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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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[Poisson regression use in nephrology].

Cécile Couchoud1,

  • 1Registre REIN, agence de la biomédecine, 1, avenue du Stade-de-France, 93212 Saint-Denis-La Plaine, France; UMR CNRS 5558, laboratoire biostatistique santé, université Claude-Bernard-Lyon I, 43, boulevard du 11 novembre 1918, 69622 Villeurbanne, France.

Nephrologie & Therapeutique
|April 12, 2020
PubMed
Summary
This summary is machine-generated.

Poisson regression offers a robust method for analyzing incidence rates in survival studies, simplifying the assessment of temporal patterns. This approach provides insights into event occurrence speeds, beneficial for clinical interpretation and handling time-dependent variables.

Keywords:
Competing riskEffet dépendant du tempsEpidemiologyPoisson regressionRegistreRegistryRisques concurrentsRégression de PoissonTime-dependant effectTime-dependent variableVariable dépendante du tempsÉpidémiologie

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Traditional survival analysis methods like Kaplan-Meier and Cox models have limitations in analyzing temporal patterns and time-dependent variables.
  • Poisson regression offers an alternative for analyzing incidence rates in cohort survival studies.

Purpose of the Study:

  • To highlight the utility of Poisson regression for analyzing incidence rates in cohort survival studies.
  • To demonstrate how Poisson regression can simplify the analysis of temporal patterns and accommodate time-dependent variables.

Main Methods:

  • Comparing Poisson regression with traditional methods (Kaplan-Meier, logrank test, Cox model) for grouped data analysis.
  • Organizing individual subject data into event-time tables stratified by time and other factors for Poisson regression analysis.

Main Results:

  • Poisson regression provides parallels to grouped data analysis, including instantaneous hazards and hazard ratios.
  • This method allows for the presentation of instantaneous event occurrence speeds, which can be clinically significant.
  • Poisson regression facilitates the inclusion of time-dependent variables and effects, which are not standard in the conventional Cox model.

Conclusions:

  • Poisson regression is a powerful and flexible tool for analyzing incidence rates in cohort survival studies.
  • It simplifies the assessment of temporal patterns and allows for more complex modeling of time-dependent factors.
  • The application of Poisson regression necessitates large-scale databases for detailed analyses with small time intervals or numerous adjustment variables.