Overview

SampleSizeR is an R package for sample size determination in epidemiological, clinical, and diagnostic research. The package provides easy-to-use functions for commonly used study designs while automatically accounting for finite population correction, design effect, response rate, and anticipated dropout.

The package returns standardized S3 objects with methods for printing, summarizing, plotting, and exporting results.

Installation

From CRAN

install.packages("SampleSizeR")

Development version

install.packages("remotes")
remotes::install_github("vinodhpmd/SampleSizeR")

Implemented Study Designs

Epidemiological Studies

Cross-sectional prevalence studies
Cluster prevalence studies
Cohort studies
Case-control studies

Clinical Trials

Parallel clinical trials
Superiority trials
Non-inferiority trials
Equivalence trials

Diagnostic Studies

Diagnostic sensitivity
Diagnostic specificity
ROC AUC studies
Diagnostic agreement studies

Features

Confidence interval based calculations
Power-based sample size estimation
Finite population correction
Cluster design effect adjustment
Response rate adjustment
Dropout adjustment
Publication-ready summaries
Built-in plotting methods
Data frame conversion

Example 1: Prevalence Study

library(SampleSizeR)
result <- ss_prevalence(
  prevalence = 0.20,
  precision = 0.05,
  conf.level = 0.95
)
print(result)
summary(result)
plot(result)

Example 2: Cohort Study

cohort <- ss_cohort(
  p0 = 0.15,
  risk.ratio = 2,
  power = 0.80
)
summary(cohort)

Example 3: Diagnostic Sensitivity

diag <- ss_diagnostic_sensitivity(
  sensitivity = 0.90,
  prevalence = 0.20,
  precision = 0.05
)
diag

Output

All functions return a SampleSizeR object.

class(result)
[1] "SampleSizeR"

Supported methods

print(result)
summary(result)
plot(result)
as.data.frame(result)

Documentation

help(package = "SampleSizeR")

?ss_prevalence

Citation

If you use SampleSizeR in published research, please cite:

Vinodh Kumar OR (2026).

SampleSizeR: Sample Size Determination for Epidemiological, Clinical and Diagnostic Studies.