Abstract
BackgroundThe comparison of the performance of two binary diagnostic tests is an important topic in Clinical Medicine. The most frequent type of sample design to compare two binary diagnostic tests is the paired design. This design consists of applying the two binary diagnostic tests to all of the individuals in a random sample, where the disease status of each individual is known through the application of a gold standard. This article presents an R program to compare parameters of two binary tests subject to a paired design.ResultsThe “compbdt” program estimates the sensitivity and the specificity, the likelihood ratios and the predictive values of each diagnostic test applying the confidence intervals with the best asymptotic performance. The program compares the sensitivities and specificities of the two diagnostic tests simultaneously, as well as the likelihood ratios and the predictive values, applying the global hypothesis tests with the best performance in terms of type I error and power. When the global hypothesis test is significant, the causes of the significance are investigated solving the individual hypothesis tests and applying the multiple comparison method of Holm. The most optimal confidence intervals are also calculated for the difference or ratio between the respective parameters. Based on the data observed in the sample, the program also estimates the probability of making a type II error if the null hypothesis is not rejected, or estimates the power if the if the alternative hypothesis is accepted. The “compbdt” program provides all the necessary results so that the researcher can easily interpret them. The estimation of the probability of making a type II error allows the researcher to decide about the reliability of the null hypothesis when this hypothesis is not rejected. The “compbdt” program has been applied to a real example on the diagnosis of coronary artery disease.ConclusionsThe “compbdt” program is one which is easy to use and allows the researcher to compare the most important parameters of two binary tests subject to a paired design. The “compbdt” program is available as supplementary material.
Highlights
The comparison of the performance of two binary diagnostic tests is an important topic in Clinical Medicine
This article presents a program called “compbdt” (Comparison of two Binary Diagnostic Tests) written in R [3] which allows us to estimate and compare the performance of two diagnostic tests subject to a paired design applying the statistical methods with the best asymptotic performance, i.e. for the confidence intervals we used the intervals that have a better coverage and average width, and for the hypothesis tests we used the methods that have the best behaviour in terms of type I error and power
The results obtained comparing the sensitivities and specificities are recorded in the file “Results_Comparison_Accuracies.txt”, those obtained when comparing the Likelihood ratio (LR) are recorded in the file “Results_Comparison_LRs.txt”, and those obtained when comparing the Predictive value (PV) are recorded in the file “Results_Comparison_PVs.txt”
Summary
The “compbdt” program has been applied to the study of Weiner et al [19] on the diagnosis of coronary artery disease, which is a classic example to illustrate statistical methods to compare parameters of two diagnostic tests. Applying the global hypothesis test (to an alpha error of 5%), we reject the hypothesis H0: (PLR1 = PLR2 and NLR1 = NLR2). Applying the global hypothesis test (to an alpha error of 5%), we reject the hypothesis H0: (PPV1 = PPV2 and NPV1 = NPV2). Applying the Holm method (to an alpha error of 5%), we do not reject the hypothesis H0: PPV1 = PPV2 and we reject the hypothesis H0: NPV1 = NPV2. For individual hypothesis tests that are declared significant, it is indicated which is the diagnostic test for which the parameter is greater, calculating the corresponding confidence interval. The conclusions obtained are similar to those obtained with the “compbdt” program, this program uses methods with better asymptotic behaviour
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