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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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ADDIS-Graphs for Online Error Control With Application to Platform Trials.

Lasse Fischer1, Marta Bofill Roig2, Werner Brannath1

  • 1Competence Center for Clinical Trials Bremen, University of Bremen, Bremen, Germany.

Biometrical Journal. Biometrische Zeitschrift
|September 28, 2025
PubMed
Summary
This summary is machine-generated.

We introduce ADDIS-Graphs, a flexible new method for online error control in studies like platform trials. This approach improves statistical power and adaptivity for testing multiple hypotheses efficiently.

Keywords:
false discovery ratefamilywise error rategraphical testing proceduresonline multiple testingplatform trials

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Area of Science:

  • Statistical methodology
  • Biostatistics
  • Clinical trial design

Background:

  • Online error control is crucial for sequential hypothesis testing, managing familywise error rate (FWER) or false discovery rate (FDR).
  • Existing methods are often rigid, designed for large-scale studies, and lack flexibility for smaller, adaptive trials like platform trials.
  • Platform trials face unique challenges including dependent p-values due to shared control arms and the need for prespecified significance levels.

Purpose of the Study:

  • To propose a novel, flexible, and interpretable graphical method for online error control in sequential hypothesis testing.
  • To address the limitations of existing methods in smaller studies and platform trial settings.
  • To enhance statistical power and adaptivity in hypothesis testing while maintaining strict error control.

Main Methods:

  • Introduction of adaptive-discarding-Graphs (ADDIS-Graphs) for familywise error rate (FWER) control.
  • Development of extensions to ADDIS-Graphs, incorporating information on the joint distribution of p-values.
  • Creation of a version of ADDIS-Graphs for false discovery rate (FDR) control.

Main Results:

  • ADDIS-Graphs demonstrate provable uniform improvement over state-of-the-art methods.
  • The graphical structure of ADDIS-Graphs allows for perfect adaptation to platform trial settings.
  • Extensions enhance the method's ability to leverage information from dependent p-values and control FDR.

Conclusions:

  • ADDIS-Graphs offer a powerful and flexible solution for online error control in sequential hypothesis testing.
  • The proposed methods are particularly well-suited for adaptive platform trials and similar settings.
  • These advancements provide improved statistical efficiency and interpretability in complex study designs.