Feasibility Estimation for Clinical Trials

At least 90% of trials are extended by at least 6 weeks because investigators fail to enroll patients on schedule (Ledford, 2011). It is therefore important at trial design-time to have good insight in how the choice of the eligibility criteria affects the recruitment rate. Based on that insight, trial designers can then adjust the eligibility criteria in order to ensure realistic recruiting rates. In this paper we propose a simple mathematical model to determine how eligibility criteria determine the recruitment rate. Our model allows us to calculate a newly proposed ”relative” measure for the effect of an eligibility condition on the recruitment rate: instead of estimating the recruitment rate of the total set of conditions, our new relative measure calculates the effect of adding, removing or changing an individual condition in the light of the other conditions. This allows for a much more fine-grained insight into the effect of individual trial-conditions, and into the interactions between the conditions. We have implemented this mathematical model in efficient algorithms, and we demonstrate our model on both real and synthetic patient data. Our experiments show that almost all medical trials in our test corpus contain logically redundant criteria, and that this redundancy can only be revealed with our new relative feasibility measure (and not with the classical absolute feasibility measure). To increase the reproducibility of our results, we have made our datasets available online.