Biostatistics Commons^™

Inter-Rater Reliability Of Statistics Based On Reconstructed Individual Patient Data From Published Kaplan-Meier Curves, Megan E. Smith

Theses & Dissertations

Introduction: Time-to-event outcomes include two elements: an indicator variable for whether the event has taken place, and the length of time from some origin point to the occurrence of the event of interest. Due to the complexity of these data, secondary analysis methods, such as indirect comparisons and meta-analysis, are easier to perform when individual-level patient data (IPD) is available.

Objectives: In 2021, an R package IPDfromKM was published, which contains an algorithm for reconstructing IPD from a Kaplan-Meier graph. The current research aimed to investigate the reproducibility of the IPDfromKM algorithm.

Methods: Three statisticians (MS, LS, …

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 …

Urinary Bile Acid Indices As Prognostic Biomarkers For The Complications Of Liver Diseases, Wenkuan Li

Theses & Dissertations

Hepatobilary diseases cause the accumulation of toxic bile acids (BA) in the liver, blood, and other tissues, which may lead to an unfavorable prognosis. In this study, we compared the urinary BA profile in 257 patients with hepatobilary diseases during a 7-year follow-up period. We investigated the use of the urinary BA profile to develop logistic regression models to predict the prognosis of hepatobiliary diseases in terms of developing disease-related complications, especially for ascites. The urinary BA profile was characterized by calculating BA indices, which quantify the composition, metabolism, hydrophilicity, and toxicity of the BA profile. All patients had high …

Bayesian Modeling For Longitudinal Count Data: Applications In Biomedical Research, Morshed Alam

Theses & Dissertations

Biomedical count data such as the number of seizures for epilepsy patients, number of new tumors at each visit or the number vomiting after each chemo-radiation for the cancer patients are common. Often these counts are measured longitudinally from patients or within clusters in multi-site trials. The Poisson and negative binomial models may not be adequate when data exhibit over or under-dispersion, respectively. On the contrary, a variety of dispersion conditions in count data can be captured by Conway-Maxwell Poisson (CMP) model.

This doctoral dissertation relegates to developing a statistical methodology to model longitudinal count data distributed as CMP via …

Multi-Level Small Area Estimation Based On Calibrated Hierarchical Likelihood Approach Through Bias Correction With Applications To Covid-19 Data, Nirosha Rathnayake

Theses & Dissertations

Small area estimation (SAE) has been widely used in a variety of applications to draw estimates in geographic domains represented as a metropolitan area, district, county, or state. The direct estimation methods provide accurate estimates when the sample size of study participants within each area unit is sufficiently large, but it might not always be realistic to have large sample sizes of study participants when considering small geographical regions. Meanwhile, high dimensional socio-ecological data exist at the community level, providing an opportunity for model-based estimation by incorporating rich auxiliary information at the individual and area levels. Thus, it is critical …

Beta Regression Models For Repeated-Measures Data Analysis, Nicholas A. Hein

Theses & Dissertations

Bounded data often give rise to uncorrectable skew and heteroscedasticity. Bounded data are a relatively frequent occurrence in clinical and research settings. For example, in neuropsychology, most neurocognitive tests are bounded, and subjects are repeatedly measured over time. The statistician needs to choose a model that accounts for the correlated nature of the repeated measures. The Beta distribution is a natural choice for modeling bounded data. Currently, generalized linear mixed models (GLMM) and generalized estimating equations (GEE) are two methods that can be used to model Beta distributed data with repeated measures. However, GLMMs and GEEs have limitations, i.e., GLMMs …