Modeling correlations in clinical trial outcomes using machine learning
Name
1127579518-MIT.pdf
Size
6.27 MB
Format
Adobe PDF
Checksum (MD5)
39b8fb5507a6a6501d9ade491a1003c7
Author(s)
Chavez-Gehrig, Arturo.
Advisor(s)
Andrew W. Lo.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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