Modeling correlations in clinical trial outcomes using machine learning

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Chavez-Gehrig, Arturo.

Advisor dc:contributor.advisor

• Andrew W. Lo.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123075

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123075