Calculate this number before reading further: from your study management system, pull every patient who withdrew or was lost to follow-up in the past 12 months. Find their ZIP code. Calculate their drive distance to your site. Then do the same for every patient who completed the protocol. Compare the average distance of completers vs. dropouts. In the majority of clinical research sites that run this analysis, dropouts live 40–60% farther from the site than completers. Distance is not just a convenience variable — it is a protocol completion predictor.
Why the Distance-Dropout Relationship Exists
Patients who live farther away face compounding barriers at each visit: longer commute time, higher transportation cost, greater disruption to work and family schedules, and more logistical planning per visit. These barriers are manageable for one or two visits but accumulate across a 10-visit protocol. Patients who drop out rarely cite distance explicitly in exit interviews — they cite “scheduling conflicts” or “too time consuming.” Distance is the underlying cause that those stated reasons obscure.
Running Your Distance-Dropout Analysis
- Export two lists from your study management system: (1) patients who withdrew or were lost to follow-up; (2) patients who completed the protocol or are on track. Include patient ZIP code for each.
- Use a free distance calculator — zipcodeapi.com or the Google Maps Distance Matrix API (free tier) — to calculate drive distance from each patient’s ZIP code to your site address.
- Calculate the average drive distance for completers and dropouts separately. Calculate the dropout rate by distance band: 0–10 miles, 10–20 miles, 20–30 miles, 30+ miles.
- The result is your site-specific distance-dropout curve. This curve tells you exactly where to draw your geotargeting boundary for maximum protocol completion rate.
Using the Data to Set Your Targeting Boundary
If your analysis shows that dropout rate exceeds 40% beyond 22 miles, set your geotargeting radius at 22 miles. You will enroll slightly fewer patients from the outer zone, but the patients you do enroll will complete at a significantly higher rate — reducing the number of replacements needed and the protocol extension risk. For sponsors, this data also supports the business case for geotargeting investment: lower dropout means fewer replacement patients means lower study cost.
When Distance Data Reveals a Satellite Opportunity
If your analysis shows a cluster of enrolled patients who completed the protocol from a ZIP code zone 30+ miles away — defying the typical distance-dropout pattern — that cluster reveals a patient population motivated enough to commit despite distance. This is your satellite screening location candidate. If you could establish a satellite visit location in that zone, you would unlock a geographically isolated population of high-completion-likelihood patients currently underserved by your site’s primary location.
48-Hour Action List
- Hour 1: Export your completer and dropout patient lists with ZIP codes from your CTMS or study files. Calculate the count in each distance band: 0–10, 10–20, 20–30, 30+ miles.
- Hour 2: Calculate dropout rate by distance band: (dropouts in band ÷ total patients in band) × 100. Plot these four data points. The dropout rate curve tells you where your targeting boundary should be.
- Hour 3: Adjust your Google Ads and Meta geographic targeting radius to match the distance at which your dropout rate exceeds your acceptable threshold (typically 35–40%).
- Day 2: Present the distance-dropout analysis to your PI and study coordinator team. Use it to brief sponsors at your next check-in — it is a data-backed explanation for your geographic recruitment strategy that builds sponsor confidence in your enrollment planning.