Why is sensitivity independent of prevalence

Sensitivity and specificity are terms used to evaluate a clinical test. They are independent of the population of interest subjected to the test. Positive and negative predictive values are useful when considering the value of a test to a clinician. They are dependent on the prevalence of the disease in the population of interest. The sensitivity and specificity of a quantitative test are dependent on the cut-off value above or below which the test is positive. In general, the higher the sensitivity, the lower the specificity, and vice versa.

Receiver operator characteristic curves are a plot of false positives against true positives for all cut-off values. The area under the curve of a perfect test is 1. Many clinical tests are used to confirm or refute the presence of a disease or further the diagnostic process. Ideally such tests correctly identify all patients with the disease, and similarly correctly identify all patients who are disease free.

In other words, a perfect test is never positive in a patient who is disease free and is never negative in a patient who is in fact diseased. Most clinical tests fall short of this ideal. The following terms are fundamental to understanding the utility of clinical tests: When evaluating a clinical test, the terms sensitivity and specificity are used. The terms positive predictive value PPV and negative predictive value NPV are used when considering the value of a test to a clinician and are dependent on the prevalence of the disease in the population of interest.

True positive : the patient has the disease and the test is positive. False positive : the patient does not have the disease but the test is positive.

True negative : the patient does not have the disease and the test is negative. False negative : the patient has the disease but the test is negative. Surgery Flashcard Collection. Anatomical Planes. Anatomy Flashcard Collection. The Inguinal Canal. A man with penile swelling. A man with blood in his urine. PSA Question Bank. Medical Student Finals Question Bank. ABG Quiz. Share Tweet. Last updated: November 12, Suggest an improvement.

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Improve this answer. Community Bot 1. Coefficients: Estimate Std. Null deviance: Sensitivity : 0. For showing that it actually does change. But how does it considering Tim 's answer?

Isnt it contradicting? But the quote seems also to be saying something else test sensitivity will likely be higher in hospitalized patients, and test specificity higher in outpatients so authors say that sensitivity differs in different groups. And I also quote another source, althoguh not as reliable as the Harrison's, that says that it is a safe 'assumption'. All I want to ask is , what is the 'assumption'? If it didn't hold, it would mean that the probability theory is broken and it isn't. Hence the question.

The sensitivity of a test depends on the population where used. The assumption that's it is independent is not always true. I'm asking how and why. But it follows from the mathematical definitions of sensitivity and specificity that prevalence cannot be one of these factors, at least not directly.

By the way, feel free to accept my answer if you're satisfied with my explanation of the mathematical definitions. I wanted to know the relation between sensitivity and prevalence mathematically. I know how they are defined. I guess the relation comes in because of the way they are calculated.

You cannot say anything about the value of one based on the value of the other. Even this link calls it a simplification. Show 2 more comments. Fomite Fomite That is true.

But how does sensitivity itself change is the question. I think the statement applies to the usual variety of tests based on a threshold of some proxy measurement. This then makes sensitivity appear larger. The explanation of the phenomenon An observation that sensitivity may be higher in a hospital based context is therefore not unrealistic. But it does give scientists a headache. ReneBt ReneBt 2, 7 7 silver badges 24 24 bronze badges.

Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. Featured on Meta. Now live: A fully responsive profile. In addition, false positive screening outcomes might be quite permissible if no harm is likely to be done to clients in protecting them against a target condition even if that condition is not present.

For example, people who are mistakenly told that they have peripheral artery disease, despite not actually having it, are likely to benefit from adopting advice to exercise appropriately, improve their diet, and discontinue smoking.

A high NPV is desirable, implying that false negatives are minimized, under a different set of circumstances. Some of these are a condition being serious, largely asymptomatic, or contagious, or if treatment for a condition is advisable early in its course, particularly if the condition can be treated effectively and is likely to progress quickly.

Under these circumstances, it would be highly undesirable if a screening test indicated that people did not have a condition when in fact they did. A moderate NPV—with its greater proportion of false negative screening test outcomes—might be acceptable under other circumstances, however, and most of those circumstances are the opposite of those that make a high NPV desirable.

For example, the false negative outcomes associated with moderate NPVs might not be problematic if the target condition is not serious or contagious, or if a condition does not progress quickly or benefit from early treatment.

Moderate NPVs might also be acceptable if diagnosis at low levels of a condition is known to be ambiguous and subsequent screening tests can easily be scheduled and performed, or if, given time, a condition is likely to resolve itself satisfactorily without treatment. If, for a variety of reasons, the PPVs and NPVs on a screening test were deemed to be either too high or too low, they could be adjusted by altering the stringency of the screening test for example, by raising or lowering cutpoints on a continuous variable or by changing the components that comprise a screening test , by altering the sample of people on whom the analyses were based for example, by identifying people who are regarded as having more pertinent demographic or health status variables , or by altering the nature of the reference standard.

Those strategies would almost inevitably result in changes to the sensitivity and specificity values, and those revised values would simply need to be reported as applying to the particular new level of stringency on the screening test, the applicable population, and the reference standard when that test was being described. This reveals, yet again, that pliability can be associated with sensitivity, specificity, and predictive values.

When describing screening tests, many researchers provide information about their reference standard; the prevalence of the target condition in their research sample s ; the criteria that had been used to indicate presence or absence of a condition according to the screening test; and the sensitivity, specificity, and predictive values they obtained 6 , 7 , 15 , 17 , The research results are not always impressive or what the researchers might have hoped for, but at least it is possible to draw informed conclusions from those results.

Sometimes only partial information is provided, and that limits the usefulness of research. For example, in a systematic review concerning the toe—brachial index in screening for peripheral artery disease, Tehan et al. In one of the more informative articles reviewed by Tehan et al. However, they provided insufficient interpretation at times.

For example, they reported an unusually low sensitivity value of Deficiencies in provision of information can be even more problematic. Although they reported a specificity of That these values are so high in a screening context raises suspicions. When following those suspicions through, it becomes evident that the researchers used the ABI as a component of the reference standard as well as being the sole variable that comprised the screening test.

The above examples illustrate the importance of research consumers being provided with complete information when screening tests are being described, and consumers being able to interpret that information appropriately—sometimes with at least a modicum of skepticism. Having a healthy level of skepticism as well as clarity concerning the nature and appropriate interpretations and uses of sensitivity, specificity, and predictive values, can be seen as important for educators, researchers, and clinicians in public health.

The author declares that this research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Rod Pope and Peta Tehan provided valuable feedback on earlier drafts of this manuscript. Professor Pope also drew my attention to literature that I had not been aware of, and Dr.

Tehan generously shared computer output of receiver operating characteristic analyses that provided confirming insights about sensitivity and specificity.

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