Comparing diagnostic tests: test of hypothesis for likelihood ratios

Dolgun, N. Anıl; Gozukara, Harika; Karaağaoğlu, Ergün

doi:10.1080/00949655.2010.531480

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Yu et al [4] proposed a new interval, based on a modification of the Wilson interval, to estimate a binomial proportion, demonstrating that this interval shows a better asymptotic performance than the rest of the existing intervals. For the sensitivity of each diagnostic test, the estimators are 11 10 1…”

Section: Estimation Of the Parametersmentioning

confidence: 99%

Compbdt: an R program to compare two binary diagnostic tests subject to a paired design

Roldán-Nofuentes¹

2020

Preprint

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Background: The comparison of the effectiveness 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. Results: The “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. Conclusions: The “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.

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Section: Estimation Of the Parametersmentioning

confidence: 99%

Compbdt: an R program to compare two binary diagnostic tests subject to a paired design

Roldán-Nofuentes¹

2020

Preprint

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“…The hypothesis tests proposed by Roldán-Nofuentes and Luna (2007) are based on the logarithmic transformation of the ratio of the positive (negative) LRs, and therefore by inverting the test statistics of the individual tests, confidence intervals are obtained for the ratio of the two LRs (in Section 3.2 we summarize this method). Dolgun et al (2012) extended the method of Leisenring and Pepe (1998) to compare the LRs simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…comparison of the positive (negative) LRs of two BDTs subject to a paired design is a topic which has not been widely studied in Statistical literature and consists of the comparison of two relative risks subject to the same type of design. The previous studies(Leisenring and Pepe (1998) andPepe (2003), Roldán-Nofuentes and Luna (2007),Dolgun et al (2012) focused mainly on the study of hypothesis tests to compare the positive (negative) LRs of the two BDTs. The comparison of the positive (negative)LRs through CIs has been the object of the very little research, and the studies that have been published byPepe (2003) andRoldán-Nofuentes and Luna (2007) have focused on proposing CIs without dealing with this question in more depth.…”

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confidence: 99%

Comparison of the predictive values of two binary diagnostic tests subject to a case-control sampling

Roldán-Nofuentes

Sidaty-Regad

2019

Preprint

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Background: The main parameters to compare binary tests are the sensitivity and the specificity. Case-control sampling to compare two binary tests is frequent in clinical practice. This design consists of applying the two binary tests to all of the individuals in a sample of those who have the disease and in another sample of those who do not have the disease. In this design, the sensitivities (specificities) are compared from the case (control) sample applying the McNemar’s test. Other parameters of a binary test are the predictive values. The predictive values of a binary test represent the clinical accuracy of a binary test when it is applied to the individuals in a population with a determined disease prevalence. Methods: This article studies the comparison of the predictive values of two diagnostic tests subject to a case-control sampling. A global hypothesis test, based on the chi-square distribution, is proposed to compare the predictive values simultaneously. The comparison of the predictive values is also studied individually. The hypothesis tests studied require knowledge of an estimation of the disease prevalence. Results: Simulation experiments were carried out to study the type I errors and the powers of the hypothesis tests, as well as to study the effect of a misspecification of the prevalence on the behaviour of the hypothesis tests and on the estimators of the predictive values. The results obtained were applied to an example on the diagnosis of the Human African Trypanosomiasis. Conclusions: A method has been proposed to compare the predictive values of two diagnostic tests subject to a case-control sampling. This method consists in: 1) Simultaneously comparing the predictive values applying the global hypothesis test based on the chi-square distribution to an error alpha; 2) If the global test is not significant, then the equality of the predictive values is not rejected. If the global test is significant to an error alpha , then the causes of the significance are studied solving the individual hypothesis tests and applying Bonferroni’s method (or Holm’s method) to an error alpha. Keywords: Binary diagnostic test, Chi-square distribution, Positive and negative predictive values.

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“…solving the global hypothesis test H 0 : (PLR 1 = PLR 2 and NLR 1 = NLR 2 ) vs H 1 : (PLR 1 ≠ PLR 2 and/or NLR 1 ≠ NLR 2 ), applying the maximum likelihood method. Dolgun et al[10] have extended the method of Leisenring and Pepe to compare the LRs simultaneously. The test statistics of the individual hypotheses tests of Pepe and the test statistics of the individual hypotheses tests of Roldán-Nofuentes and Luna have a very similar asymptotic behaviour.…”

mentioning

confidence: 99%

Compbdt: an R program to compare two binary diagnostic tests subject to a paired design

Roldán-Nofuentes

2020

BMC Med Res Methodol

View full text Add to dashboard Cite

Background: The 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. Results: The "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. Conclusions: The "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.

show abstract

Comparing diagnostic tests: test of hypothesis for likelihood ratios

Cited by 3 publications

References 17 publications

Compbdt: an R program to compare two binary diagnostic tests subject to a paired design

Compbdt: an R program to compare two binary diagnostic tests subject to a paired design

Comparison of the predictive values of two binary diagnostic tests subject to a case-control sampling

Compbdt: an R program to compare two binary diagnostic tests subject to a paired design

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