| |July 201919each activity. The shapes of these output curves are quite similar for well-run clinical trials, regardless of therapeutic area, geography, etc. But, you will never see these similarities unless you characterize the overall subprocess, collect and visualize the appropriate activity data, and then analyze the shapes of the output curves.But what about seeing the connections between subprocesses? Here, we instinctively recognize that the connections are there. After all, it is axiomatic that I can't activate a trial site if I haven't selected the trial site for inclusion in the study, I can't screen/enroll patients if I haven't activated the trial site, and I can't generate study data if patients haven't enrolled in the study. However, this type of thinking does not result in understanding the quantitative connections between subprocesses.In fact, many of us who work in the industry know that we rarely look for quantitative connections between subprocesses. We view these subprocesses as being more or less standalone processes. As a friend of mine at a global CRO once quipped, "In this business, we all live on our separate islands ... the Study Start Up island, the Project Management island, the Data Management island ... and no one talks to anyone from any of the other islands".But the connections are there.About 8 years ago, after I had transitioned from Big Pharma to a global CRO, I was given the opportunity to run a global Study Start Up group. Having come from a background in Engineering and Manufacturing, I assumed, given the complexity and expense inherent in a clinical trial, that there would be a significant amount of Operations Research literature on the clinical trial process, in general, and Study Start Up, in particular.I was mistaken.However, I did come across one excellent article by Dr. Gen Li in Pharmaceutical Executive magazine in the December 2008 issue entitled "Site Activation: The Key to More Efficient Clinical Trials" that had a fascinating graph in it. The graph showed a beautiful linear relationship between investigator site activation cycle time (the time from study start to the time all sites were activated) and patient enrollment cycle time (the time from study start to the time all patients were enrolled), for a number of clinical trials in a particular disease condition. Dr. Li's insights in the article are worth reading in and of themselves, but my Engineering training told me that any relationship that was as strong as the one shown meant that there was also a specific mathematical relationship between the two cycle times. After several years of exploration, I was able to determine the explicit mathematical connection between site activation and patient enrollment, and empirically verify the existence of that relationship by looking at clinical trial data for many trials in many indications. An idealized view of that relationship is shown in the plot below.Ultimately, it was possible to leverage the insights provided by the mathematics to build a simple discrete model that allowed me to reproduce the linear relationship shown in Dr. Li's research between site activation cycle times and patient enrollment cycle times.Similar analysis can be done to discover the mathematical relationship between patient enrollment and how data flow in a clinical trial. Again, that this relationship exists makes intuitive sense since without a patient being enrolled in a clinical trial, no patient data for that patient can be generated. However, to be able to derive the mathematical relationship requires looking at the output curves for patient enrollment and data flow and recognizing the mathematics that drive the shapes of the output curves.Eventually, it was possible to determine the characteristic shape of the output curve for each clinical trial subprocess, and co-plot the various output curves for Site Selection, Site Activation, Patient Screening, Patient Enrollment and Data Flow. The distinct pattern of these output curves has been empirically verified using comprehensive clinical trial data sets.Since each subprocess was shown to be operationally and mathemati-cally connected to the next subproc-ess, the implication was that a clinical trial can be optimized at every step to shorten the time required to com-plete the data collection and cleaning process. The desire to discover the independent variables that could be optimized to accomplish this cycle time reduction was the impetus to carry out the analysis articulated in step (2) above.
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