You need to recruit 0 participants to ensure 0 remain at the end of the study.
function calculateSampleSize() {
// Clear errors
document.getElementById('errorN').style.display = 'none';
document.getElementById('errorRate').style.display = 'none';
document.getElementById('resultBox').style.display = 'none';
// Get inputs
var reqNInput = document.getElementById('requiredN').value;
var rateInput = document.getElementById('dropoutRate').value;
// Parse values
var reqN = parseFloat(reqNInput);
var rate = parseFloat(rateInput);
// Validation
var hasError = false;
if (isNaN(reqN) || reqN <= 0) {
document.getElementById('errorN').style.display = 'block';
hasError = true;
}
if (isNaN(rate) || rate = 100) {
document.getElementById('errorRate').style.display = 'block';
hasError = true;
}
if (hasError) {
return;
}
// Calculation Logic
// Formula: N_adjusted = N_required / (1 – dropout_rate_decimal)
var decimalRate = rate / 100;
var adjustedN = reqN / (1 – decimalRate);
// Since we are dealing with people, we must round UP to the nearest whole person (ceiling)
var finalRecruitmentGoal = Math.ceil(adjustedN);
var bufferSize = finalRecruitmentGoal – reqN;
// Update UI
document.getElementById('displayRequired').innerText = reqN;
document.getElementById('displayBuffer').innerText = "+" + bufferSize;
document.getElementById('displayTotal').innerText = finalRecruitmentGoal + " Participants";
document.getElementById('textTotal').innerText = finalRecruitmentGoal;
document.getElementById('textFinal').innerText = reqN;
document.getElementById('resultBox').style.display = 'block';
}
Adjusting Sample Size for Anticipated Dropouts
In research methodology, particularly in longitudinal studies, clinical trials, and surveys, participant attrition (dropout) is a common challenge. If researchers only recruit the exact sample size indicated by a power analysis, any loss of participants during the study will reduce the statistical power, potentially rendering the results non-significant or invalid.
This Dropout Rate Sample Size Calculator helps researchers determine the "Enrollment Goal"—the total number of participants that must be recruited initially to ensure that the final dataset contains enough valid subjects after attrition occurs.
Why is this calculation necessary?
Statistical power analysis determines the minimum sample size ($N$) required to detect an effect of a certain size with a specific confidence level. This is often referred to as the "Analytic Sample Size." However, during the course of a study, participants may:
Withdraw consent.
Be lost to follow-up (move away, stop responding).
Fail to adhere to the protocol (e.g., not taking medication).
To protect the integrity of the study, the recruitment target must be inflated to account for these anticipated losses.
The Adjustment Formula
The calculation used to adjust for attrition is derived from the inverse of the retention rate. It is calculated as follows:
$N_{recruited} = \frac{N_{required}}{(1 – R)}$
Where:
$N_{recruited}$: The total number of participants to enroll (Recruitment Goal).
$N_{required}$: The sample size required for statistical power (from G*Power or similar tools).
$R$: The anticipated dropout rate expressed as a decimal (e.g., 20% = 0.20).
Example Calculation
Imagine you are conducting a randomized control trial. Your power analysis suggests you need 80 completed participants to achieve 80% power at an alpha of 0.05. Based on previous literature in your field, you anticipate an attrition rate of 15%.
Since you cannot recruit a fraction of a person, you round up to the next whole number. You must recruit 95 participants to ensure you finish with at least 80 evaluable subjects.
How to Estimate the Dropout Rate
Estimating $R$ accurately is critical. If you overestimate, you waste resources recruiting unnecessary subjects. If you underestimate, your study may be underpowered. To estimate the rate:
Review Literature: Look for similar studies in your specific field and population to see their reported attrition rates.
Pilot Studies: Conduct a small pilot study to gauge participant retention.
Study Duration: Longer studies typically have higher dropout rates than shorter ones.
Invasiveness: Studies requiring invasive procedures or significant time commitments often see higher attrition.
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"mainEntity": [{
"@type": "Question",
"name": "How do you calculate sample size with dropout rate?",
"acceptedAnswer": {
"@type": "Answer",
"text": "To calculate the adjusted sample size, divide your required statistical sample size by (1 minus the anticipated dropout rate). For example, if you need 100 participants and expect 20% dropout, the calculation is 100 / (1 – 0.20) = 125."
}
}, {
"@type": "Question",
"name": "Why do we adjust sample size for non-response?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Adjusting for non-response or dropout ensures that the final dataset has enough data points to maintain statistical power. Without this adjustment, the study may fail to detect significant effects due to a smaller-than-planned sample size."
}
}, {
"@type": "Question",
"name": "What is a typical dropout rate for clinical trials?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Dropout rates vary widely by field and study design, but 15% to 20% is often used as a standard conservative estimate in clinical trials unless specific data suggests otherwise."
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