2026-09-20T19:45:39Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1139642022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Andrew W. Lo.
Wong, Chi Heem
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-03-02T22:20:51Z
2018-03-02T22:20:51Z
2017
2017
http://hdl.handle.net/1721.1/113964
1023498646
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 28).
Previous estimates of drug development success rates rely on relatively small samples of pharmaceutical industry-curated databases, which are subject to potential sample selection biases. Using a sample of 185,994 unique entries of clinical-trial data for over 21,143 compounds from January 1st, 2000 to October 31st, 2015, we estimate aggregate success rates and durations of clinical trials. We also compute disaggregated estimates by stratifying across several features including: disease type, clinical phase, industry/academic sponsor, biomarker presence, lead indication status, and over time. In several cases, our results differ significantly from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, it has improved to 2.5% and 8.3% in 2014 and 2015 respectively. Also, trials with biomarkers have slightly lower success probabilities when all therapeutics groups are considered, but have much higher success probabilities in oncology and genitourinary diseases.
by Chi Heem Wong.
S.M.
vi, 41 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
Estimation of clinical trial success rates and related parameters
Thesis
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<Abstract>Previous estimates of drug development success rates rely on relatively small samples of pharmaceutical industry-curated databases, which are subject to potential sample selection biases. Using a sample of 185,994 unique entries of clinical-trial data for over 21,143 compounds from January 1st, 2000 to October 31st, 2015, we estimate aggregate success rates and durations of clinical trials. We also compute disaggregated estimates by stratifying across several features including: disease type, clinical phase, industry/academic sponsor, biomarker presence, lead indication status, and over time. In several cases, our results differ significantly from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, it has improved to 2.5% and 8.3% in 2014 and 2015 respectively. Also, trials with biomarkers have slightly lower success probabilities when all therapeutics groups are considered, but have much higher success probabilities in oncology and genitourinary diseases.</Abstract>
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