scholarly journals Sensitivity and specificity and their confidence intervals cannot exceed 100%

BMJ ◽  
1999 ◽  
Vol 318 (7177) ◽  
pp. 193-193 ◽  
Author(s):  
J. J Deeks ◽  
D. G Altman
Keyword(s):  
Confidence Intervals ◽  
Sensitivity And Specificity

Exact confidence limits for prevalence of a disease with an imperfect diagnostic test

2010 ◽  
Vol 138 (11) ◽  
pp. 1674-1678 ◽  
Author(s):  
J. REICZIGEL ◽  
J. FÖLDI ◽  
L. ÓZSVÁRI
Keyword(s):  
Confidence Intervals ◽  
Diagnostic Test ◽  
Sensitivity And Specificity ◽  
Simulation Study ◽  
Confidence Level ◽  
Asymptotic Methods ◽  
Real Life ◽  
Disease Status ◽  
Confidence Limits ◽  
Extensive Simulation

SUMMARYEstimation of prevalence of disease, including construction of confidence intervals, is essential in surveys for screening as well as in monitoring disease status. In most analyses of survey data it is implicitly assumed that the diagnostic test has a sensitivity and specificity of 100%. However, this assumption is invalid in most cases. Furthermore, asymptotic methods using the normal distribution as an approximation of the true sampling distribution may not preserve the desired nominal confidence level. Here we proposed exact two-sided confidence intervals for the prevalence of disease, taking into account sensitivity and specificity of the diagnostic test. We illustrated the advantage of the methods with results of an extensive simulation study and real-life examples.

Deep learning algorithm performance in mammography screening: A systematic review and meta-analysis.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13553-e13553
Author(s):  
Rosimeire Aparecida Roela ◽  
Gabriel Vansuita Valente ◽  
Carlos Shimizu ◽  
Rossana Veronica Mendoza Lopez ◽  
Tatiana Cardoso de Mello Tucunduva ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Deep Learning ◽  
Confidence Intervals ◽  
Sensitivity And Specificity ◽  
Mammography Screening ◽  
Learning Algorithm ◽  
Data Extraction ◽  
Meta Analysis ◽  
Paper Analysis

e13553 Background: Mammography interpretation presents some challenges however, better technological approaches have allowed increased accuracy in cancer diagnosis and nowadays, radiologists sensitivity and specificity for mammography screening vary from 84.5 to 90.6 and 89.7 to 92.0%, respectively. Since its introduction in breast image analysis, artificial intelligence (AI) has rapidly improved and deep learning methods are gaining relevance as a companion tool to radiologists. Thus, the aim of this systematic review and meta analysis was to evaluate the sensitivity and specificity of AI deep learning algorithms and radiologists for breast cancer detection through mammography. Methods: A systematic review was performed using PubMed and the words: deep learning or convolutional neural network and mammography or mammogram, from January 2015 to October 2020. All titles and abstracts were doubly checked; duplicate studies and studies in languages other than English were excluded. The remaining complete studies were doubly assessed and those with specificity and sensibility information had data collected. For the meta analysis, studies reporting specificity, sensitivity and confidence intervals were selected. Heterogeneity measures were calculated using Cochran Q test (chi-square test) and the I2 (percentage of variation). Sensitivity and specificity and 95% confidence intervals (CI) values were calculated, using Stata/MP 14.0 for Windows. Results: Among 223 studies, 66 were selected for full paper analysis and 24 were selected for data extraction. Subsequently, only papers evaluating sensitivity, especificity, CI and/or AUC were analyzed. Eleven studies compared AUC using AI with another method and for these studies, a differential AUC was calculated, however no differences were observed: AI vs Reader (n = 3; p = 0.109); AI vs AI (n = 5; p = 0.225); AI vs AI + reader (n = 2; p = 0.180); AI + Reader vs reader (n = 2; p = 0.655); AI vs reader (n > 1) (n = 3; p = 0.102). Some studies had more than one comparison. A meta analysis was performed to evaluate sensitivity and specificity of the methods. Five studies were included in this analysis and a great heterogeneity among them was observed. There were studies evaluating more than one AI algorithm and studies comparing AI with readers alone or in combination with AI. Sensitivity for AI; AI + reader; reader alone, were 76.08; 84.02; 80.91, respectively. Specificity for AI; AI + reader; reader alone, were 96.62; 85.67; 84.89, respectively. Results are shown in the table. Conclusions: Although recent improvements in AI algorithms for breast cancer screening, a delta AUC between comparisons of AI algorithms and readers was not observed.[Table: see text]

A Simple Linear Discrimination Algorithm for AD Patients and Normal Controls

2012 ◽  
Vol 155-156 ◽  
pp. 18-22
Author(s):  
Yun Yi Yan ◽  
Guo Zhang Hu ◽  
Bao Long Guo ◽  
Yu Jie He
Keyword(s):  
Standard Deviation ◽  
Confidence Intervals ◽  
Sensitivity And Specificity ◽  
Classification Accuracy ◽  
Simple Method ◽  
Linear Discrimination ◽  
Significant Difference ◽  
The Mean ◽  
Normal Controls ◽  
Discrimination Method

One simple but effective discrimination method was presented in this paper to separate AD from normal controls. After detecting the thickness of cortex with highly significant difference, the mean and standard deviation of these vertices are computed to construct confidence intervals. We introduced one relax coefficients to control the width of intervals and by experiments the coefficients was optimized. Experiments results showed that using this simple method, the classification accuracy, sensitivity and specificity of Alzheimer’s disease versus normal controls could be as high as 85%, 88.89% and 93.84% respectively.

