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This article will describe my experiences in creating robust strategies for optimizing site selection and offer a few recommendations.
I have run across study teams that select and reuse underperforming study sites in clinical trials. I have also encountered many study sites that have selected studies that do not enroll. This article will describe my experiences in creating robust strategies for optimizing site selection and offer a few recommendations.
Study teams tend to base their study site selection criteria on experience and relationships with research investigators. Moreover, study teams select sites in areas with high patient population concentrations. While this method can be effective by theory, it has backfired in many cases because study teams oftentimes select research institutions that experience ‘Study Crowding Rates’. In other words, too many competing studies in the area, and number of studies compared to the amount of research staff.
Study teams should break from the herd by selecting study sites that can access untapped patient populations and are not running many trials. While some of the physicians may not be as experienced, study teams can mitigate quality risk by setting up fully electronic clinical trial capabilities, and conducting analytical monitoring.
Big data solutions now enable us to aggregate clinical trial data, as demonstrated in Figure 1.
Figure 1 illustrates aggregated data for all actively recruiting Alzheimer’s clinical trials in the US. There are 38 Alzheimer’s clinical trials in New York City. Due to study crowding in the NYC region, we looked at sites in the Toms River and New Jersey areas, where there are less clinical trials, and we found several research centers with solid capabilities and sufficient research experience.
We executed advanced analytical algorithms to evaluate enrollment potential for each of the selected study sites, and we discovered that a study site in Toms River exhibited the highest enrollment potential compared to other sites in New Jersey (this algorithm was over 70% accurate in predicting enrollment in an immunology trial).
Estimates suggest that it costs ~$50,000 to initiate a study site, and 15-20% never enroll one clinical trial subject. Implementing analytical models during the site selection phase can minimize enrollment risks and reduce costs.
It is estimated that almost 30% of patients drop out of a clinical trial1, which leads study teams to extend clinical trial deadlines and budgets. Reasons can include poorly designed protocols, distance from study sites, and socioeconomic factors.2 When selecting study sites, study teams must ensure that patients have convenient access to the study site, and that patient populations are close in proximity to the study site. In addition, it is important to understand your patient population; in Alzheimer’s trial, consider targeting a patient’s caregiver, and understand patient geodemographics, so that you can engage your patients during enrollment campaign design and execution.
Moe Alsumidaie is a clinical trials and healthcare business strategist, who specializes in advanced data analysis. Do you have the expertise to join the Exclusive Breakthrough Solutions in Clinical Trials & Healthcare Group? Apply Here.
 Source: Seeking Predictable Subject Characteristics That Influence Clinical Trial Discontinuation. Jai Shankar K.B. Yadlapalliand Irwin G. Martin. Drug Information Journal, May 2012; vol.46:pp.313-319,first published on April 9, 2012