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Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

An efficient and exact approach for detecting trends with binary endpoints.

Guogen Shan1, Changxing Ma, Alan D Hutson

  • 1Department of Biostatistics, University at Buffalo, 3435 Main Street, Buffalo, NY 14214, USA.

Statistics in Medicine
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an improved statistical method for analyzing trends in 2 × K contingency tables, offering better power and less conservatism than existing approaches for detecting population proportion differences.

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Area of Science:

  • Statistics
  • Biostatistics
  • Statistical Methods

Background:

  • Nuisance parameters can complicate hypothesis testing, particularly when analyzing trends in categorical data.
  • Exact testing offers a robust method for controlling nuisance parameters, improving the accuracy of statistical inferences.
  • Existing methods for trend testing in contingency tables, like the Cochran-Armitage test, have limitations in controlling type I error and power.

Purpose of the Study:

  • To adapt Lloyd's exact testing approach for controlling nuisance parameters to analyze trends in 2 × K contingency tables.
  • To develop unconditional tests for trend analysis that are more powerful and less conservative than existing methods.
  • To compare the performance of the proposed unconditional procedure against other conditional and unconditional approaches.

Main Methods:

  • Utilized an exact testing framework to develop unconditional tests for trends in 2 × K contingency tables.
  • Employed the Cochran-Armitage test statistic as a basis for comparison with the proposed method.
  • Conducted comparative analyses focusing on type I error rates and statistical power.

Main Results:

  • The proposed unconditional procedure demonstrates superior power properties compared to existing conditional and unconditional methods.
  • The new approach is less conservative, providing more accurate and reliable trend detection.
  • An illustrative example confirmed the practical applicability and advantages of the proposed method.

Conclusions:

  • The adapted exact testing approach provides a preferable method for trend analysis in 2 × K contingency tables.
  • This procedure offers enhanced statistical power and reduced conservatism, leading to more robust findings.
  • The method is valuable for researchers analyzing categorical data where trend detection is crucial.