scholarly journals Estimating Prevalence Using an Imperfect Test

2011 ◽  
Vol 2011 ◽  
pp. 1-5 ◽  
Author(s):  
Peter J. Diggle
Keyword(s):  
Confidence Intervals ◽  
Diagnostic Test ◽  
Random Sample ◽  
Sensitivity And Specificity ◽  
Bayesian Method ◽  
Standard Estimate ◽  
Credible Intervals ◽  
Positive Results ◽  
Imperfect Test

The standard estimate of prevalence is the proportion of positive results obtained from the application of a diagnostic test to a random sample of individuals drawn from the population of interest. When the diagnostic test is imperfect, this estimate is biased. We give simple formulae, previously described by Greenland (1996) for correcting the bias and for calculating confidence intervals for the prevalence when the sensitivity and specificity of the test are known. We suggest a Bayesian method for constructing credible intervals for the prevalence when sensitivity and specificity are unknown. We provide R code to implement the method.

scholarly journals Application of Statistics in Establishing Diagnostic Certainty

2012 ◽  
Vol 47 (2) ◽  
pp. 233-236 ◽  
Author(s):  
Craig R. Denegar ◽  
Mitchell L. Cordova
Keyword(s):  
Health Care ◽  
Confidence Intervals ◽  
Likelihood Ratio ◽  
Sensitivity And Specificity ◽  
Diagnostic Tests ◽  
Athletic Trainers ◽  
Negative Outcomes ◽  
Certified Athletic Trainers ◽  
Statistical Procedures ◽  
Diagnostic Certainty

The examination and assessment of injured and ill patients leads to the establishment of a diagnosis. However, the tests and procedures used in health care, including procedures performed by certified athletic trainers, are individually and collectively imperfect in confirming or ruling out a condition of concern. Thus, research into the utility of diagnostic tests is needed to identify the procedures that are most helpful and to indicate the confidence one should place in the results of the test. The purpose of this report is to provide an overview of selected statistical procedures and the interpretation of data appropriate for assessing the utility of diagnostic tests with dichotomous (positive or negative) outcomes, with particular attention to the interpretation of sensitivity and specificity estimates and the reporting of confidence intervals around likelihood ratio estimates.

An Objective Method to Evaluate the Risk of Recurrence and Extension of Dupuytren’s Disease

2004 ◽  
Vol 29 (5) ◽  
pp. 427-430 ◽  
Author(s):  
Y. ABE ◽  
T. ROKKAKU ◽  
S. OFUCHI ◽  
S. TOKUNAGA ◽  
K. TAKAHASHI ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Confidence Intervals ◽  
Sensitivity And Specificity ◽  
Scoring System ◽  
Prognostic Value ◽  
Dupuytren’S Disease ◽  
Objective Method ◽  
Dupuytren's Disease ◽  
Risk Of Recurrence ◽  
New Scoring

This study was undertaken to assess the influence of the factors related to Dupuytren’s diathesis on the rates of recurrence and extension of Dupuytren’s disease after surgery. The records of 65 patients who underwent surgery for Dupuytren’s disease were retrospectively studied and the presence of factors related to diathesis were recorded. The sensitivity and specificity of each factor for predicting recurrence and extension were calculated. Odds ratios and 95% confidence intervals were also calculated and a discriminant analysis was performed to explore correlations between recurrence and extension and the significant variables. Our results confirmed the prognostic value of diathesis. The results have been used to develop a new scoring system for evaluating the risk of recurrence and extension.

Reading and Interpreting the Research

2016 ◽  
pp. 109-136
Author(s):  
John C. Norcross ◽  
Thomas P. Hogan ◽  
Gerald P. Koocher ◽  
Lauren A. Maggio
Keyword(s):  
Confidence Intervals ◽  
Sensitivity And Specificity ◽  
Effect Size ◽  
Test Scores ◽  
Odds Ratio ◽  
False Positives ◽  
Evidence Based ◽  
Normal Curve ◽  
False Negatives ◽  
Making Sense

This chapter reviews the numerical indices most frequently encountered in evidence-based practice (EBP), with an emphasis on practical interpretation. The research that fuels EBP abounds with numbers. Making sense of and applying that research requires familiarity with those numbers. This chapter covers features of the normal curve, standard errors, and confidence intervals for both test scores and statistics. Various measures of effect size and their practical interpretation receive special attention. Especially when investigating a condition (for example, depression or alcoholism), a host of rates and ratios play a prominent role in interpreting evidence, including the odds ratio, sensitivity and specificity of measures, false positives and false negatives, and positive and negative predictive power. The chapter also discusses the effect of outliers on research results.

scholarly journals Common Medical and Statistical Problems: The Dilemma of the Sample Size Calculation for Sensitivity and Specificity Estimation

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1258
Author(s):  
M. Rosário Oliveira ◽  
Ana Subtil ◽  
Luzia Gonçalves
Keyword(s):  
Sample Size ◽  
Confidence Intervals ◽  
Sensitivity And Specificity ◽  
Sample Size Calculation ◽  
Estimation Method ◽  
Interval Estimation ◽  
Sample Size Determination ◽  
Estimation Methods ◽  
Sample Sizes ◽  
Expected Length

Sample size calculation in biomedical practice is typically based on the problematic Wald method for a binomial proportion, with potentially dangerous consequences. This work highlights the need of incorporating the concept of conditional probability in sample size determination to avoid reduced sample sizes that lead to inadequate confidence intervals. Therefore, new definitions are proposed for coverage probability and expected length of confidence intervals for conditional probabilities, like sensitivity and specificity. The new definitions were used to assess seven confidence interval estimation methods. In order to determine the sample size, two procedures—an optimal one, based on the new definitions, and an approximation—were developed for each estimation method. Our findings confirm the similarity of the approximated sample sizes to the optimal ones. R code is provided to disseminate these methodological advances and translate them into biomedical practice.

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