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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.
Load the package:
library(SampleSizeDiagnostics)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 width of the confidence interval: 0.1
Confidence interval level: 0.95result <- SampleSizeDiagnostics(sn = 0.9, sp = 0.85, p = 0.2, w = 0.1, CI = 0.95)
print(result)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)The function returns a data frame containing the calculated sample sizes and input parameters. Here is a breakdown of the output:
Precision: Desired width of the confidence interval
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 levelThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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