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

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
One-Way ANOVA01:18

One-Way ANOVA

One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
Two-Way ANOVA01:17

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...
Introduction to the Sign Test01:10

Introduction to the Sign Test

The sign test is an important tool in nonparametric statistics, offering a straightforward yet effective method for analyzing matched pairs, nominal data, or hypotheses concerning the median of a population. It transforms data points into positive or negative signs, avoiding the need for assumptions about data distribution and instead focusing on the direction of change. It is particularly valuable when data does not conform to the normal distribution requirements of many parametric tests. For...
Bonferroni Test01:10

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...

You might also read

Related Articles

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

Sort by
Same author

Closed MCP-Mod for Pairwise Comparisons of Several Doses With a Control.

Statistics in medicine·2025
Same author

Optimal allocation strategies in platform trials with continuous endpoints.

Statistical methods in medical research·2024
Same author

A novel approach to visualize clinical benefit of therapies for chronic graft versus host disease (cGvHD): the probability of being in response (PBR) applied to the REACH3 study.

Bone marrow transplantation·2023
Same author

Familywise error rate control for block response-adaptive randomization.

Statistical methods in medical research·2023
Same author

Designing an exploratory phase 2b platform trial in NASH with correlated, co-primary binary endpoints.

PloS one·2023
Same author

Adjusting for treatment selection in phase II/III clinical trials with time to event data.

Statistics in medicine·2022
Same journal

Comparison of Different Methods for the Meta-Analysis of Diagnostic Test Accuracy Studies-A Simulation Study.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
See all related articles

Related Experiment Videos

Directional multivariate tests rejecting null and negative effects in all variables.

Ekkehard Glimm1, Jürgen Läuter

  • 1Novartis Pharma AG, Basel, Switzerland. ekkehard.Glimm@Novartis.com

Biometrical Journal. Biometrische Zeitschrift
|July 30, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces two novel multivariate tests for treatment superiority, improving upon existing methods by allowing for superiority across multiple endpoints. These statistical tests are validated through simulations and applied to an osteoporosis clinical trial.

Related Experiment Videos

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Multivariate Analysis

Background:

  • Establishing treatment superiority often involves multiple endpoints.
  • Existing one-sided multivariate tests have limitations regarding null hypothesis specification.
  • Multivariate normal distribution is a common assumption in statistical modeling.

Purpose of the Study:

  • To propose two novel directional multivariate tests for assessing treatment superiority.
  • To overcome limitations of previous methods by relaxing the null hypothesis constraint.
  • To evaluate the performance and applicability of the proposed tests.

Main Methods:

  • Development of a one-sided, scale-invariant Hotelling T²-test variant.
  • Introduction of a data-driven summary score test.
  • Performance evaluation using Monte Carlo simulations.
  • Application in an osteoporosis clinical trial setting.

Main Results:

  • The proposed tests effectively establish treatment superiority across multiple endpoints.
  • The new methods address limitations of prior single-point null hypothesis tests.
  • Simulations demonstrate the statistical performance of the developed tests.
  • Successful application in an osteoporosis trial confirms practical utility.

Conclusions:

  • The novel multivariate tests offer advancements in comparing treatments with multiple outcomes.
  • These methods provide a more flexible framework for superiority testing in clinical research.
  • The study validates the practical relevance of these statistical innovations.