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An R-Based Landscape Validation of a Competing Risk Model
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Confidence Estimation via the Parametric Bootstrap in Logistic Joinpoint Regression.

Ryan Gill1, Grzegorz A Rempala, Michal Czajkowski

  • 1Department of Mathematics, University of Louisville, Louisville, KY 40292 USA.

Journal of Statistical Planning and Inference
|February 18, 2010
PubMed
Summary
This summary is machine-generated.

This study analyzes clustered logistic joinpoint models for cancer mortality data. It finds parametric bootstrap confidence bounds can be inconsistent when the joinpoint coincides with observation times.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Joinpoint models are crucial for analyzing trends in health data, such as cancer mortality.
  • Maximum likelihood estimators and parametric bootstrap methods are commonly used for inference in these models.
  • The accuracy of confidence bounds is essential for reliable trend interpretation.

Purpose of the Study:

  • To investigate the asymptotic properties of estimators in a clustered logistic joinpoint model with an unknown joinpoint.
  • To determine conditions for the consistency of parametric bootstrap confidence bounds.
  • To address inconsistencies in bootstrap confidence bounds when the joinpoint aligns with observation times.

Main Methods:

  • Analysis of asymptotic properties of maximum likelihood and related estimators.
  • Derivation of sufficient conditions for the consistency of parametric bootstrap confidence bounds.
  • Simulation studies to evaluate bootstrap performance under different joinpoint scenarios.
  • Development and application of a removal algorithm to correct for joinpoint-observation time issues.

Main Results:

  • Sufficient conditions for consistent confidence bounds were established, contingent on the true joinpoint not being an observation time.
  • Simulation results demonstrated inconsistency of bootstrap confidence bounds when the joinpoint coincided with observation times.
  • A removal algorithm improved consistency but increased mean square error.

Conclusions:

  • The parametric bootstrap method for joinpoint models requires careful consideration of the joinpoint's location relative to observation times.
  • The proposed removal algorithm offers a solution for improved consistency, albeit with a trade-off in efficiency.
  • The methods were successfully applied to analyze US cancer mortality data for individuals aged 65 and over.