3 Ways to Approach Challenges During Study Feasibility Processes

Identifying high performing sites for a clinical trial is a crucial step in the study feasibility stage to make sure trials remain on their targeted timeline. However, it’s often difficult to confidently identify the best sites for a trial purely based on anecdotal experience or limited historic performance data.

With real-time, industry-wide, site performance data, it’s possible to more accurately assess site performance therefore predict potential enrollment timelines. Below are three ways to leverage deep industry data during study feasibility processes to improve site selection. 

Approach 1: More Accurately Forecast Enrollment via Country and Site Footprint

During early-stage conceptual planning, pharmaceutical companies and CROs need to determine the optimal number of countries and sites they will need to enroll their target number of participants. This is often challenging and can be inaccurate when using only generalized, study-level enrollment rates. With deep, granular, site and country performance data, pharmaceutical companies and CROs can more accurately forecast the enrollment at both the country and site level, enabling study planners to make confident, data-driven decisions. Starting a trial with an accurate target number of countries and sites using demonstrated historical data verified by robust predictive models, ensures the study is on the right track from day one. 

Approach 2: Utilize Scenario Analysis to Determine Enrollment Duration

Once the number of countries and sites are decided, study planners must create a target enrollment timeline. Leveraging predictive models built on a foundation of historical, cross-sponsor and cross-CRO clinical trial data to predict enrollment duration allows pharmaceutical companies and CROs to determine realistic timelines based on sites and countries chosen. Using scenario analysis, it’s possible to test different combinations and numbers of sites and countries to finalize the study footprint needed to meet targeted timelines.

Approach 3: Predict Site Performance for Site Selection

Identifying the best sites for a trial can often be difficult, however, leveraging real, historical site performance data combined with predicted site performance allows for pharmaceutical companies and CROs to gain a deeper understanding of sites’ potential and make sure they’re selecting the most effective sites within countries while avoiding non-enrolling sites for their trial. Additionally, while a site may appear to be a good fit for a certain clinical trial, it may be in an area where there are already trials underway within the same indication, leading to site congestion. Mitigating sites’ congestion and understanding contribution as part of the holistic plan is important when making site selection decisions.

A Study Feasibility Solution: Medidata Intelligent Trials

Medidata Intelligent Trials has the ability to ease many of the common roadblocks that may happen during study feasibility phases of clinical trials. Leveraging the industry’s largest clinical and operational dataset of over 34K trials and 10M patients, Intelligent Trials allows pharmaceutical companies and CROs to have much deeper insights needed for accurate study planning. Learn more.