Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Commentary: Muddying the waters: Limitations of secondary subgroup analyses of randomized controlled trials.

JTCVS structural and endovascular·2026
Same author

Ten-Year Anniversary of the Advisory Panel on Healthcare Innovation Report: Assessing Progress and What Is Left to Do.

HealthcarePapers·2026
Same author

Building the Structures and Ecosystem Required for Sustainable Health Innovation in Canada.

HealthcarePapers·2026
Same author

2025 Canadian Surgery Forum: Sept. 17-20, 2025.

Canadian journal of surgery. Journal canadien de chirurgie·2025
Same author

Home-Based Prehabilitation for Older Surgical Patients With Frailty: A Randomized Clinical Trial.

JAMA surgery·2025
Same author

Pilot study evaluating frailty-focused care for hospitalised patients with chronic obstructive pulmonary disease.

BMJ open quality·2025
Same journal

Harms Reporting Was Frequently Incomplete or Discordant in Biomedical Randomized Trials Published in 2023: A Meta-epidemiological Study.

Journal of clinical epidemiology·2026
Same journal

Using an Open Science Checklist in Grant Proposal Reviews to Predict Reproducibility of Funded Publications.

Journal of clinical epidemiology·2026
Same journal

A comparison of five statistical methods used to analyse longitudinal EORTC QLQ-C30 quality of life scores in randomised controlled trials: a simulation study.

Journal of clinical epidemiology·2026
Same journal

Sample Size Determination for Decision-centered Pragmatic Trials.

Journal of clinical epidemiology·2026
Same journal

Many multicenter randomized controlled trials do not account for center effect: a methodological review.

Journal of clinical epidemiology·2026
Same journal

Patient Acceptability of the Modified Zelen Approach to Randomized Trials - A Survey of the CAPS THA Cohort.

Journal of clinical epidemiology·2026
See all related articles

Related Experiment Video

Updated: Jun 20, 2026

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

Correlation between serial tests made disease probability estimates erroneous.

Carl van Walraven1, Peter C Austin, Alison Jennings

  • 1Ottawa Health Research Institute, Ontario, Canada. carlv@ohri.ca

Journal of Clinical Epidemiology
|September 1, 2009
PubMed
Summary
This summary is machine-generated.

Combining results from correlated diagnostic tests can overestimate disease probability. Clinicians must account for test correlation to accurately calculate posttest probability, avoiding significant diagnostic errors.

Related Experiment Videos

Last Updated: Jun 20, 2026

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

Area of Science:

  • Medical Diagnostics
  • Biostatistics
  • Clinical Decision-Making

Background:

  • Calculating disease probability after diagnostic testing is crucial for clinical decisions.
  • Standard methods assume test independence, which may not hold true in practice.

Purpose of the Study:

  • To investigate and illustrate the errors in disease probability calculations when using correlated diagnostic tests.
  • To quantify the impact of test correlation on posttest probability estimates.

Main Methods:

  • A simulation study was conducted to model disease status and binary test results.
  • Variables included disease prevalence, test performance characteristics, and the degree of correlation between tests.
  • The primary metric was the absolute difference between calculated and true disease probability after two positive tests.

Main Results:

  • Correlated diagnostic tests led to an overestimation of the true disease probability.
  • In cases of perfect test correlation, the posttest probability approximated the probability after a single test.
  • The discrepancy between calculated and true probability increased with higher test correlation and wider probability shifts between tests.

Conclusions:

  • Combining results from correlated diagnostic tests inflates the estimated disease probability.
  • Accurate clinical assessment requires explicit consideration of the correlation between serial diagnostic tests.