Sample Size Calculation
Sample size calculation determines the number of participants needed to detect a clinically meaningful effect with adequate statistical power.
1Detailed Explanation
A priori sample size calculation is a CONSORT requirement for RCTs and is expected for most comparative studies. Key inputs include: expected effect size (from pilot data or prior literature), desired power (typically 80-90%), significance level (alpha, typically 0.05), and expected attrition/dropout. Calculations differ by study design: RCTs use t-tests, chi-square, or ANOVA; survival analyses use log-rank tests; correlation studies use Fisher's z transformation. Software (G*Power, nQuery, PASS, R packages) facilitates calculations. Sensitivity analyses showing sample size for alternative effect sizes are informative. Underpowered studies cannot reliably answer their research question.
2Examples
- A.N = 200 per arm needed for 90% power to detect HR = 0.7 at alpha = 0.05
- B.Allowing for 20% dropout: enroll 125 per arm to retain 100 per arm
3Why It Matters in Research
Sample size justification is mandatory for RCT publication and is expected for most hypothesis-testing studies. Journals increasingly scrutinize sample size calculations.
4Related Terms
Master Medical Writing with SciPaperX
Get AI-powered assistance with Sample Size Calculation and all aspects of medical manuscript preparation.