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Related Concept Videos

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
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Distributions to Estimate Population Parameter01:26

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Updated: May 22, 2026

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
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Published on: June 10, 2025

Cutpoint selection for discretizing a continuous covariate for generalized estimating equations.

Gisela Tunes-da-Silva1, John P Klein

  • 1Department of Statistics, University of São Paulo, São Paulo, São Paulo, Brazil.

Computational Statistics & Data Analysis
|May 29, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a method for determining the optimal cutpoint in continuous covariates for regression analysis. It ensures accurate significance testing when identifying the best threshold for predicting transplant outcomes.

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Area of Science:

  • Biostatistics
  • Regression Analysis
  • Clinical Data Science

Background:

  • Regression analysis often requires dichotomizing continuous covariates.
  • Determining the optimal cutpoint for this dichotomization is crucial but challenging.
  • Standard significance testing can be compromised by the multiple testing involved in cutpoint selection.

Purpose of the Study:

  • To develop and illustrate a method for estimating the optimal cutpoint of a continuous covariate in regression.
  • To test the hypothesis that a dichotomized covariate significantly impacts the outcome.
  • To adjust significance tests for multiple testing in cutpoint estimation.

Main Methods:

  • Generalized estimation approach for regression analysis.
  • Estimation of the optimal cutpoint for a continuous covariate.
  • Hypothesis testing for the significance of the dichotomized covariate.
  • Type-I error rate adjustment for multiple testing.

Main Results:

  • The proposed method allows for the estimation of an optimal cutpoint.
  • Significance testing with adjusted error rates can identify impactful dichotomized covariates.
  • The technique was applied to hematopoietic stem cell transplantation data.

Conclusions:

  • Accurate cutpoint estimation and adjusted significance testing are vital in regression.
  • This approach can identify critical thresholds, such as the CD34 cell dose, affecting transplant outcomes.
  • The methods provide a robust framework for analyzing continuous covariates in clinical research.