Bayesian semiparametric and flexible models for analyzing biomedical data

In this thesis I develop novel Bayesian inference approaches for some typical data analysis problems as they arise with biomedical data. The common theme is the use of flexible and semi-parametric Bayesian models and computation intensive simulation-based implementations. In chapter 2, I propose a new approach for inference with multivariate ordinal data. The application concerns the assessment of toxicities in a phase III clinical trial. The method generalizes the ordinal probit model. It is based on flexible mixture models. In chapter 3, I develop a semi-parametric Bayesian approach for bio-panning phage display experiments. The nature of the model is a mixed effects model for repeated count measurements of peptides. I develop a non-parametric Bayesian random effects distribution and show how it can be used for the desired inference about organ-specific binding. In chapter 4, I introduce a variation of the product partition model with a non-exchangeable prior structure. The model is applied to estimate the success rates in a phase II clinical of patients with sarcoma. Each patient presents one subtype of the disease and subtypes are grouped by good, intermediate and poor prognosis. The prior model respects the varying prognosis across disease subtypes. Two subtypes with equal prognoses are more likely a priori to have similar success rates than two subtypes with different prognoses

External Validation of a Model Determining Risk of Neoplastic Progression of Barrett\u27s Esophagus in a Cohort of Us Veterans

BACKGROUND AND AIMS: Risk of esophageal adenocarcinoma (EAC) in those with Barrett\u27s esophagus (BE) is 11-fold greater than the general population. It remains unclear which BE patients are at highest risk of progression to EAC. We aimed to validate a predictive model risk-stratifying BE patients. METHODS: We conducted a retrospective cohort study at the Houston Veteran Affairs Medical Center of consecutive patients with a new diagnosis of BE from November 1990 to January 2019. Study follow-up was through February 2020. Patients were excluded if they had no follow-up EGD with esophageal biopsy sampling after the initial BE-diagnosing EGD or evidence of high-grade dysplasia (HGD) or EAC on initial EGD. We performed an external validation study of a risk model containing sex, smoking, BE length, and low-grade dysplasia (LGD) status and assessed discriminatory ability using the area under the receiver operating characteristic curve (AUROC). RESULTS: Among 608 BE patients, 24 progressed to HGD/EAC. The points-based model discriminated well with an AUROC of .72 (95% confidence interval [CI], .63-.82). When categorized into low-, intermediate-, and high-risk groups according to published cutoffs, the AUROC was poor at .57. Restructured into low-risk versus high-risk groups, the AUROC was .72 (95% CI, .64-.80). Excluding baseline LGD did not reduce discriminatory ability (AUROC, .73; 95% CI, .64-.82). CONCLUSIONS: This external validation provides further evidence that the model including sex, LGD status, smoking status, and BE length may help to risk stratify BE patients. A simplified version excluding LGD status and/or reducing the number of risk groups has increased utility in clinical practice without loss of discriminatory ability

Baseline Characteristics of Sars-Cov-2 Vaccine Non-Responders in a Large Population-Based Sample

INTRODUCTION: Studies indicate that individuals with chronic conditions and specific baseline characteristics may not mount a robust humoral antibody response to SARS-CoV-2 vaccines. In this paper, we used data from the Texas Coronavirus Antibody REsponse Survey (Texas CARES), a longitudinal state-wide seroprevalence program that has enrolled more than 90,000 participants, to evaluate the role of chronic diseases as the potential risk factors of non-response to SARS-CoV-2 vaccines in a large epidemiologic cohort. METHODS: A participant needed to complete an online survey and a blood draw to test for SARS-CoV-2 circulating plasma antibodies at four-time points spaced at least three months apart. Chronic disease predictors of vaccine non-response are evaluated using logistic regression with non-response as the outcome and each chronic disease + age as the predictors. RESULTS: As of April 24, 2023, 18,240 participants met the inclusion criteria; 0.58% (N = 105) of these are non-responders. Adjusting for age, our results show that participants with self-reported immunocompromised status, kidney disease, cancer, and other non-specified comorbidity were 15.43, 5.11, 2.59, and 3.13 times more likely to fail to mount a complete response to a vaccine, respectively. Furthermore, having two or more chronic diseases doubled the prevalence of non-response. CONCLUSION: Consistent with smaller targeted studies, a large epidemiologic cohort bears the same conclusion and demonstrates immunocompromised, cancer, kidney disease, and the number of diseases are associated with vaccine non-response. This study suggests that those individuals, with chronic diseases with the potential to affect their immune system response, may need increased doses or repeated doses of COVID-19 vaccines to develop a protective antibody level

Antibody Duration after infection From Sars-Cov-2 in the Texas Coronavirus antibody Response Survey

Understanding the duration of antibodies to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes COVID-19 is important to controlling the current pandemic. Participants from the Texas Coronavirus Antibody Response Survey (Texas CARES) with at least 1 nucleocapsid protein antibody test were selected for a longitudinal analysis of antibody duration. A linear mixed model was fit to data from participants (n = 4553) with 1 to 3 antibody tests over 11 months (1 October 2020 to 16 September 2021), and models fit showed that expected antibody response after COVID-19 infection robustly increases for 100 days postinfection, and predicts individuals may remain antibody positive from natural infection beyond 500 days depending on age, body mass index, smoking or vaping use, and disease severity (hospitalized or not; symptomatic or not)

Incorporating Uncertainty Quantification for the Performance Improvement of Academic Recommenders

Deep learning is widely used in many real-life applications. Despite their remarkable performance accuracies, deep learning networks are often poorly calibrated, which could be harmful in risk-sensitive scenarios. Uncertainty quantification offers a way to evaluate the reliability and trustworthiness of deep-learning-based model predictions. In this work, we introduced uncertainty quantification to our virtual research assistant recommender platform through both Monte Carlo dropout ensemble techniques. We also proposed a new formula to incorporate the uncertainty estimates into our recommendation models. The experiments were carried out on two different components of the recommender platform (i.e., a BERT-based grant recommender and a temporal graph network (TGN)-based collaborator recommender) using real-life datasets. The recommendation results were compared in terms of both recommender metrics (AUC, AP, etc.) and the calibration/reliability metric (ECE). With uncertainty quantification, we were able to better understand the behavior of our regular recommender outputs; while our BERT-based grant recommender tends to be overconfident with its outputs, our TGN-based collaborator recommender tends to be underconfident in producing matching probabilities. Initial case studies also showed that our proposed model with uncertainty quantification adjustment from ensemble gave the best-calibrated results together with the desirable recommender performance