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Related Experiment Videos

Regression modeling with recurrent events and time-dependent interval-censored marker data.

Eric Bingshu Chen1, Richard J Cook

  • 1Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West Waterloo, Ontario, Canada N2L 3G1. b3chen@uwaterloo.ca

Lifetime Data Analysis
|December 3, 2003
PubMed
Summary

This study models the relationship between bone lesions and skeletal complications in breast cancer patients. It uses statistical methods to analyze how new lesions impact complication rates, accounting for detection delays.

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Area of Science:

  • Biostatistics
  • Oncology
  • Epidemiology

Background:

  • Chronic disease studies often examine relationships between marker and response processes.
  • Understanding pathophysiology and evaluating surrogate outcomes are key interests.
  • Joint models are increasingly used for complex disease progression analysis.

Purpose of the Study:

  • To assess the impact of new bone lesions on skeletal complication incidence in breast cancer patients.
  • To develop and apply statistical methods for analyzing time-dependent covariates in point process models.
  • To address interval-censoring in marker process data.

Main Methods:

  • Utilized a point process model for skeletal complications.
  • Incorporated bone lesion onset as an internal time-dependent covariate.

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  • Employed a modified Expectation-Maximization (EM) algorithm to handle interval-censored data.

    • Main Results:

    • Developed a regression framework to quantify the association between bone lesions and skeletal complications.
    • Demonstrated the utility of the proposed methods in a breast cancer metastasis trial.
    • Successfully managed interval-censored data for the marker process.

    Conclusions:

    • The developed methods provide a robust approach to understanding disease progression when markers are intermittently observed.
    • This research offers insights into the pathophysiology of skeletal complications in metastatic breast cancer.
    • Statistical modeling can effectively link marker processes to clinical outcomes in chronic diseases.