Please enter valid positive numbers for all fields.
Required Rate (Patients per Site per Month)
0.00
Total Monthly Enrollment (Global)
0
Patients Per Site (Total)
0
function calculateCTRecruitment() {
// Clear previous error
document.getElementById('ct_error_msg').style.display = 'none';
document.getElementById('ct_results_area').style.display = 'none';
// Get Input Values
var targetN = parseFloat(document.getElementById('target_n').value);
var numSites = parseFloat(document.getElementById('num_sites').value);
var months = parseFloat(document.getElementById('duration_months').value);
// Validation
if (isNaN(targetN) || isNaN(numSites) || isNaN(months) || targetN <= 0 || numSites <= 0 || months <= 0) {
document.getElementById('ct_error_msg').style.display = 'block';
document.getElementById('ct_error_msg').innerText = "Please enter valid values greater than zero for all inputs.";
return;
}
// Calculations
// 1. Global Monthly Enrollment Required
var globalMonthly = targetN / months;
// 2. Patients Per Site Per Month (PSPM)
var pspm = globalMonthly / numSites;
// 3. Average Total Patients Per Site
var perSiteTotal = targetN / numSites;
// Update DOM
document.getElementById('res_pspm').innerText = pspm.toFixed(3) + " pts/site/mo";
document.getElementById('res_monthly_global').innerText = globalMonthly.toFixed(1) + " patients/mo";
document.getElementById('res_per_site_total').innerText = perSiteTotal.toFixed(1) + " patients/site";
// Show Results
document.getElementById('ct_results_area').style.display = 'block';
}
How to Calculate Recruitment Rate in Clinical Trials
Accurately calculating the recruitment rate is one of the most critical aspects of clinical trial feasibility and planning. Underestimating recruitment speed leads to study delays and increased budgets, while overestimating can lead to resource strain. This guide explains the core metrics used to track and project enrollment speed.
What is the Recruitment Rate?
In clinical research, the recruitment rate is the velocity at which participants are screened, enrolled, and randomized into a study. The industry standard metric for this is PSPM (Patients per Site per Month), sometimes referred to as SSMR (Site Study Monthly Recruitment).
The Recruitment Rate Formula
To determine the required performance to meet a specific deadline, the basic formula involves the total target sample size ($N$), the number of active clinical sites ($S$), and the duration of the recruitment period in months ($T$).
Result: You need a rate of approximately 4.17 patients per site per month to meet the deadline.
Why Tracking Monthly Enrollment Matters
Calculating the global monthly enrollment helps Sponsors and CROs understand the volume of data that will be generated.
Resource Planning: High recruitment rates require more Clinical Research Associates (CRAs) for monitoring visits.
Drug Supply: Manufacturing and logistics must align with the enrollment velocity to prevent stock-outs at sites.
Budgeting: Most investigator payments are triggered by enrollment milestones; faster recruitment accelerates cash flow requirements.
Factors Influencing Recruitment Rates
When using the calculator above, consider these variables which often reduce the theoretical rate in the real world:
Site Activation Stagger: Not all sites open on day one. A "ramp-up" period usually occurs.
Seasonality: Recruitment often slows during summer holidays or end-of-year breaks.
Protocol Complexity: Stringent inclusion/exclusion criteria significantly lower the screen-to-randomization ratio.
Competing Trials: Other studies recruiting the same patient population at the same sites will dilute the rate.
Using the Calculator for Feasibility
You can use this tool in reverse logic for feasibility. If you know that the average recruitment rate for a specific indication (e.g., Oncology Phase III) is historically 0.5 patients/site/month, you can adjust the "Number of Active Sites" or "Recruitment Duration" in the calculator until the calculated "Required Rate" matches your historical benchmark. If the required rate is significantly higher than historical data, your study timeline is likely unrealistic.