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

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1462
Author(s):  
José Antonio Roldán-Nofuentes ◽  
Saad Bouh Regad
Keyword(s):  
Sample Size ◽  
Confidence Intervals ◽  
Diagnostic Test ◽  
Diagnostic Tests ◽  
Medical Test ◽  
Predictive Values ◽  
General Rules ◽  
Clinical Accuracy ◽  
The Difference ◽  
Paired Design

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.

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.

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.

Precision of sensitivity estimations in diagnostic test evaluations. Power functions for comparisons of sensitivities of two tests.

1985 ◽  
Vol 31 (4) ◽  
pp. 574-580 ◽  
Author(s):  
K Linnet
Keyword(s):  
Confidence Interval ◽  
Sample Size ◽  
Diagnostic Test ◽  
Diagnostic Tests ◽  
Sample Sizes ◽  
Power Functions ◽  
Gaussian Distributions ◽  
Sensitivity Estimate

Abstract The precision of estimates of the sensitivity of diagnostic tests is evaluated. "Sensitivity" is defined as the fraction of diseased subjects with test values exceeding the 0.975-fractile of the distribution of control values. An estimate of the sensitivity is subject to sample variation because of variation of both control observations and patient observations. If gaussian distributions are assumed, the 0.95-confidence interval for a sensitivity estimate is up to +/- 0.15 for a sample of 100 controls and 100 patients. For the same sample size, minimum differences of 0.08 to 0.32 of sensitivities of two tests are established as significant with a power of 0.90. For some published diagnostic test evaluations the median sample sizes for controls and patients were 63 and 33, respectively. I show that, to obtain a reasonable precision of sensitivity estimates and a reasonable power when two tests are being compared, the number of samples should in general be considerably larger.

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 COMPARACION DEL PROMEDIO Y LA MEDIANA COMO ESTIMADORES DE LA MEDIA PARA MUESTRAS NORMALES DEPENDIENDO DEL TAMAÑO DE MUESTRA

2017 ◽  
Vol 23 (2) ◽  
pp. 33
Author(s):  
José W. Camero Jiménez ◽  
Jahaziel G. Ponce Sánchez
Keyword(s):  
Confidence Interval ◽  
Sample Size ◽  
Normal Distribution ◽  
Confidence Intervals ◽  
Hypothesis Tests ◽  
Sample Mean ◽  
Palabras Clave ◽  
The Mean ◽  
The Difference

Actualmente los métodos para estimar la media son los basados en el intervalo de confianza del promedio o media muestral. Este trabajo pretende ayudar a escoger el estimador (promedio o mediana) a usar dependiendo del tamaño de muestra. Para esto se han generado, vía simulación en excel, muestras con distribución normal y sus intervalos de confianza para ambos estimadores, y mediante pruebas de hipótesis para la diferencia de proporciones se demostrará que método es mejor dependiendo del tamaño de muestra. Palabras clave.-Tamaño de muestra, Intervalo de confianza, Promedio, Mediana. ABSTRACTCurrently the methods for estimating the mean are those based on the confidence interval of the average or sample mean. This paper aims to help you choose the estimator (average or median) to use depending on the sample size. For this we have generated, via simulation in EXCEL, samples with normal distribution and confidence intervals for both estimators, and by hypothesis tests for the difference of proportions show that method is better depending on the sample size. Keywords.-Sampling size, Confidence interval, Average, Median.

scholarly journals Determinazione della numerosità campionaria

2020 ◽  
Vol 32 (1) ◽  
pp. 26-29
Author(s):  
Anteo Di Napoli ◽  
Franco Francesco
Keyword(s):  
Clinical Trial ◽  
Sample Size ◽  
Epidemiological Study ◽  
Diagnostic Test ◽  
Sampling Method ◽  
Scientific Progress ◽  
Point Of View ◽  
Ethical Point ◽  
The Difference ◽  
Adequate Sample Size

Determining the adequate sample size for a clinical trial is crucial in the design of an epidemiological study. In fact the question about the number of subjects need to study is common for clinical investigators, because a correct sample size is fundamental to obtain reliable findings. The larger the sample size under study, the greater the chance of detecting, as statistically significant, a clinically important effect it exists. This issue is related to the precision and the power of a study in measuring the difference between treatments being studied, the validity and accuracy of a diagnostic test, the occurrence of a disease. However, conducting a study with an adequate sample size is fundamental not only in statistical terms, but also from an ethical point of view. It is unjustifiable to expose patients to the risks of a research if the study has not the necessary preconditions to obtain findings useful to substantial scientific progress. Calculating sample size depends on several issues, such as the type of sampling method, the type of the study, the desired power and level of confidence fixed for the study. The aim of this article is to summarize the criterions for defining the appropriate sample size and to present some examples of methods for its calculating. (Epidemiology_statistics)

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