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dc.contributor.advisorDimitris J. Bertsimas.en_US
dc.contributor.authorRelyea, Stephen L. (Stephen Lawrence)en_US
dc.contributor.otherMassachusetts Institute of Technology. Operations Research Center.en_US
dc.date.accessioned2013-12-06T19:52:36Z
dc.date.available2013-12-06T19:52:36Z
dc.date.issued2013en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/82727
dc.descriptionThesis (S.M. in Operations Research)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 67-71).en_US
dc.description.abstractSince 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.en_US
dc.description.statementofresponsibilityby Stephen L. Relyea.en_US
dc.format.extent71 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectOperations Research Center.en_US
dc.titleAn analytics approach to designing clinical trials for canceren_US
dc.typeThesisen_US
dc.description.degreeS.M.in Operations Researchen_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Center
dc.contributor.departmentSloan School of Management
dc.identifier.oclc864016392en_US


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