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Related Concept Videos

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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 Cox...
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

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...
Crossover Experiments01:16

Crossover Experiments

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.
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Bioavailability Study Design: Single Versus Multiple Dose Studies01:11

Bioavailability Study Design: Single Versus Multiple Dose Studies

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...
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Bioequivalence of Drugs: Drugs with Multiple Indications

The concept of therapeutic equivalence (TE) in drugs with multiple indications is complex. A generic drug may be therapeutically equivalent to a brand-name product for one specific indication, but this doesn't necessarily mean it's equivalent for all other indications. Evidence of TE in one patient group and bioequivalence shown in healthy volunteers can support—but not confirm—TE for other indications. However, definitive proof requires individual clinical studies for each indication due to...

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Related Experiment Video

Updated: May 16, 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

A group sequential Holm procedure with multiple primary endpoints.

Yining Ye1, Ai Li, Lingyun Liu

  • 1Global Biostatistical Sciences, Amgen Inc., South San Francisco, CA, USA. yye@amgen.com

Statistics in Medicine
|December 15, 2012
PubMed
Summary
This summary is machine-generated.

We introduce a group sequential Holm procedure for multiple primary endpoints, controlling the familywise error rate across interim analyses. This powerful method simplifies to a weighted Holm procedure without interim analyses and outperforms parallel group sequential designs.

Related Experiment Videos

Last Updated: May 16, 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

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methods

Background:

  • Managing multiple primary endpoints in clinical trials presents statistical challenges.
  • Group sequential designs allow for interim analyses but require careful multiplicity control.
  • Existing methods may lack power or flexibility when dealing with both multiple endpoints and interim analyses.

Purpose of the Study:

  • To propose a novel group sequential Holm procedure for clinical trials with multiple primary endpoints.
  • To ensure strong familywise error rate control in the presence of multiple endpoints and interim analyses.
  • To develop a more powerful and flexible statistical approach compared to existing methods.

Main Methods:

  • The proposed method integrates the Holm procedure with group sequential design principles.
  • It functions as a closed testing procedure, maintaining strong familywise error rate control.
  • The procedure simplifies to a weighted Holm procedure when no interim analyses are performed.

Main Results:

  • The group sequential Holm procedure effectively addresses multiplicity from both multiple endpoints and multiple analyses.
  • It preserves the familywise error rate in the strong sense.
  • The proposed method demonstrates greater statistical power than the parallel group sequential method.

Conclusions:

  • The group sequential Holm procedure offers a robust and powerful statistical framework for trials with multiple primary endpoints and interim analyses.
  • It provides an alternative to fixed sequence testing, eliminating the need to pre-specify testing order.
  • This method enhances statistical efficiency while maintaining rigorous error rate control.