scholarly journals Comparison of the predictive values of diagnostic tests subject to a case-control sampling with application to the diagnosis of Human African Trypanosomiasis

2019 ◽  
Author(s):  
José Antonio Roldán-Nofuentes ◽  
Saad Bouh Sidaty-Regad
Keyword(s):  
Diagnostic Test ◽  
Diagnostic Tests ◽  
Human African Trypanosomiasis ◽  
Control Sample ◽  
Case Control ◽  
Disease Prevalence ◽  
Type I ◽  
African Trypanosomiasis ◽  
Hypothesis Tests ◽  
Predictive Values

AbstractCase-control sampling to compare the accuracy of two binary diagnostic tests is frequent in clinical practice. This type of sampling consists of applying the two diagnostic 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 sampling, the sensitivities are compared from the case sample applying the McNemar’s test, and the specificities from the control sample. Other parameters of binary tests are the positive and negative predictive values. The predictive values of a diagnostic test represent the clinical accuracy of a binary diagnostic test when it is applied to the individuals in a population with a determined disease prevalence. 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 the disease prevalence. Simulation experiments were carried out to study the type I errors and the powers of the hypothesis tests proposed, as well as to study the effect of a misspecification of the prevalence on the asymptotic behavior of the hypothesis tests and on the estimators of the predictive values. The results obtained were applied to a real example on the diagnosis of the Human African Trypanosomiasis. The model proposed was extended to the situation in which there are more than two diagnostic tests.

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

2019 ◽  
Author(s):  
Jose Antonio Roldán-Nofuentes ◽  
Saad Bouh Sidaty-Regad
Keyword(s):  
Diagnostic Tests ◽  
Hypothesis Test ◽  
Control Sample ◽  
Case Control ◽  
Disease Prevalence ◽  
Type I ◽  
Hypothesis Tests ◽  
Chi Square ◽  
Global Test ◽  
Predictive Values

Abstract 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.

scholarly journals Global Hypothesis Test to Compare the Predictive Values of Diagnostic Tests Subject to a Case-Control Design

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 658
Author(s):  
Saad Bouh Regad ◽  
José Antonio Roldán-Nofuentes
Keyword(s):  
Diagnostic Tests ◽  
Hypothesis Test ◽  
Control Design ◽  
Case Control ◽  
Alternative Methods ◽  
Manuscript Studies ◽  
Type I ◽  
Hypothesis Tests ◽  
Chi Square ◽  
Predictive Values

Use of a case-control design to compare the accuracy of two binary diagnostic tests is frequent in clinical practice. This design consists of applying the two diagnostic 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. This manuscript studies the comparison of the predictive values of two diagnostic tests subject to a case-control design. A global hypothesis test, based on the chi-square distribution, is proposed to compare the predictive values simultaneously, as well as other alternative methods. The hypothesis tests studied require knowing the prevalence of the disease. Simulation experiments were carried out to study the type I errors and the powers of the hypothesis tests proposed, as well as to study the effect of a misspecification of the prevalence on the asymptotic behavior of the hypothesis tests and on the estimators of the predictive values. The proposed global hypothesis test was extended to the situation in which there are more than two diagnostic tests. The results have been applied to the diagnosis of coronary disease.

scholarly journals Disease Prevalence Matters: Challenge for SARS-CoV-2 Testing

Antibodies ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 50
Author(s):  
Chin-Shern Lau ◽  
Tar-Choon Aw
Keyword(s):  
Diagnostic Test ◽  
Sensitivity And Specificity ◽  
False Positive ◽  
Disease Prevalence ◽  
Test Results ◽  
Community Screening ◽  
Predictive Values ◽  
Low Prevalence

While sensitivity and specificity are important characteristics for any diagnostic test, the influence of prevalence is equally, if not more, important when such tests are used in community screening. We review the concepts of positive/negative predictive values (PPV/NPV) and how disease prevalence affects false positive/negative rates. In low-prevalence situations, the PPV decreases drastically. We demonstrate how using two tests in an orthogonal fashion can be especially beneficial in low-prevalence settings and greatly improve the PPV of the diagnostic test results.

