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Related Concept Videos

The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.
Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

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Wald-Wolfowitz Runs Test II

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Related Experiment Video

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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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A SAS macro for a clustered logrank test.

Margaret R Stedman1, David R Gagnon, Robert A Lew

  • 1Harvard Medical School, Boston, MA, United States. mstedman2@partners.org

Computer Methods and Programs in Biomedicine
|April 19, 2011
PubMed
Summary
This summary is machine-generated.

The clustered logrank test offers nonparametric significance testing for correlated survival data, particularly in cluster randomized trials. A new SAS macro is presented for implementing this test when entire clusters are randomized.

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Published on: October 23, 2020

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Survival Analysis

Background:

  • Correlated survival data presents unique statistical challenges.
  • Nonparametric methods are crucial for analyzing such data without distributional assumptions.
  • Cluster randomized trials require specialized statistical approaches due to data dependency.

Purpose of the Study:

  • To introduce a SAS macro for the 2-sample clustered logrank test.
  • To provide a practical tool for analyzing correlated survival data in cluster randomized trials.
  • To detail the theoretical underpinnings and application of the clustered logrank test.

Main Methods:

  • Development of a SAS macro implementing the 2-sample clustered logrank test.
  • Focus on scenarios where entire clusters are randomized to treatment or control.
  • Description of the underlying statistical theory and practical implementation.

Main Results:

  • A functional SAS macro for the 2-sample clustered logrank test is available.
  • The macro facilitates the analysis of survival data from cluster randomized trials.
  • Demonstration of the test's applicability to correlated survival data.

Conclusions:

  • The developed SAS macro provides a valuable resource for researchers analyzing clustered survival data.
  • This tool simplifies the application of the clustered logrank test in practice.
  • The macro supports robust statistical inference in cluster randomized trial settings.