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 can sensitivity and specificity of 2 tests can be used to estimate the adjusted infection and co-infection rates when considering 2 diseases?

I can't figure out how to solve this problem.. let's say I test 100 individuals for disease X with a test of sensitivity (se)=0.9 and specificity (sp)=0.8. Given say 50 individuals are positive (p=0.5)...
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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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Statistical metric for maximizing true positives relative to “redundant” positives

I am trying to optimize a binary classification test. Unlike many common instances, my "list" of true positives and false positives also contains a large number of redundant true positives. ...
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35 views

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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What could be a good way to interpret this neurophysiological data?

We are looking at different combinations of neurophysiological tests, and trying to find out which combinations of variables are best suited for clinical use (diagnosing diabetic polyneuropathy). To ...
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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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compute posterior probability using specificity and sensitivity

Im trying to calculate the posterior and have this posterior_probability = (prevalence * sensitivity)/ ((prevalence * sensitivity) + ((1-prevalence) * specificity)) but am getting the wrong answer?
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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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Variable significance very sensitive to specification of non-correlated second variable

I´m doing research on a political science topic and my models leave me behind with a big questionmark at this point. I have a dataset containing 79 observations on a number of variables and trying and ...
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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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P-values for sensitivity/specificity when only improvement is possible

Imagine two diagnostic tests in a single population at risk of the disease, one of which is always "more strict" than the other, meaning that if test 1 is positive, test 2 will be positive ...
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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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Any formula to assess accuracy of repeated testing?

Accuracy of a diagnostic test can be assessed by sensitivity, specificity, positive and negative predictive values. How are these affected if test is repeated one more time (total 2 times). Is there ...
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Population selection for sensitivity and specificity

For medical physical assessment sensitivity and specificity is commonly used to assess the clinical relevance of the test. I'm specifically referring to manual medicine here, with orthopedic tests of ...
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Logistic regression model predicts only one outcome, producing a high specificity but very low sensitivity. How do I improve the model?

I'm designing a logistic regression model to predict hospital mortality. Why? To identify 'adjusted' odds ratios for a variable of interest on mortality. Methods: - set up using a training dataset (75%...
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Calculate the confidence interval of a balanced accuracy by taking the mean of the CIs of sensitivity and specificity?

Because sensitivity and specificity are typically estimated as binomial proportions (e.g. k = TP, n = TP+FN), we can use any of the methods used to estimate the confidence interval for binomial ...
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how taking multiple tests increases chance of detection of illness if you have it?

I thought of this as I'm reading about covid tests accuracy, and I am thinking how taking multiple tests influences the chance of correctly detecting illness/no illness. So, if I understand this ...
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Bootstrap vs Wilson score confidence interval

For estimation of the confidence interval of sensitivity and specificity, when I should use the Wilson score and when I should use bootstrapping?
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How to do meta-analysis of diagnostic tests with only sensitivity and specificity?

I am a junior physician, and I've run into some problems with the meta-analysis of diagnostic tests and I was hoping to get some input. Some background: Aside from my undergraduate statistics courses ...
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Testing the difference in sensitivities between two lab tests - with part overlapping populations

I have two rapid diagnostic tests for which I test their sensitivity (+/-) in detecting cases against the gold standard (ELISA, also +/-). I now want to test whether the tests perform significantly ...
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Is there any possibility of overfitting even after higher AUC, specificity and sensitivity obtained through repeated k-fold cross-validation?

I have built a model where a 10-fold cross-validation was performed 10 times. The average AUC, MCC, specificity, sensitivity of 10 times were reported as the prediction performance. Yet some people ...
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Ordinal logistic regression: sensitivity / specifity

I am trying to determine the sensitivity and specificity per category of an outcome with ordinal categories between 1 and 6 predicted by 5 predictors. Overall accuracy is around 45%, but I want to ...
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Sensitivity test

I have an independent variable that has 3 groups - 'Yes' 'No' and 'Unknown'. I have decided that I can assume the 'Unknowns' as 'No'. I want to run sensitivity analysis to test whether making this ...
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PPV vs Sensitivity, they look the same!

