worldwide, and the trend has doubled in the past 20 years. However, attempts to treat melanoma have encountered considerable resistance and remained ineffective. The …

Flexible Partially Linear Single Index Regression Models For Multivariate Survival Data, Na Lei

Electronic Thesis and Dissertation Repository

Survival regression models usually assume that covariate effects have a linear form. In many circumstances, however, the assumption of linearity may be violated. The present work addresses this limitation by adding nonlinear covariate effects to survival models. Nonlinear covariates are handled using a single index structure, which allows high-dimensional nonlinear effects to be reduced to a scalar term. The nonlinear single index approach is applied to modeling of survival data with multivariate responses, in three popular models: the proportional hazards (PH) model, the proportional odds (PO) model, and the generalized transformation model. Another extension of the PH and PO model …

Evaluation Of The Survival Effect For Various Treatment Modalities Among Stage Ii And Iii Rectal Cancer Patients In California, 1994-2009, Myung Mi Cho

Loma Linda University Electronic Theses, Dissertations & Projects

Background: European trials evaluating the effect of preoperative (PreOP) versus postoperative chemoradiotherapy (PostOP CRT) found no survival benefit. However, the effect of a change from PostOP to PreOP CRT has not been evaluated in a population-based setting. We sought to evaluate multimodal treatment changes and overall survival for perioperative (PeriOP) CRT versus surgery alone and for PreOP versus PostOP CRT from 1994 through 2009 among patients receiving radical surgery for stage II and III rectal cancer (RC).

Patients and Methods: We conducted a nonconcurrent cohort study evaluating demographic predictors of multimodal therapy for stage II and III RC using …

An Analysis Of Risk Reduction Choices In Dcis Breast Cancer Patients, Lauren Soltesz

Statistics

The main focus of this paper was to evaluate possible demographic and clinical characteristics associated with a woman’s choice of breast conserving surgery (BCS), unilateral mastectomy (ULM), or bilateral risk reduction mastectomy (BRRM). The cohort consisted of patients presenting to the City of Hope National Medical Center with ductal carcinoma in situ breast cancer who elected to have cancer directed surgery (N=305). Analyses to examine associations of patient characteristics with type of surgery were conducted using a multinomial logistic regression. Results showed that older women were more likely to choose breast conserving surgery over bilateral risk reduction mastectomy than younger …

Bayesian Phase I Dose Finding In Cancer Trials, Lin Yang

Dissertations & Theses (Open Access)

This dissertation explores phase I dose-finding designs in cancer trials from three perspectives: the alternative Bayesian dose-escalation rules, a design based on a time-to-dose-limiting toxicity (DLT) model, and a design based on a discrete-time multi-state (DTMS) model.

We list alternative Bayesian dose-escalation rules and perform a simulation study for the intra-rule and inter-rule comparisons based on two statistical models to identify the most appropriate rule under certain scenarios. We provide evidence that all the Bayesian rules outperform the traditional ``3+3'' design in the allocation of patients and selection of the maximum tolerated dose.

The design based on a time-to-DLT model …