Clinical Epidemiology

2016 ◽  
pp. 285-290
Author(s):  
Scott C. Litin ◽  
John B. Bundrick
Keyword(s):  
Diagnostic Test ◽  
Diagnostic Tests ◽  
Clinical Decision Making ◽  
Clinical Epidemiology ◽  
Clinical Decision ◽  
Test Results ◽  
Specific Patient ◽  
Predictive Values ◽  
Test Sensitivity ◽  
Sensitivity Specificity

Diagnostic tests are tools that either increase or decrease the likelihood of disease. The sensitivity, specificity, and predictive values of normal and abnormal test results can be calculated with even a limited amount of information. Some physicians prefer interpreting diagnostic test results by using the likelihood ratio. This ratio takes properties of a diagnostic test (sensitivity and specificity) and makes them more helpful in clinical decision making. It helps the clinician determine the probability of disease in a specific patient after a diagnostic test has been performed.

scholarly journals Passive surveillance of human African trypanosomiasis in Côte d’Ivoire: Understanding prevalence, clinical symptoms and signs, and diagnostic test characteristics

2021 ◽  
Vol 15 (8) ◽  
pp. e0009656
Author(s):  
Minayégninrin Koné ◽  
Dramane Kaba ◽  
Jacques Kaboré ◽  
Lian Francesca Thomas ◽  
Laura Cristina Falzon ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Cote D'ivoire ◽  
Predictive Value ◽  
Diagnostic Tests ◽  
Human African Trypanosomiasis ◽  
Clinical Symptoms ◽  
Dried Blood Spots ◽  
African Trypanosomiasis ◽  
Blood Spots ◽  
Symptoms And Signs

Background Little is known about the diagnostic performance of rapid diagnostic tests (RDTs) for passive screening of human African trypanosomiasis (HAT) in Côte d’Ivoire. We determined HAT prevalence among clinical suspects, identified clinical symptoms and signs associated with HAT RDT positivity, and assessed the diagnostic tests’ specificity, positive predictive value and agreement. Methods Clinical suspects were screened with SD Bioline HAT, HAT Sero-K-Set and rHAT Sero-Strip. Seropositives were parasitologically examined, and their dried blood spots tested in trypanolysis, ELISA/Tbg, m18S-qPCR and LAMP. The HAT prevalence in the study population was calculated based on RDT positivity followed by parasitological confirmation. The association between clinical symptoms and signs and RDT positivity was determined using multivariable logistic regression. The tests’ Positive Predictive Value (PPV), specificity and agreement were determined. Results Over 29 months, 3433 clinical suspects were tested. The RDT positivity rate was 2.83%, HAT prevalence 0.06%. Individuals with sleep disturbances (p<0.001), motor disorders (p = 0.002), convulsions (p = 0.02), severe weight loss (p = 0.02) or psychiatric problems (p = 0.04) had an increased odds (odds ratios 1.7–4.6) of being HAT RDT seropositive. Specificities ranged between 97.8%-99.6% for individual RDTs, and 93.3–98.9% for subsequent tests on dried blood spots. The PPV of the individual RDTs was below 14.3% (CI 2–43), increased to 33.3% (CI 4–78) for serial RDT combinations, and reached 67% for LAMP and ELISA/Tbg on RDT positives. Agreement between diagnostic tests was poor to moderate (Kappa ≤ 0.60), except for LAMP and ELISA/Tbg (Kappa = 0.66). Conclusion Identification of five key clinical symptoms and signs may simplify referral for HAT RDT screening. The results confirm the appropriateness of the diagnostic algorithm presently applied, with screening by SD Bioline HAT or HAT Sero-K-Set, supplemented with trypanolysis. ELISA/Tbg could replace trypanolysis and is simpler to perform. Trial registration ClinicalTrials.gov NCT03356665.

scholarly journals Human African trypanosomiasis amongst urban residents in Kinshasa: a case-control study

2004 ◽  
Vol 9 (8) ◽  
pp. 869-875 ◽  
Author(s):  
J. Robays ◽  
A. Ebeja Kadima ◽  
P. Lutumba ◽  
C. Miaka mia Bilenge ◽  
V. Kande Betu Ku Mesu ◽  
...  
Keyword(s):  
Human African Trypanosomiasis ◽  
Case Control Study ◽  
Case Control ◽  
Urban Residents ◽  
African Trypanosomiasis ◽  
Control Study

A novel and accurate diagnostic test for human African trypanosomiasis

The Lancet ◽  
2004 ◽  
Vol 363 (9418) ◽  
pp. 1358-1363 ◽  
Author(s):  
Marios C Papadopoulos ◽  
Paulo M Abel ◽  
Dan Agranoff ◽  
August Stich ◽  
Edward Tarelli ◽  
...  
Keyword(s):  
Diagnostic Test ◽  
Human African Trypanosomiasis ◽  
African Trypanosomiasis

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

2020 ◽  
Author(s):  
Jose Antonio Roldán-Nofuentes
Keyword(s):  
Confidence Intervals ◽  
Diagnostic Tests ◽  
Null Hypothesis ◽  
Type Ii ◽  
Type Ii Error ◽  
Hypothesis Tests ◽  
Likelihood Ratios ◽  
Predictive Values ◽  
R Program ◽  
Paired Design

Abstract 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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