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Dimitris J. Bertsimas. Relyea, Stephen L. (Stephen Lawrence) Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center Sloan School of Management 2013-12-06T19:52:36Z 2013-12-06T19:52:36Z 2013 http://hdl.handle.net/1721.1/82727 864016392 Thesis (S.M. in Operations Research)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 67-71). Since chemotherapy began as a treatment for cancer in the 1940s, cancer drug development has become a multi-billion dollar industry. Combination chemotherapy remains the leading treatment for advanced cancers, and cancer drug research and clinical trials are enormous expenses for pharmaceutical companies and the government. We propose an analytics approach for the analysis and design of clinical trials that can discover drug combinations with significant improvements in survival and toxicity. We first build a comprehensive database of clinical trials. We then use this database to develop statistical models from earlier trials that are capable of predicting the survival and toxicity of new combinations of drugs. Then, using these statistical models, we develop optimization models that select novel treatment regimens that could be tested in clinical trials, based on the totality of data available on existing combinations. We present evidence for advanced gastric and gastroesophageal cancers that the proposed analytics approach a) leads to accurate predictions of survival and toxicity outcomes of clinical trials as long as the drugs used have been seen before in different combinations, b) suggests novel treatment regimens that balance survival and toxicity and take into account the uncertainty in our predictions, and c) outperforms the trials run by the average oncologist to give survival improvements of several months. Ultimately, our analytics approach offers promise for improving life expectancy and quality of life for cancer patients at low cost. by Stephen L. Relyea. S.M. in Operations Research 71 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Operations Research Center. An analytics approach to designing clinical trials for cancer Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="97cf98d0-c944-49f2-b364-7e425f768efc"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>An analytics approach to designing clinical trials for cancer</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2013</PublicationDate> <Authors> <Author> <DisplayName>Relyea, Stephen L. (Stephen Lawrence)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Operations Research Center.</Keyword> <Abstract>Since chemotherapy began as a treatment for cancer in the 1940s, cancer drug development has become a multi-billion dollar industry. Combination chemotherapy remains the leading treatment for advanced cancers, and cancer drug research and clinical trials are enormous expenses for pharmaceutical companies and the government. We propose an analytics approach for the analysis and design of clinical trials that can discover drug combinations with significant improvements in survival and toxicity. We first build a comprehensive database of clinical trials. We then use this database to develop statistical models from earlier trials that are capable of predicting the survival and toxicity of new combinations of drugs. Then, using these statistical models, we develop optimization models that select novel treatment regimens that could be tested in clinical trials, based on the totality of data available on existing combinations. We present evidence for advanced gastric and gastroesophageal cancers that the proposed analytics approach a) leads to accurate predictions of survival and toxicity outcomes of clinical trials as long as the drugs used have been seen before in different combinations, b) suggests novel treatment regimens that balance survival and toxicity and take into account the uncertainty in our predictions, and c) outperforms the trials run by the average oncologist to give survival improvements of several months. Ultimately, our analytics approach offers promise for improving life expectancy and quality of life for cancer patients at low cost.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>