Printed From BioPortfolio.com

06:00 EDT 11th May 2011 | BioPortfolio

Authors who publish evaluations of dichotomous (yes/no) diagnostic tests often include the predictive values of their test at a single prior probability (eg, the prevalence of the target disease within the evaluation data set). The objectives of this technical note are to demonstrate why single-probability predictive values are misleading and to show a better way to display positive predictive values (PPV) and negative predictive values (NPV) for a newly evaluated test. Secondly, this technical note will show readers how to calculate predictive values from only sensitivity and specificity for any desired prior probability. As prior probability increases from 0% to 100%, PPV increases from 0% to 100%, but NPV goes in the opposite direction (drops from 100% to 0%). Because prior probabilities vary so greatly across situations, predictive values should be provided in publications for the full range of potential prior probabilities (if provided at all). This is easily done with a 2-curve graph displaying the predictive values (y-axis) against the prior probability (x-axis).

Section of Epidemiology, Department of Population Medicine & Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.

This article was published in the following journal.

**Name: **Veterinary clinical pathology / American Society for Veterinary Clinical Pathology

**ISSN**: 0275-6382**Pages**:

- PubMed Source: http://www.ncbi.nlm.nih.gov/pubmed/21554365
- DOI: http://dx.doi.org/10.1111/j.1939-165X.2011.00315.x

Bayesian predictive power, the expectation of the power function with respect to a prior distribution for the true underlying effect size, is routinely used in drug development to quantify the probabi...

The dangers of widespread nitric oxide screening for primary ciliary dyskinesia.

Primary ciliary dyskinesia (PCD) is underdiagnosed and requires complex testing at specialist diagnostic centres. Measurement of nasal nitric oxide (nNO) has good sensitivity and specificity screening...

Diagnostic testing in the context of high-value care: Incorporating prior probability.

This is the fifth article of a series on fundamental concepts in biostatistics and research. In this article, the author reviews the fundamental concepts in diagnostic testing, prior probability and p...

Pretest risk estimation is routinely used in clinical medicine to inform further diagnostic testing in individuals with suspected diseases. To our knowledge, the overall characteristics and specific d...

Although progressive ratio (PR) schedules have been used to explore effects of a range of reinforcer parameters (e.g., magnitude, delay), effects of reinforcer probability remain underexplored. The pr...

The purpose of this study is to evaluate if the implementation of quantitative pretest probability assessment will significantly reduce the unnecessary use of the intra-emergency departmen...

CT Perfusion Added to CT Angiography

Background In the differential diagnosis of unstable angina versus non-anginal chest pain, an exercise test is often the modality of choice for further investigation. However, in a substan...

Neonatal Procalcitonin Intervention Study

In neonates, clinical signs and symptoms associated with early-onset sepsis are non-specific and currently available tests have poor positive and negative predictive values. The investigat...

One-hour Troponin in a Low-prevalence Population of Acute Coronary Syndrome

This study evaluates if the 1-hour rule-in/rule-out algorithm for a high-sensitivity cardiac troponin T (hs-cTnT) is safe and effective in patients with a lower pretest probability of an a...

D-dimer Testing Tailored to Clinical Pretest Probability in Suspected Pulmonary Embolism

Prospective, multicentre, cohort study assessing a diagnostic management strategy for suspected Pulmonary Embolism with independent central adjudication of outcomes

Predictive Value Of Tests

In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.

Likelihood Functions

Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.

Confidence Intervals

A range of values for a variable of interest, e.g., a rate, constructed so that this range has a specified probability of including the true value of the variable.

Statistical Distributions

The complete summaries of the frequencies of the values or categories of a measurement made on a group of items, a population, or other collection of data. The distribution tells either how many or what proportion of the group was found to have each value (or each range of values) out of all the possible values that the quantitative measure can have.

Risk

The probability that an event will occur. It encompasses a variety of measures of the probability of a generally unfavorable outcome.