Data-driven Analysis of Clinical Trials
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’...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/153768 |
| _version_ | 1826213868385337344 |
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| author | Cho, Joonhyuk |
| author2 | Lo, Andrew W. |
| author_facet | Lo, Andrew W. Cho, Joonhyuk |
| author_sort | Cho, Joonhyuk |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:56:07Z |
| format | Thesis |
| id | mit-1721.1/153768 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:56:07Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1537682024-03-16T03:06:00Z Data-driven Analysis of Clinical Trials Cho, Joonhyuk Lo, Andrew W. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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. 2024-03-15T19:22:31Z 2024-03-15T19:22:31Z 2024-02 2024-02-21T17:10:06.317Z Thesis https://hdl.handle.net/1721.1/153768 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Cho, Joonhyuk Data-driven Analysis of Clinical Trials |
| title | Data-driven Analysis of Clinical Trials |
| title_full | Data-driven Analysis of Clinical Trials |
| title_fullStr | Data-driven Analysis of Clinical Trials |
| title_full_unstemmed | Data-driven Analysis of Clinical Trials |
| title_short | Data-driven Analysis of Clinical Trials |
| title_sort | data driven analysis of clinical trials |
| url | https://hdl.handle.net/1721.1/153768 |
| work_keys_str_mv | AT chojoonhyuk datadrivenanalysisofclinicaltrials |