TY - JOUR T1 - Why clinical trials are terminated JF - bioRxiv DO - 10.1101/021543 SP - 021543 AU - Theodore R. Pak AU - Maria D. Rodriguez AU - Frederick P. Roth Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/07/02/021543.abstract N2 - Background: Evidence-based clinical practice relies on unbiased reporting of negative results. Meta-analysis of drug safety and efficacy across many clinical trials is difficult given the unconstrained nature of reasons that are provided to ClinicalTrials.gov to explain clinical trial terminations.Methods and Findings: We scanned all trials in ClinicalTrials.gov marked with the “terminated” status (N=3122), meaning the trial had been stopped before the scheduled end date. Under the current reporting framework, any number of reasons may be given for termination, and these need not conform to a controlled vocabulary. Here we develop a controlled vocabulary for trial termination, and map each terminated trial to as many as three vocabulary terms. Mapping to this “ontology of termination” allows further analysis and conclusions. First, we identify the subset of terminated trials that ended citing safety concerns (6.2%) or failure to establish efficacy (10.8%), and were further able to stratify these rates across trials of different phases. Second, we examine termination reasons where a stricter data model could have preserved more evidentiary value, either because the data model was misused (7.6%) or because the given reason left unclear whether the decision to terminate was based on analysis of the data (74.9%, with 20.4% mentioning a decision-maker that may have had access to the data). Third, we show that imposing a controlled vocabulary of reasons for termination would avoid ambiguity and improve the evidentiary value of clinical trials.Conclusions: We encourage wider use of an “ontology of termination” and propose four questions that should be posed on trial termination. These simple steps would promote transparency and enable ready access to negative trial results for meta-analysis. ER -