Estimation of clinical trial success rates and related parameters

By Chi Heem Wong, K. W. Siah, Andrew W. Lo. Biostatistics, 2018

1,352 citations recorded by the source corpus.

&NA; Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial data for over 21 143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail 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, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient‐selection have higher overall success probabilities than trials without biomarkers.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ced92ee22b6ee95157b04d46eac32082fc8b0509

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Citations in the corpus

• Machine learning applications in drug development (2019, background): “There are large variations due to drug type ($479 million for a HIV drug, compared to $936 million for a rheumatoid arthritis drug) [8].”
• Artificial Intelligence for Clinical Trial Design. (2019, background): “More than one third of all Phase III compounds fail to advance to approval [4].”
• Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, background): “Thus, the use of AI is mainly associated with a need to reduce attrition and costs [444].”
• Reinforcement Learning with Action-Derived Rewards for Chemotherapy and Clinical Trial Dosing Regimen Selection (2018, background): “For oncology trials, the failure rate rises to two-thirds (Wong et al., 2018).”
• Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials (2019, neutral): “Food and Drug Administration (FDA) approval (5), the misidentification of essential genes in cancer and the mischaracterization of reportedly target-specific inhibitors likely contribute to their exceedingly high failure rate.”