I am looking at the equation PPV and Sensitivity and I got this PPV = TP / (TF+FN) and Sensitivity = TP / (TF+FN) Which ...
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Calculating Specificity from Weka output

In short I need to know or calculate the sensitivity and specificity from Weka 3.8.4 output. ...
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Can the smallest detectable change or minimal detectable change of a measure be applied to a study with a much smaller sample size?

In a pilot study, we are evaluating whether there was an effect of the intervention. To determine the magnitude of the effect we are comparing the mean difference between timepoints to the group level ...
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Sensibility and specificity with inconclusive cases (non binary problem)

I need to compare the diagnosis of two methods vs a gold standard (all is paired data). The results are categorical variables classified as positive, negative and inconclusive. How do you deal with ...
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OneR and area under the ROC curve

In the book Data Mining, author Ian Witten says that it does not make sense to calculate the area under the ROC curve when applying OneR because it finds only one point (sensitivity, specificity) ...
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Imperfect test sensitivity…what does 0% prevalence really mean?

I'm trying to think about test sensitivity and specificity in a theoretical disease-host system. Say you have a population from which you take a sample and test for the pathogen of interest using a ...
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Could two tests, one with high sensitivity, and other with high specificity, be meaningfully combined?

As an example, let's say we have some test with 99% sensitivity but 50% specificity, and we have some other test with 60% sensitivity and 95% specificity. So, we could have confidence in positive ...
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Model vs clinician label generation - Prevalence & Sensitivity

I was reading online about generating labels for a dataset manually through clinician review and based on probabilistic models where we get the likelihood. I was able to read under the advantage of ...
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Very low specificity in random forests classification model with balanced classes

I've been building a model within a k-fold cross-validation,and seem to be getting pretty much the same results i.e. very high sensitivity and low specificity. What I've tried: Different values of ...
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106 views

ROC Curve AUC for Hypothesis Testing Sensitivity (Power) vs Specificity ($1-\alpha$)

Let's set up a hypothesis test of $H_0: \theta=\theta_0$ versus $H_1: \theta\ne\theta_0$, and let's say that I have two techniques to assess this (say equal-variance t-test versus unequal-variance ...
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Sensitivity of a diagnostic test

Diagnostic tests A1 and A2 are used to detect the presence or absence of a disease. The results of A1 and A2 are a priori independent of the presence or absence of the disease. A1 and A2 have ...
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ROC showing some strange results

I have a continuous x and a binary y and I do a roc analysis on this (using roctab in stata). I get the following output: Viewing the variables, there are only 2 ...
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Deriving a loss function properly accounting for different error type costs

Consider a classification setting like a medical test: Not finding an existing health issue might be much worse (by a factor of 50) than assuming an issue when there is none. I.e. a setting in which ...
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ROC curve looking off compared to my results

I have a dependent variable "response" as binary 1 = response, 0 = no response (for surgery). I have an independent variable of a certain measurement in degrees (continuous/ordered variable). A ...
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FPR in Confusion Matrix

I was trying to manually calculate $\text{TPR}$ and $\text{FPR}$ for the given data. But unfortunately I dont have any false positive cases in my dataset and even no true positive cases. So I am ...
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Why is the mean of sensitivity and specificity equal to the AUC?

For a given cut-point in a prediction model or score, the mean of sensitivity and specificity equals the AUC. I've read that and I have observed this empirically. How can I prove this?
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How to make sense of both high FNR and NPV?

So, even though this is one of the most basic and already explained things about statistics I always seem to find way not to get it. Having this table which shows high Negative Predictive Value (352/...
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Finding optimal cutoff point [duplicate]

.Hello,everyone. I am studying the influence of one biomarker on multiple disease characteristics, and I would like to calculate its cutoff point. I created univariate ROC curves to investigate the ...
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Rule of Thumb for Sensitivity and Specificity

What are rule of thumb cutoffs for sensitivity and specificity? What is high, good, fair, low?
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Combining time series to increase sensitivity

I have two sensors logging data synchronously. The two sensors are similar, so the recorded time series are correlated except for some special conditions. I recorded data in different input parameter ...
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How to decide threshold values for AUC?

With respect to the pROC package for R(https://rdrr.io/cran/pROC/man/ggroc.html). ...