<form id="cp_calculatedfieldsf_pform_1">
Cohort Study Power Calculator
Enter any 3 values to calculate the missing variable
<label for="fieldname1">Power</label>
<input type="number" id="fieldname1" style="width: 100%;">
<label for="fieldname2">Effect Size</label>
<input type="number" id="fieldname2" style="width: 100%;">
<label for="fieldname3">Standard Deviation</label>
<input type="number" id="fieldname3" style="width: 100%;">
<label for="fieldname4">Sample Size</label>
<input type="number" id="fieldname4" style="width: 100%;">
<button type="button" onclick="calculate()" style="background-color: #0093da; color: white; width: 49%;">Calculate</button>
<button type="button" onclick="resetFields()" style="background-color: #0093da; color: white; width: 49%;">Reset</button>
<script>
function calculate()
const power = parseFloat(document.getElementById("fieldname1").value);
const effectSize = parseFloat(document.getElementById("fieldname2").value);
const standardDeviation = parseFloat(document.getElementById("fieldname3").value);
const sampleSize = parseFloat(document.getElementById("fieldname4").value);
const zAlpha = 1.96; // Z value for 95% confidence level
if (isNaN(power) && !isNaN(effectSize) && !isNaN(standardDeviation) && !isNaN(sampleSize))
const zBeta = (effectSize / standardDeviation) * Math.sqrt(sampleSize / 2) - zAlpha;
document.getElementById("fieldname1").value = 1 - normalCDF(zBeta);
else if (!isNaN(power) && isNaN(effectSize) && !isNaN(standardDeviation) && !isNaN(sampleSize))
const zBeta = normalInverseCDF(1 - power);
document.getElementById("fieldname2").value = (zBeta + zAlpha) * standardDeviation / Math.sqrt(sampleSize / 2);
else if (!isNaN(power) && !isNaN(effectSize) && isNaN(standardDeviation) && !isNaN(sampleSize))
const zBeta = normalInverseCDF(1 - power);
document.getElementById("fieldname3").value = (zBeta + zAlpha) * Math.sqrt(sampleSize / 2) / effectSize;
else if (!isNaN(power) && !isNaN(effectSize) && !isNaN(standardDeviation) && isNaN(sampleSize))
const zBeta = normalInverseCDF(1 - power);
document.getElementById("fieldname4").value = 2 * Math.pow((zBeta + zAlpha) * standardDeviation / effectSize, 2);
else
alert("Please leave one field empty to calculate its value.");
function resetFields()
document.getElementById("fieldname1").value =
;
document.getElementById("fieldname2").value = ;
document.getElementById("fieldname3").value =
;
document.getElementById("fieldname4").value = ;
function normalCDF(x)
return (1.0 + Math.erf(x / Math.sqrt(2.0))) / 2.0;
function normalInverseCDF(p)
function rationalApproximation(t)
const c = [2.515517, 0.802853, 0.010328];
const d = [1.432788, 0.189269, 0.001308];
return t - ((c[2] * t + c[1]) * t + c[0]) / (((d[2] * t + d[1]) * t + d[0]) * t + 1.0);
</script>
</form>
Enter the power, effect size, standard deviation, and sample size into the calculator to determine the missing variable for a cohort study.
Cohort Study Power Formula
The following formula is used to calculate the power of a cohort study.
zbeta = (ES / SD) \cdot \sqrtn / 2 - z_\alpha
- zβ is the Z value for the power
- ES is the effect size
- SD is the standard deviation
- n is the sample size
- zα is the Z value for the confidence level (typically 1.96 for 95% confidence)
To calculate the power of a cohort study, use the formula above. The Z value for the power (zβ) can be found using the normal cumulative distribution function (CDF) and its inverse.
What is a Cohort Study?
A cohort study is a type of observational study where a group of individuals (the cohort) is followed over time to observe outcomes such as the development of diseases. The cohort is typically divided into subgroups based on exposure to certain risk factors, and the incidence of outcomes is compared between these subgroups. Cohort studies are valuable in epidemiology for identifying and quantifying the relationship between risk factors and health outcomes. chat Calculator can be prospective (following participants forward in time) or retrospective (looking back at historical data).
How to Calculate Cohort Study Power?
The following steps outline how to calculate the power of a cohort study.
- First, determine the effect size (ES).
- Next, determine the standard deviation (SD).
- Next, determine the sample size (n).
- Finally, calculate the power using the formula and the Z values for the confidence level and power.
- After inserting the values and calculating the result, check your answer with the calculator above.
Example Problem :
Use the following variables as an example problem to test your knowledge.
Effect Size (ES) = 0.5
Standard Deviation (SD) = 1.2
Sample Size (n) = 100