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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
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RMST-based multiple contrast tests in general factorial designs.

Merle Munko1, Marc Ditzhaus1, Dennis Dobler2

  • 1Department of Mathematics, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.

Statistics in Medicine
|February 25, 2024
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Summary
This summary is machine-generated.

This study extends permutation tests for restricted mean survival time (RMST) to complex designs and develops multiple testing procedures. These methods offer reliable survival analysis without the restrictive proportional hazards assumption.

Keywords:
factorial designmultiple testingresamplingrestricted mean survival timesurvival analysis

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Methodology

Background:

  • Proportional hazards assumption is often restrictive in survival analysis.
  • Restricted Mean Survival Time (RMST) is a desirable, assumption-free estimand.
  • Existing RMST tests have limitations in complex designs and multiple comparisons.

Purpose of the Study:

  • Extend permutation tests for RMST to general factorial designs and contrast hypotheses.
  • Develop multiple testing procedures for RMST to identify specific group differences.
  • Evaluate the performance of proposed methods in simulations and real data.

Main Methods:

  • Utilized a Wald-type test statistic and its asymptotic behavior for extended permutation tests.
  • Incorporated a groupwise bootstrap approach for RMST analysis.
  • Developed multiple RMST tests leveraging the asymptotic dependence structure for enhanced power.

Main Results:

  • Proposed permutation and bootstrap tests demonstrate improved performance in complex survival data analysis.
  • Multiple testing procedures effectively identify specific group differences in RMST.
  • Simulations confirm the validity and power of the developed global and multiple testing procedures.

Conclusions:

  • The extended permutation tests and groupwise bootstrap offer robust alternatives to assumption-laden survival analysis methods.
  • The developed multiple testing framework provides a powerful tool for post-hoc analysis of RMST differences.
  • These advancements enhance the practical application of RMST in comparing survival outcomes across multiple groups.