Choosing Sample Size for Evaluating a Diagnostic Test

Introduction

The SampleSizeDiagnostics package provides a function for calculating the sample size needed for evaluating a diagnostic test based on sensitivity, specificity, prevalence, and desired precision.

In this vignette, we will demonstrate how to use the SampleSizeDiagnostics function to calculate the necessary sample size for different scenarios.

Example Usage

Load the package:

library(SampleSizeDiagnostics)

Basic Example

Let’s calculate the sample size needed for a diagnostic test with the following parameters:

Sensitivity: 0.9
Specificity: 0.85
Prevalence: 0.2
Desired half-width (margin of error) of the confidence interval: 0.1
  (This creates a total CI width of 0.2: estimate ±0.1)
Confidence interval level: 0.95

result <- SampleSizeDiagnostics(sn = 0.9, sp = 0.85, p = 0.2, w = 0.1, CI = 0.95)
print(result)

Varying the Confidence Interval

You can also calculate the sample size with a different confidence interval level, for example, 0.9:

result <- SampleSizeDiagnostics(sn = 0.9, sp = 0.85, p = 0.2, w = 0.1, CI = 0.9)
print(result)

Interpretation of Results

The function returns a data frame containing the calculated sample sizes and input parameters. Here is a breakdown of the output:

Precision: Desired half-width of the CI (margin of error). Total width = 2 × Precision
Sensitivity: Sensitivity of the diagnostic test
Specificity: Specificity of the diagnostic test
Prevalence: Prevalence of the disease
N1: Sample size for sensitivity
N2: Sample size for specificity
Total_Subjects: Total sample size needed (maximum of N1 and N2)
CI: Confidence interval level

Understanding the Width Parameter

IMPORTANT: The parameter w represents the half-width or margin of error of the confidence interval, NOT the total width. This follows Buderer (1996).