Related Experiment Video
Updated: Jun 7, 2026
E-Patient Counseling Trial (E-PACO): Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy
Published on: August 1, 2019
Does prevalence matter to physicians in estimating post-test probability of disease? A randomized trial
Thomas Agoritsas1, Delphine S Courvoisier, Christophe Combescure
1Division of Clinical Epidemiology, University Hospitals of Geneva, Gabrielle Perret-Gentil 6, 1211, Geneva 14, Switzerland. thomas.agoritsas@hcuge.ch
Background:
The probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known.
Objective:
To assess whether physicians correctly estimate post-test probability according to various levels of prevalence and explore this skill across respondent groups.
Design:
Randomized trial.
Participants:
Population-based sample of 1,361 physicians of all clinical specialties.
Intervention:
We described a scenario of a highly accurate screening test (sensitivity 99% and specificity 99%) in which we randomly manipulated the prevalence of the disease (1%, 2%, 10%, 25%, 95%, or no information).
Main Measures:
We asked physicians to estimate the probability of disease following a positive test (categorized as <60%, 60-79%, 80-94%, 95-99.9%, and >99.9%). Each answer was correct for a different version of the scenario, and no answer was possible in the "no information" scenario. We estimated the proportion of physicians proficient in assessing post-test probability as the proportion of correct answers beyond the distribution of answers attributable to guessing.
Key Results:
Most respondents in each of the six groups (67%-82%) selected a post-test probability of 95-99.9%, regardless of the prevalence of disease and even when no information on prevalence was provided. This answer was correct only for a prevalence of 25%. We estimated that 9.1% (95% CI 6.0-14.0) of respondents knew how to assess correctly the post-test probability. This proportion did not vary with clinical experience or practice setting.
Conclusions:
Most physicians do not take into account the prevalence of disease when interpreting a positive test result. This may cause unnecessary testing and diagnostic errors.
More Related Videos
06:55Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
06:16Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
Related Concept Videos
01:08Prevalence and Incidence
Prevalence indicates the proportion of individuals in a population who have a specific disease or health condition at a...
01:13Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
01:24Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
01:12What is an Experiment?
01:52Regression Toward the Mean