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

Decision Making: P-value Method01:09

Decision Making: P-value Method

6.8K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.8K
Bioequivalence Data: Statistical Interpretation01:16

Bioequivalence Data: Statistical Interpretation

192
Body:The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
192
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

6.1K
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...
6.1K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

404
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,...
404
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.1K
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...
5.1K
Dose-Response Relationship: Potency and Efficacy01:22

Dose-Response Relationship: Potency and Efficacy

6.3K
The potency of a drug is the measure of its ability to produce a biological response and can be compared by looking at the half-maximum effective concentration or EC50 values of different drugs. A lower EC50 value indicates higher potency of the drug. In the dose–response curve of two antihypertensive drugs, candesartan and irbesartan, a significant difference is observed in their EC50 values. A lower EC50 value for candesartan indicates that it is more potent than irbesartan, as it...
6.3K

You might also read

Related Articles

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

Sort by
Same author

Investigating the analytical robustness of the social and behavioural sciences.

Nature·2026
Same author

A comparison of combined <i>p</i>-value functions for meta-analysis.

Research synthesis methods·2026
Same author

Addressing Outcome Reporting Bias in Meta-Analysis: A Selection Model Perspective.

Statistics in medicine·2025
Same author

A Bayes factor framework for unified parameter estimation and hypothesis testing.

The British journal of mathematical and statistical psychology·2025
Same author

A scoping review on metrics to quantify reproducibility: a multitude of questions leads to a multitude of metrics.

Royal Society open science·2025
Same author

Outcomes Truncated by Death in RCTs: A Simulation Study on the Survivor Average Causal Effect.

Biometrical journal. Biometrische Zeitschrift·2025
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
Same journal

Subgroup Analysis of Interval-censored Failure Time Data With Application to Alzheimer's Disease.

Statistics in medicine·2026
Same journal

Rejoinder to Commentaries on "A Perspective on the Appropriate Implementation of ICH E9(R1) Addendum Strategies for Handling Intercurrent Events".

Statistics in medicine·2026
Same journal

A Multi-Stage Drop-the-Loser Design With Superiority Boundaries.

Statistics in medicine·2026
Same journal

Interpretable ROI Identification in Brain Image Analysis: Overcoming CNN Black Box Challenges With Kriging-Enhanced Adaptive Sampling.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Jan 14, 2026

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

19.4K

Combined P-Value Functions for Compatible Effect Estimation and Hypothesis Testing in Drug Regulation.

Samuel Pawel1, Małgorzata Roos1, Leonhard Held1

  • 1Epidemiology, Biostatistics and Prevention Institute (EBPI), Center for Reproducible Science (CRS), University of Zurich, Zurich, Switzerland.

Statistics in Medicine
|October 23, 2025
PubMed
Summary
This summary is machine-generated.

Drug regulation

Keywords:
confidence intervalestimandmedian estimatemeta‐analysistwo‐trials rule

More Related Videos

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation
15:04

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation

Published on: January 19, 2019

12.7K
High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents HPHC
11:38

High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents HPHC

Published on: May 10, 2016

12.7K

Related Experiment Videos

Last Updated: Jan 14, 2026

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

19.4K
Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation
15:04

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation

Published on: January 19, 2019

12.7K
High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents HPHC
11:38

High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents HPHC

Published on: May 10, 2016

12.7K

Area of Science:

  • Biostatistics
  • Pharmacometrics
  • Regulatory Science

Background:

  • The two-trials rule in drug efficacy requires two significant trials.
  • Combining effect estimates from multiple trials is complex.
  • Fixed-effect meta-analysis may produce misleading confidence intervals.

Purpose of the Study:

  • To unify the two-trials rule and meta-analysis within a combined p-value framework.
  • To derive compatible p-values, effect estimates, and confidence intervals.
  • To evaluate different p-value combination methods for two trials.

Main Methods:

  • Recasting the two-trials rule and meta-analysis using combined p-value functions.
  • Deriving closed-form solutions for p-values, effect estimates, and confidence intervals.
  • Analyzing Wilkinson's, Stouffer's, Edgington's, Fisher's, Pearson's, and Tippett's methods.

Main Results:

  • All methods consistently estimate the true effect when trials have identical effects, though bias varies.
  • Methods differ in their convergence when true effects diverge: conservative (Pearson's), anti-conservative (Fisher's, Tippett's), and balanced (Edgington's, meta-analysis).
  • Edgington's confidence intervals encompass individual trial effects, unlike meta-analytic intervals.

Conclusions:

  • The choice of method depends on the specific estimand of interest.
  • All analyzed methods can be appropriate for combining results from two trials.
  • The R package 'twotrials' facilitates compatible hypothesis testing and effect estimation.