Estimation of clinical trial success rates and related parameters.

Chi Heem Wong,Kien Wei Siah,Andrew W Lo

doi:10.1093/biostatistics/kxx069

Chi Heem Wong, Kien Wei Siah + Show 1 more

Open Access

https://doi.org/10.1093/biostatistics/kxx069

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Abstract

SUMMARYPrevious 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.

Highlights

The probability of success (POS) of a clinical trial is critical for clinical researchers and biopharma investors to evaluate when making scientific and economic decisions
We construct estimates of the POS and other related risk characteristics of clinical trials using 406 038 entries of industry- and non-industry-sponsored trials, corresponding to 185 994 unique trials over 21 143 compounds from Informa Pharma Intelligence’s Trialtrove and Pharmaprojects databases from January 1, 2000 to October 31, 2015. This is the largest investigation far into clinical trial success rates and related parameters. To process this large amount of data, we develop an automated algorithm that traces the path of drug development, infers the phase transitions, and computes the POS statistics in hours
We find that 13.8% of all drug development programs eventually lead to approval, which is higher than the 10.4% reported by Hay and others (2014) and the 9.6% reported by Thomas and others (2016)

Summary

Introduction

The probability of success (POS) of a clinical trial is critical for clinical researchers and biopharma investors to evaluate when making scientific and economic decisions. Without up-to-date estimates of the POS, investors may misjudge the risk and value of drug development, leading to lost opportunities for both investors and patients. One of the biggest challenges in estimating the success rate of clinical trials is access to accurate information on trial characteristics and outcomes. Gathering such data is expensive, time-consuming, and. Previous studies of success rates have been constrained by the data in several respects. In the landmark study of this area, Hay and others (2014) analyzed 7372 development paths of 4451 drugs using 5820 phase transitions. Smietana and others (2016) computed statistics using 17 358 phase transitions for 9200 compounds, while Thomas and others (2016) used 9985 phase transitions for 7455 clinical drug development programs. It is estimated that trained analysts would require tens of thousands of hours of labor to incorporate its full information manually to produce POS estimates

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