Confidence Intervals and Sample Size to Compare the Predictive Values of Two Diagnostic Tests

A binary diagnostic test is a medical test that is applied to an individual in order to determine the presence or the absence of a certain disease and whose result can be positive or negative. A positive result indicates the presence of the disease, and a negative result indicates the absence. Positive and negative predictive values represent the accuracy of a binary diagnostic test when it is applied to a cohort of individuals, and they are measures of the clinical accuracy of the binary diagnostic test. In this manuscript, we study the comparison of the positive (negative) predictive values of two binary diagnostic tests subject to a paired design through confidence intervals. We have studied confidence intervals for the difference and for the ratio of the two positive (negative) predictive values. Simulation experiments have been carried out to study the asymptotic behavior of the confidence intervals, giving some general rules for application. We also study a method to calculate the sample size to compare the parameters using confidence intervals. We have written a program in R to solve the problems studied in this manuscript. The results have been applied to the diagnosis of colorectal cancer.

Automated summary

This manuscript focuses on comparing the positive (negative) predictive values of two binary diagnostic tests through confidence intervals in a paired design. Simulation experiments have been conducted to study the behavior of the confidence intervals asymptotically, providing general rules for application. The manuscript also discusses a method for calculating sample size for comparing parameters using confidence intervals, with results applied to the diagnosis of colorectal cancer.