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 Experiment Videos

Planning and analyzing adaptive group sequential survival trials.

Gernot Wassmer1

  • 1Institute for Medical Statistics, Informatics, and Epidemiology, University of Cologne, Cologne, Germany. gernot.wassmer@uni-koeln.de

Biometrical Journal. Biometrische Zeitschrift
|September 16, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

A Note on "A Comparison of Two Methods for Adaptive Multi-Arm Two-Stage Design".

Statistics in medicine·2026
Same author

Clinical trials with interim analyses: standardizing terminology to increase clarity.

Trials·2025
Same author

Two-stage group-sequential designs with delayed responses - what is the point of applying corresponding methods?

BMC medical research methodology·2024
Same author

Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.

Statistics in biopharmaceutical research·2021
Same author

Nested combination tests with a time-to-event endpoint using a short-term endpoint for design adaptations.

Pharmaceutical statistics·2019
Same author

Recent advances in methodology for clinical trials in small populations: the InSPiRe project.

Orphanet journal of rare diseases·2018
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

This paper presents a flexible statistical approach for clinical trials that measure time-to-event outcomes. By using the inverse normal method, researchers can adjust trial parameters, such as the total number of participants or events, while the study is ongoing. This flexibility helps maintain the statistical power of a trial even when initial assumptions about the treatment effect prove inaccurate. The authors provide methods for calculating accurate confidence intervals and p-values throughout the study. This approach is available in specialized software to assist trial designers.

Area of Science:

  • Biostatistics and clinical trial methodology
  • Adaptive group sequential survival trials within medical research

Background:

Clinical researchers often face uncertainty regarding the total number of events required for survival studies. Standard group sequential designs rely on error spending functions to manage interim looks at data. These traditional frameworks struggle when the anticipated information accrual deviates from initial projections. No prior work had resolved how to flexibly modify design parameters during ongoing survival investigations. That uncertainty drove interest in adaptive strategies for adjusting trial features mid-study. Prior research has shown that rigid designs may lead to underpowered studies if assumptions are incorrect. This gap motivated the development of methods allowing for mid-trial adjustments based on observed data. The current literature increasingly explores how these adaptive tools integrate with survival analysis techniques.

Purpose Of The Study:

The aim of this paper is to present the inverse normal method for conducting adaptive group sequential survival trials. Researchers often encounter difficulties when the information available at interim stages does not match initial expectations. This study addresses the need for flexible design parameters that can be adjusted during the course of a trial. The authors seek to demonstrate how this method allows for modifications while maintaining statistical rigor. They also intend to provide clear procedures for calculating confidence intervals and p-values. Furthermore, the work explores how to rescue studies that might otherwise fail due to insufficient statistical power. By defining these techniques, the authors provide a framework for more versatile clinical research. The study also introduces software tools to facilitate the implementation of these adaptive designs.

Keywords:
clinical trial designstatistical powerhazard ratio estimationlog-rank testADDPLAN software

Frequently Asked Questions

The researchers propose using the inverse normal method to combine interim and final data. This technique allows for adjustments to the maximum information or other design parameters based on observed interim results, unlike standard error spending approaches which are restricted to fixed information accrual.

The ADDPLAN software serves as the primary computational tool for implementing these adaptive designs. It facilitates the calculation of p-values, confidence intervals, and median unbiased estimates, which are otherwise complex to derive manually during an ongoing trial.

A log-rank test is necessary to compare two survival functions within this framework. This statistical test provides the basis for the inverse normal combination, ensuring that the trial maintains validity while allowing for mid-study modifications.

Related Experiment Videos

Main Methods:

Review Approach framing involves evaluating the inverse normal method for survival analysis. The authors examine how this technique integrates with the log-rank test for comparing two survival functions. They investigate the properties of these analyses by comparing them against alternative statistical approaches. The study details the specification of effect estimates within an adaptive framework. Researchers define procedures for calculating confidence intervals at both interim and final stages. They also describe the derivation of median unbiased estimates for the hazard ratio. The team implements these statistical procedures within the ADDPLAN software environment. This approach ensures that the proposed methods are accessible for practical application in clinical trial planning.

Main Results:

Key Findings From the Literature indicate that the inverse normal method effectively facilitates adaptive modifications in survival trials. The authors show that this technique coincides with standard analysis strategies when no design changes occur. They provide specific formulas for calculating confidence intervals for the hazard ratio at each trial stage. The study establishes that median unbiased estimates are obtainable through end-of-trial computations. Results suggest that this method can successfully rescue studies that are initially underpowered. The researchers demonstrate that this approach supports broader changes to design parameters than traditional error spending functions. Comparisons with alternative approaches confirm the robustness of the proposed analysis techniques. The implementation in ADDPLAN software provides a validated tool for applying these adaptive methods in practice.

Conclusions:

The authors demonstrate that the inverse normal method provides a robust framework for adaptive survival trials. This approach allows for significant design modifications while maintaining statistical validity throughout the study. Researchers can effectively rescue underpowered trials by adjusting parameters based on interim information. The proposed technique yields reliable confidence intervals for the hazard ratio at various stages. Median unbiased estimates are achievable using the methods described for end-of-trial calculations. Overall p-values remain consistent with standard analysis strategies when no modifications occur. The study highlights that this flexibility supports broader changes in trial design compared to traditional methods. These findings offer a practical pathway for implementing adaptive strategies in survival research.

The authors utilize hazard ratio data to construct confidence intervals. They distinguish between intervals computed at interim stages and those calculated only upon trial completion, noting that the latter enables the derivation of median unbiased estimates.

The researchers measure the impact of design modifications on statistical power. They demonstrate that the proposed technique can rescue studies that would otherwise be underpowered, contrasting this with fixed-sample designs that cannot adapt to lower-than-expected event rates.

The authors claim that this method opens the way to diverse design changes. They suggest that the flexibility afforded by the inverse normal approach enables researchers to adapt trial parameters more broadly than previously possible.