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Full-Text Articles in Clinical Trials
Machine Learning Models Leveraging Patient-Similarity And Clinical Temporality For Disease Prognoses, Ahmad F. Al Musawi
Machine Learning Models Leveraging Patient-Similarity And Clinical Temporality For Disease Prognoses, Ahmad F. Al Musawi
Theses and Dissertations
Electronic Health Records (EHRs) constitute a comprehensive and high-dimensional repository of clinical data, encompassing a wide array of patient-level information such as diagnoses, procedures, medications, laboratory results, and unstructured clinical narratives. These data hold immense potential for advancing predictive modeling in healthcare, including tasks such as disease progression modeling, hospital readmission prediction, and length of stay (LoS) estimation. However, the intrinsic complexity of EHR data—manifested in its heterogeneity, sparsity, and temporal dynamics—poses significant analytical challenges that limit the generalizability and interpretability of conventional machine learning models. Recent methodological advancements in deep learning and graph-based learning, particularly Graph Neural Networks (GNNs), …
Integrated Multiple Adaptive Design Involving Sample Size Re-Estimation And (Covariate-Adjusted) Response-Adaptive Randomization For Continuous And Binary Outcomes, Christine M. Orndahl
Integrated Multiple Adaptive Design Involving Sample Size Re-Estimation And (Covariate-Adjusted) Response-Adaptive Randomization For Continuous And Binary Outcomes, Christine M. Orndahl
Theses and Dissertations
Historically, clinical trials have been performed based on decisions made prior to the start of the trial. Adaptive designs have been developed to provide increased flexibility, allowing pre-specified changes to occur based on interim data. Each adaptive design addresses a unique pitfall of a non-adaptive design, such as minimizing the chance of an under- or over-powered study by utilizing interim data to update the sample size estimate (sample size re-estimation) or increasing the ethical benefit of a trial by allocating more participants to the better performing treatment group ([covariate-adjusted] response-adaptive randomization). Additional benefit is attainable by combining more than one …
Natural Lead-In Approaches To Response-Adaptive Allocation In Clinical Trials, Erin E. Donahue
Natural Lead-In Approaches To Response-Adaptive Allocation In Clinical Trials, Erin E. Donahue
Theses and Dissertations
Response-adaptive (RA) allocation designs can be implemented in clinical trials to skew the allocation of incoming subjects toward the better performing treatment group based on the previously accrued subjects' responses. These designs alleviate potential ethical concerns of equally allocating subjects in a trial when one treatment arm is inferior. The RA design can be generalized to include covariate information in the covariate-adjusted response-adaptive (CARA) design, which aims to maximize treatment successes conditional on a set of patient characteristics. While RA and CARA designs can improve the treatment of patients, they have unstable estimators and increased variability in early stages of …