2026-09-20T11:36:22Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1537682024-03-16T03:06:00Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Lo, Andrew W.
Cho, Joonhyuk
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2024-03-15T19:22:31Z
2024-03-15T19:22:31Z
2024-02
2024-02-21T17:10:06.317Z
https://hdl.handle.net/1721.1/153768
The research combines two studies in the field of clinical trials. The first evaluates the amyotrophic lateral sclerosis (ALS) drug AMX0035 using Bayesian decision analysis (BDA), balancing FDA safety standards with patient needs. This method provides a quantitative way to consider both the patient’s perspective and the disease’s impact. The second study uses machine learning models to predict how long clinical trials will take. By analyzing a large dataset, it identifies factors that affect trial duration, helping to streamline the trial process and potentially reduce costs. Together, these studies offer new ways to evaluate and manage clinical trials, combining patient-focused evaluation with efficient trial design.
S.M.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Data-driven Analysis of Clinical Trials
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>The research combines two studies in the field of clinical trials. The first evaluates the amyotrophic lateral sclerosis (ALS) drug AMX0035 using Bayesian decision analysis (BDA), balancing FDA safety standards with patient needs. This method provides a quantitative way to consider both the patient’s perspective and the disease’s impact. The second study uses machine learning models to predict how long clinical trials will take. By analyzing a large dataset, it identifies factors that affect trial duration, helping to streamline the trial process and potentially reduce costs. Together, these studies offer new ways to evaluate and manage clinical trials, combining patient-focused evaluation with efficient trial design.</Abstract>
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