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Adjusted win ratio with stratification: Calculation methods and interpretation.

Samvel B Gasparyan1, Folke Folkvaljon1, Olof Bengtsson1

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|July 30, 2020
PubMed
Summary
This summary is machine-generated.

The win ratio method offers a robust way to compare ordinal data distributions. This study unifies win ratio estimation with stratification and numeric adjustment, providing interpretable treatment effect measures.

Keywords:
Cochran–Mantel–Haenszel testFligner–Policello testHodges–Lehmann estimatorKansas City cardiomyopathy questionnaireWilcoxon testWin ratioadjustmentclinical trialdapagliflozin in patients with heart failure and reduced ejection fractionestimandheart failureintercurrent eventlocation testmissing datanumber needed to treatpatient reported outcomerank analysisrank analysis of covariancestratificationsymptom scorevan Elteren testwin probability

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Area of Science:

  • Statistics
  • Biostatistics
  • Non-parametric statistics

Background:

  • The win ratio is a flexible method for comparing distributions of two independent, ordinal random variables.
  • Existing methods may require specific distributional assumptions or lack interpretability in complex settings.

Purpose of the Study:

  • To develop a unified theory for win ratio estimation incorporating stratification and adjustment by a numeric variable.
  • To compare the performance and properties of win ratio tests with established non-parametric tests.

Main Methods:

  • Building upon the crude win ratio estimate, the study develops methods for stratified and adjusted win ratio estimation.
  • Comparison of win ratio tests with Wilcoxon rank-sum, Fligner-Policello, van Elteren, regression on ranks, and rank analysis of covariance tests.

Main Results:

  • The unified win ratio estimation theory is presented, handling stratification and numeric adjustment.
  • Win ratio tests are shown to be comparable to well-known non-parametric tests under minimal assumptions.
  • The win ratio provides an interpretable measure of treatment effect.

Conclusions:

  • The win ratio offers a powerful and interpretable tool for comparing distributions in the presence of stratification and covariates.
  • Win ratio tests provide a valid and flexible alternative to traditional non-parametric tests for detecting group differences.
  • This unified approach enhances the applicability of the win ratio in various statistical analyses.