Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Decision Making: Traditional Method

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...

Hypothesis: Accept or Fail to Reject?

Hypothesis: Accept or Fail to Reject?

The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null hypothesis and 'fail to...

Statistical Hypothesis Testing

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

Cochran's Q Test

Cochran's Q Test

Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square distribution,...

Toxicity Testing in Animals

Toxicity Testing in Animals

Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...

Sensitivity, Specificity, and Predicted Value

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...

You might also read

Related Articles

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

Sort by

Same author

Motivational Interviewing: A Physician and Social Worker Partnership in Rehabilitation Healthcare.

Health & social work·2024

Same author

Social Work and Engaging with the Seemingly Incompatible Host Agency.

Health & social work·2022

Same author

Lower Copays Sweeten Outcomes-based Deal for Brilinta.

Managed care (Langhorne, Pa.)·2019

Same author

Once-a-day HIV Treatment: Pricey But Perhaps Worth It.

Managed care (Langhorne, Pa.)·2019

Same author

CVS and the $100,000 QALY.

Managed care (Langhorne, Pa.)·2019

Same author

Backlash Against Copay Accumulators.

Managed care (Langhorne, Pa.)·2018

Same journal

Big pharma moves into biologics.

Biotechnology healthcare·2013

Same journal

Study: High Copayments Cut Biodrug Use up to 21 Percent.

Biotechnology healthcare·2013

Same journal

Avian flu and the quest for immunity: not just chicken feed.

Biotechnology healthcare·2013

Same journal

Variety of Biotech Therapies, Uses Take Prominence Among ASCO Presentations.

Biotechnology healthcare·2013

Same journal

Opening the door to follow-on proteins?

Biotechnology healthcare·2013

Same journal

On specialty pharmaceuticals: reflections of a second-year medical student.

Biotechnology healthcare·2013

See all related articles

Search research articles

Related Experiment Video

Updated: May 14, 2026

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

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

Published on: August 9, 2024

To test or not to test?

Biotechnology Healthcare

|February 12, 2013

Summary

This summary is machine-generated.

Genetic testing can enhance diagnosis and identify patients unlikely to respond to expensive biologic therapies. However, this approach is not yet widely adopted, presenting a complex issue for healthcare payers.

Related Experiment Videos

Last Updated: May 14, 2026

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

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:

Pharmacogenomics
Medical Diagnostics
Health Economics

Background:

Biologic therapies represent a significant healthcare expenditure.
Identifying non-responders to biologics is crucial for cost-effectiveness.
Genetic testing offers a potential tool for optimizing treatment selection.

Purpose of the Study:

To evaluate the utility of genetic testing in improving diagnosis.
To assess the role of genetic screening in identifying non-responders to biologic therapies.
To explore the perspective of payers on the integration of genetic testing in healthcare.

Main Methods:

Review of current literature on genetic testing in therapy selection.
Analysis of cost-effectiveness data for biologic treatments.
Examination of payer policies and perspectives on novel diagnostic tools.

Main Results:

Genetic testing shows promise for personalized medicine by improving diagnostic accuracy.
Screening for non-response to biologics via genetic tests is technically feasible but not standard practice.
Payers face complex decisions regarding the reimbursement and adoption of genetic testing due to cost and evidence considerations.

Conclusions:

Genetic testing can refine diagnoses and predict biologic therapy response, optimizing patient outcomes and resource allocation.
The widespread adoption of genetic testing for therapy selection is hindered by economic and logistical challenges for healthcare systems.
Further research and clear payer guidelines are needed to integrate genetic testing into mainstream clinical practice effectively.