Epidemiology & Technology

Blog Archive

Sample size for Paired T test

Simple pre-post designs where the outcome assessed is continuous. Using pwrss package Output References
April 4, 2025

Precision based Sample Size Calculation using precise in R

https://joss.theoj.org/papers/10.21105/joss.03118 https://ctu-bern.github.io/presize/ Verbatim From the article - There are two approaches for each measure. Proportion Shiny Webapp
March 30, 2025

Recommended Precision for Sample Size of Prevalence

Often people are confused about the precision that should be used when estimating sample size for prevalence studies. Some guidance here From: Sample size calculation for prevalence studies using Scalex and ScalaR calculators  Expected PrevalenceRecommended PrecisionLarge ScaleSmall ScalePreliminary StudyRemark110 to 90%2 ~ 3%4 ~ 5% > 5% 2 < 10%0.25*EP0.50*EP > 0.50*EPCannot be equal to EP or larger e.g. 4%1%2% > 2%Cannot be 4% or
March 30, 2025

Sample size for Prevalence in R using epiR

Simple Random Sampling Expected prevalence 50%, relative error 10%, Finite population correction - None Expected prevalence 50%, absolute error 10%, Finite population correction - None One Stage Cluster Sampling Estimate number of clusters when Expected prevalence = 50% , relative error 10%, cluster size 75, ICC 0.2, 95% Confidence. Entire
March 30, 2025

Sample Size for Correlations

To assess sample size for an expected Pearsons correlation ρ0 between two continuous variables n = ([z1 - α + z1 − β]2/z02) + 3 Where z0 = 0.5 (ln [1 + ρ0] − ln [1 − ρ0]) This tests the hypothesis, H0: ρ = 0 versus H1: ρ = ρ0 > 0 Estimation
March 30, 2025