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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

636
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
636
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

644
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
644
Crossover Experiments01:16

Crossover Experiments

4.6K
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
4.6K
Clinical Trials: Overview01:11

Clinical Trials: Overview

5.1K
Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
5.1K
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

242
Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
242
Bioavailability Study Design: Single Versus Multiple Dose Studies01:11

Bioavailability Study Design: Single Versus Multiple Dose Studies

256
Bioavailability studies are essential for understanding how a drug is absorbed, distributed, metabolized, and excreted in the body. These studies assess the extent and rate at which the active pharmaceutical agent becomes available at the site of action. The design of bioavailability studies can involve single-dose or multiple-dose regimens, each with distinct advantages and limitations.Single-dose studies are the preferred approach due to their simplicity and reduced drug exposure for...
256

You might also read

Related Articles

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

Sort by
Same author

Plant Kelch phosphatases are Ser/Thr phosphatases involved in cell cycle regulation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Effect of vitamin C and hesperidin on serum uric acid concentrations in healthy adults with high uric acid levels: the randomized controlled 'HesperidrinC trial'.

European journal of nutrition·2026
Same author

The role of statistical power in context: implications for regulatory practices.

Integrated environmental assessment and management·2026
Same author

Multiple Contrast Tests for Count Data: Small Sample Approximations and Their Limitations.

Biometrical journal. Biometrische Zeitschrift·2025
Same author

A 3-Week Inpatient Rehabilitation Programme Improves Body Composition in People with Cystic Fibrosis with and Without Elexacaftor/Tezacaftor/Ivacaftor Therapy.

Nutrients·2025
Same author

Analysis of Covariance in General Factorial Designs Through Multiple Contrast Tests Under Variance Heteroscedasticity.

Statistics in medicine·2025

Related Experiment Video

Updated: Feb 19, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.4K

Multi-arm trials with multiple primary endpoints and missing values.

Mario Hasler1, Ludwig A Hothorn2

  • 1Lehrfach Variationsstatistik, Christian-Albrechts-University of Kiel, Kiel, Germany.

Statistics in Medicine
|November 7, 2017
PubMed
Summary

This study extends multiple contrast tests for multiple endpoints to handle missing data. It maintains strong familywise error control and increases statistical power compared to complete case analysis.

Keywords:
correlated endpointsmissing valuesmultiple contrast testsmultiplicity adjustmentmultivariate t distribution

More Related Videos

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
04:53

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

11.3K
Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study
03:05

Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study

Published on: November 21, 2025

659

Related Experiment Videos

Last Updated: Feb 19, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.4K
A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
04:53

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

11.3K
Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study
03:05

Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study

Published on: November 21, 2025

659

Area of Science:

  • Biostatistics
  • Statistical Methods
  • Clinical Trial Design

Background:

  • Handling missing data is crucial in clinical trials with multiple endpoints.
  • Existing methods for multiple endpoints may struggle with missing data, potentially compromising statistical validity.
  • Ensuring robust statistical inference requires methods that account for data complexities.

Purpose of the Study:

  • To develop and present an extension of multiple contrast tests for multiple endpoints that effectively addresses missing values.
  • To maintain strong familywise error rate (FWER) control in the presence of missing data.
  • To enhance statistical power by utilizing all available observational data.

Main Methods:

  • The study extends multiple contrast tests for multiple endpoints to scenarios with missing values.
  • Endpoints are assumed to be normally distributed, correlated, and possess equal covariance matrices across treatments.
  • Multivariate t-distributions with endpoint-specific degrees of freedom are applied.

Main Results:

  • The proposed method maintains the familywise error rate type I in the strong sense within an admissible range.
  • It effectively avoids issues related to differing marginal errors type I.
  • The approach exploits all available observations, leading to increased statistical power over complete case analysis.

Conclusions:

  • This extension provides a statistically sound and powerful method for analyzing multiple endpoints with missing data.
  • It offers an advantage over traditional complete case analysis by preserving statistical power.
  • The method ensures robust control of Type I errors, crucial for reliable clinical trial outcomes.