Questions tagged [sensitivity-specificity]

Sensitivity & specificity are descriptive statistics that measure the quality of a classification system. They are the proportion of real positives that are classified correctly (sensitivity) & the proportion of real negatives that are classified correctly (specificity). They are an integral part of ROC analysis.

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How to show that simple random sample sensitivity is unbiased for population sensitivity

In diagnostic testing, sensitivity $S$ is the probability that the test gives a positive result given that you have the condition being tested. From a simple random sample of people who take the test, ...
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Cases where specificity can be very high and precision very low simultaneously?

I was trying to understand the difference between a ROC curve and a PR curve by reading this page: ROC vs precision-and-recall curves. A quote from top voted the answer: Interestingly, by Bayes' ...
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Confidence intervals for series of Covid tests

Suppose I take two antigen tests and each test has a specificity of 75.3% (95% confidence interval from 65.8% to 83.4%) e.g. from https://pubmed.ncbi.nlm.nih.gov/33455451/ A. what is the sensitivity ...
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Is there any Statistic method which could reflect the diagnose value when the prevalence of special type of characteristic is lower

Our present study is conducting a novel diagnosis test for predicting a disease by the X-ray. The gold-standard is a minimally invasive procedure can test a tissue sample for the disease. In our ...
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Name of the value when sensitivity=specificity

Is there a name for the value along the ROC curve where sensitivity=specificity? This seems like a reasonable way to have a single scalar value to compare classifiers.
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Post test probability with a ROC curve

I have data that is normally distributed related to risk of a particular disease. At the median of the distribution, you would expect to observe the population prevalence level of disease P0=0.01. For ...
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Calculating true positive and true negative from sensitivity and specificity

I know the specificity and sensitivity of a test are both 90%. The incidence is 2.5%. The total population is 10,000 and the number of people with the illness is 250, how do I calculate the true ...
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Determine test accuracy

I'm trying to analyze results of a certain study. In this study, a certain diagnostic test was conducted in order to see if the pre-test assumed diagnosis was correct. Management was changed in about ...
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Logistic regression metric

I am interested to understand in which scenarios person should use sensitivity, specificity, and when should person opt for precision recall. On a high level I understand for a balanced data set we ...
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Is Average Accuracy An Ill-Defined Metric? [closed]

Five people each took a 100 question yes/no question test. I have their individual accuracy scores and was planning to report their average. However, I was informed that "You cannot take an ...
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Create AUC-ROC from single sensitivity and specificity value? [duplicate]

Is it possible and appropriate to estimate the area under the receiver operating characteristic curve from a single point estimate of an individual's sensitivity and specificity performance?
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Could we calculate the sensitivity and specificity when the test result of gold-standard is binary but the new test is 3-class classification?

Now we want to create a new test (Method A) to screen for a disease. In fact, there is a ‘gold-standard’ screening test for the disease (Method B). And the test result of gold-standard (Method B) is ...
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Roc curve straight line interpretation [duplicate]

How can I fix the straight line? is that mean that 70% of my cases that tag as positive and actually negative have the same value?
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Prediction, concordance, ROC curves, and the c-statistic: when is it appropriate to use?

I have a study with 2 variables where one variable should hypothetically predict the other. Var1 is a continuous biological measurement being categorized into a binomial: does the biological ...
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Accuracy, Recall (sensitivity), Specificity confusion

I came up with this paper with 765 citations! On page 2, it expresses an equation relating these metrics: Accuracy sensitivity (a.k.a Recall). specificity (a.k.a 1 - type1 error rate) prevalence (...
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S-Shaped/Vertical ROC curve crossing the diagonal

I am running binary logistic regression and when the ROC curve is created this is the output (see below): I believe it makes sense somewhat, as the model does have 100% specificity and 0% sensitivity ...
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1 Sigma Error Brings Specificity > 1

I am running a classification model on some medical images. When calculating the specificity, we get a value very close to one and then when the 1 sigma standard deviation is calculated its range ...
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Sensitivity / specificity with dependent observations

Suppose we have a classification scenario, where each observation is comprised from a reader, a case and the status (0 or 1). Each reader get several cases (but each case is shown to only one reader). ...
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Does a term exist for this concept in statistics?

Suppose you have a dataset where the covariates are biomedical information for a given patient (e.g. height, weight, blood type, etc.) and the response variable is whether the patient has a certain ...
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How to calculate the likelyhood that a single measurement "belongs" to a standard distribution?

(Apologies in advance for using wrong terms and probably asking the wrong question) Given a standard distribution with some $\mu$ and $\sigma$, and a single measurement $m$. Can I calculate the ...
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Controlling false positive rate and false negative rate

My notes define the false positive rate as $$\dfrac{\text{false positives}}{\text{true negatives} + \text{false positives}} = \dfrac{\text{false positives}}{\text{total negatives}}$$ and the false ...
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Why is it called Sensitivity/Recall and Specificity?

Where do the terms: Sensitivity, Recall and Specificity come from historically? I've been looking for an answer for quite some time but to no avail. I understand the formulae and what they mean but I ...
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Calculating the standard error of sensitivity and specificity

For a method, I'm calculating it's sensitivity and specificity. I also want to calculate standard errors, but I'm unsure how. I don't have a sample to calculate it from. All I have are 50 iterations ...
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Sensitivity Testing - Using Probit GLM to Infer Parameters of the Independent Variable

I am an engineer working with some sensitivity data. In this type of experiment, you test a device at a certain stimulus and observe whether or not it fails (binary outcome). For example, you might ...
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Multiple observations AUC, Sensitivity and Specificity

I am trying to find a CIs for the AUC, sensitivity and specificity. In my scenario, each observation consistent of a subject and an area examined, that is it is obvious that areas examined for the ...
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Estimating the mean and variance for a set of probabilities (bounded by 0 and 1) based on Image Segmentation results

Data My data is from a set of images wherein I am computing the Sensitivity of an image segmentation algorithm. Sensitivity is computed as: $$Sensitivity=\frac{TP_{pixels}}{TP_{pixels}+FN_{pixels}}$$ ...
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Predictive values

I have a MCQ question from an old exam in a class for evidence based medicin I cannot figure out how to calculate: The authors found a sensitivity of 99% and a specificity of 40%. Under the assumption ...
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Why is c-index useful when it is derived from time-backward sensitivity and specificity?

