000012324 001__ 12324 000012324 005__ 20241023145014.0 000012324 0247_ $$2doi$$a10.6082/uchicago.12324 000012324 037__ $$aTHESIS 000012324 037__ $$bDissertation 000012324 041__ $$aeng 000012324 245__ $$aBayesian Parametric and Nonparametric Models for Clinical Trial 000012324 260__ $$bUniversity of Chicago 000012324 269__ $$a2024-06 000012324 300__ $$a162 000012324 336__ $$aDissertation 000012324 502__ $$bPh.D. 000012324 520__ $$aWith the advancement of computer-based technology, progress in computation has enabled effective real-life application of sampling methods. This has led to the adoption of Bayesian models in clinical trials. To this end, this dissertation comprises three papers that develop and apply Bayesian parametric and nonparametric models for the planning and analysis of clinical trials. The first paper focuses on developing a statistical clustering method that clusters subjects across multiple groups through Bayesian nonparametric modeling. This method, named the Plaid Atoms Model (PAM), is built on the concept of “atom-skipping", which allows the model to stochastically assign zero weights to atoms in an infinite mixture. By implementing atom-skipping across different groups, PAM establishes a dependent clustering pattern, identifying both common and unique clusters among these groups. This approach furtherprovides interpretable posterior inference such as the posterior probability of cluster being unique to a single group or common across a subset of groups. The paper also discusses the theoretical properties of the proposed and related models. Minor extensions of the model for multivariate or count data are presented. Simulation studies and applications using real-world datasets illustrate the performance of the new models with comparison to existing models. The second paper delves into leveraging information from external data to augment the control arm of a current randomized clinical trial (RCT), aiming to borrow information while addressing potential heterogeneity in subpopulations between the external data and the current trial. To achieve this, we employ the PAM model introduced in the first paper. This method is used to identify overlapping and unique subpopulations across datasets, enabling us to limit information borrowing to those subpopulations common to both the external data and the current trial. This strategy establishes a Hybrid Control (HC) that results in a more precise estimation of treatment effects. Through simulation studies, we validate the robustness of the proposed method. Additionally, its application to an Atopic Dermatitis dataset shows the method’s improved treatment effect estimation. The third paper introduces a Bayesian Estimator of Sample Size (BESS) method and its application in oncology dose optimization clinical trials. BESS seeks a balance among three factors: Sample size, Evidence from observed data, and Confidence in posterior inference. It uses a simple logic of "given the evidence from data, with a specific sample size one is guaranteed to achieve a degree of confidence in the posterior inference." This approach contrasts with traditional sample size estimation (SSE), which typically relies on frequentist inference: BESS assumes a possible outcome from the observed data rather than utilizing the true parameters values in SSE method’s sample size calculation. As a result, BESS does not calibration sample size based on type I or II error rates but on posterior probabilities, offering a more interpretable statement for investigators. The proposed method can easily accommodates sample size re-estimation and the incorporation of prior information. We demonstrate its performance through case studies via oncology optimization trials. However, BESS can be applied in general hypothesis tests which we discuss at the end. 000012324 542__ $$fCC BY 000012324 650__ $$aBiostatistics 000012324 650__ $$aStatistics 000012324 653__ $$aClinical Trial 000012324 653__ $$aConfidence 000012324 653__ $$aDependent clustering 000012324 653__ $$aDirichlet process 000012324 653__ $$aPower priors 000012324 653__ $$aReal world data 000012324 690__ $$aBiological Sciences Division 000012324 690__ $$aPritzker School of Medicine 000012324 691__ $$aPublic Health Sciences 000012324 7001_ $$aBi, Dehua 000012324 72012 $$aJi, Yuan 000012324 72014 $$aAwad, Issam 000012324 72014 $$aZhou, Tianjian 000012324 72014 $$aPolley, Mei-Yin 000012324 8564_ $$9a5f25173-a5e0-4bf0-9f32-576e226594ff$$ePublic$$s5155457$$uhttps://knowledge.uchicago.edu/record/12324/files/Bi_uchicago_0330D_17341.pdf 000012324 909CO $$ooai:uchicago.tind.io:12324$$pDissertations$$pGLOBAL_SET 000012324 983__ $$aDissertation