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- RNA sequencing (2)
- Adaptive (1)
- Age-at-onset penetrance (1)
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- Correlated data (1)
- Differential expressions analysis (1)
- Early phase adaptive clinical trial design; BMA-CRM design; Bridging Study; Bayesian Biosimilar Design; Calibrated Power Prior (1)
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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
A Tail-Based Test For Differential Expression Analysis And Pathway Analysis In Rna-Sequencing Data, Jiong Chen
A Tail-Based Test For Differential Expression Analysis And Pathway Analysis In Rna-Sequencing Data, Jiong Chen
Dissertations & Theses (Open Access)
RNA sequencing data have been abundantly generated in biomedical research for biomarker discovery and pathway analysis. Such data at the exon-level are usually heavily tailed and correlated. Conventional statistical tests based on the mean or median difference for differential expression likely suffer from low power when the between-group difference occurs mostly in the upper or lower tail of the distribution of gene expression. We propose a tail-based test to make comparisons between groups in terms of a specific distribution area rather than a single location. The proposed test, which is derived from quantile regression, adjusts for covariates and accounts for …
Novel Bayesian Adaptive Clinical Trial Designs In Early Phases, Haitao Pan
Novel Bayesian Adaptive Clinical Trial Designs In Early Phases, Haitao Pan
Dissertations & Theses (Open Access)
Early phase, or phase I and phase II, trials are the first step in testing new medicines that have been developed in the lab. The main goal of phase I clinical trials is to establish the recommended dose of new drugs for phase II trials. For the cytotoxic drugs, the goal is to find maximum tolerated dose (MTD). The guiding principle for dose escalation in phase I trials is to avoid exposing too many patients to subtherapeutic doses while preserving safety and maintaining rapid accrual. Therefore, dose escalation methods, especially Bayesian designs, are recommended to be used in phase I …
Detecting And Evaluating Therapy Induced Changes In Radiomics Features Measured From Non-Small Cell Lung Cancer To Predict Patient Outcomes, Xenia J. Fave
Dissertations & Theses (Open Access)
The purpose of this study was to investigate whether radiomics features measured from weekly 4-dimensional computed tomography (4DCT) images of non-small cell lung cancers (NSCLC) change during treatment and if those changes are prognostic for patient outcomes or dependent on treatment modality. Radiomics features are quantitative metrics designed to evaluate tumor heterogeneity from routine medical imaging. Features that are prognostic for patient outcome could be used to monitor tumor response and identify high-risk patients for adaptive treatment. This would be especially valuable for NSCLC due to the high prevalence and mortality of this disease.
A novel process was designed to …
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 …
Further Advances For The Sequential Multiple Assignment Randomized Trial (Smart), Tianjiao Dai
Further Advances For The Sequential Multiple Assignment Randomized Trial (Smart), Tianjiao Dai
Dissertations & Theses (Open Access)
ABSTRACT
FURTHER ADVANCES FOR THE SEQUENTIAL MULTIPLE ASSIGNMENT RANDOMIZED TRIAL (SMART)
Tianjiao Dai, M.S.
Advisory Professor: Sanjay Shete, Ph.D.
Sequential multiple assignment randomized trial (SMART) designs have been developed these years for studying adaptive interventions. In my Ph.D. study, I mainly investigate how to further improve SMART designs and optimize the interventions for each individual in the trial. My dissertation has focused on two topics of SMART designs.
1) Developing a novel SMART design that can reduce the cost and side effects associated with the interventions and proposing the corresponding analytic methods. I have developed a time-varying SMART design in …