Concordance index with a binary outcome is equivalent to area under the receiver-operator curve. Thus, c-index is based on sensitivity and specificity. As Frank Harrell has pointed out, both on Cross ...
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Does the number of tests conducted in one sample affects the estimation of CI of overall sensitivity of the assay?

Say we developed an assay that detects mutations in biosample. In each sample, we test large amount of DNA sites (tens of thousands). Therefore, we will have sensitivity and corresponding confidence ...
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Sum of sensitivity and specificity meaningful?

I came across an article in the BMJ that claims that a useful rule of thumb metric for assessing a medical test's performance is that the sum of specificity and sensitivity should be greater than 1.5 ...
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Why does SpIn SnOut exist?

Can somebody explain the logic behind "SpIn SnOut" ? Let Sp = specificity, Sn = sensitivity, TP/TN = true positives/negatives, FP/FN = false positives/negatives, and PPV/NPV = positive/...
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What's a good way to calculate agreement between examiners when the measure is binary?

I want to assess ability to identify tumor types in humans vs. a computers. There are 7 tumor types. I am interested in calculating agreement between the human examiners but I am not sure the best way ...
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When should one look at sensitivity vs. specificity instead of precision vs. recall?

The precision vs. recall tradeoff is the most common tradeoff evaluated while developing models, but sensitivity vs. specificity addresses a similar issue. When should one of these pairs of metrics be ...
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Why is the optimal cutoff for AUC different from that of specificity in this simulation?

I am working on a binary classification problem on an imbalanced data where the majority class is about 90% 'no' and the minority class is about 10% 'yes' of the total data. Iteration 1: I randomly ...
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Logistic regression for classification: are there any analytical solutions for the out-of-sample accuracy?

I run a binary logistic regression, with a binary dependent variable and a continuous independent one. Now I want to evaluate the out-of-sample performance of the classification algorithm so obtained. ...
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Chances of testing negative

Buddy takes a test and tests positive for a disease. Buddy was close to $6$ other friends they all take the same test and end up testing negative. The test has a $FPR=0.01$ and a $FNR=0.15$. What's ...
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Tuning the Logit model based on sensitivity

I am working on a logistic model to predict whether or not an employee is likely to take a leave day during a given three month period. The model only correctly classified the “non-event" ...
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Origin of terms "sensitivity" and "specificity"

Who coined the terms "sensitivity" and "specificity"—meaning the complements of false positives, and false negatives, respectively in tests and measures—and when did they first do ...
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"Posterior" Sensitivity and Specificity in Classification

Let's set aside what we know about proper scoring rules and predicting probabilities; let's do CLASSIFICATION. Define sensitivity as the ability to call an observation a $1$ if it really is a $1$: $ \...
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When are ROC curves to compare imaging tests valid? (Focus on the example below)

I would like to ask what criticism could be raised in the following case example: In this paper they test a way of detecting narrowing of the cervical canal on radiographs using a ratio of ...
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Is there a commonly-used name for "one minus sensitivity" and "one minus specificity" of a test, such as a Covid-19 or pregnancy test?

I am looking at a test strategy for Covid-19, where I want to combine two tests with different sensitivity and specificity. Visualizing this would probably make sense in a "1-sensitivity" vs ...
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Sensitivity and Specificity for multinomial logit model

Hi I have a question about the sensitivity and specificity. Situation: I have a estimation result using multinomial logit model. I want to calculate the sensitivity and specificity. Question: ...
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Why we use precision/recall in binary classification but sensitivity(=recall)/specificity in medicine?

Sensitivity=recall is used in both fields, but the second metric is different. Why? Both tasks (classification and medicine) look same - data has two classes and we do some predictions on it and want ...
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Identifying threshold from Youden Index - Using ROC curve to calculate minimally important change (MIC)

I am trying to determine the minimally important change (MIC) of a frailty instrument using an anchor-based approach outlined below. Step 1. Fit a logistic regression model between ...
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Take into account Sensitivity and Specificity when calculating Estimates/CI's

I'm trying to thing about how to calculate confidence intervals in the presence of known measurement error for some test protocol. Let's assume we're doing a sample survey in which we're performing ...
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Can AUC increase while both sensitivity and specificity decrease?

Assume I am given two classifiers - A and B. Is it possible that the specificity and sensitivity obtained from using B will be higher than A, but the AUC of A will be higher than the AUC of B? Will ...
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Estimating positive and negative predictive value without knowing the prevalence

There is a lot of discussion about the positive predictive value of a test currently. I know that if I know specificity, sensitivity of a test and the prevalence $p$ in the sample, then I can easily ...
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Balance classifier performance (boosting ensemble)

I'm trying to build a classifier for my highly imbalanced binary data, and I'd appreciate some help on how to balance by results. The dataset has the following stats: ...
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Classifier can predict time series 1 day in advance, but not more. Why?

To ask the question more precisely: when doing Time Series classification, I observe the classifier prediction is good if test data directly follows (in chronology) the train data. But when the train ...
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Prevalence threshold calculations error

I see on this page the formula for calculation of prevalence threshold: Given also are following values on that page: ...
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