2026-09-20T01:55:01Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1230752026-06-06T00:54:50Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Andrew W. Lo.
Chavez-Gehrig, Arturo.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-11-22T00:10:09Z
2019-11-22T00:10:09Z
2019
2019
https://hdl.handle.net/1721.1/123075
1127579518
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 69-71).
This thesis explores the problem of characterizing the covariance of clinical trial outcomes using drug and trial features. The binary nature of FDA approvals makes drug development risky, but approaches in finance theory could better manage that risk, allowing more high potential drugs to be developed. To apply these methods confidently, it is necessary to understand the covariance between projects. The paper outlines several approaches for this task and their theoretical foundations, such as finding the nearest valid covariance matrix, online sequence prediction, and a new approach using function approximation via random forest. This function approximation approach to estimating covariance is implemented and tested on historical clinical trial data.
by Arturo Chavez-Gehrig.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
71 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Modeling correlations in clinical trial outcomes using machine learning
Thesis
application/pdf
2019-11-22T00:10:08Z
Publication
Master
EECS
unknown
unknown
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