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Augmented likelihood for incorporating auxiliary information into left-truncated data.

Yidan Shi1, Leilei Zeng2, Mary E Thompson1

  • 1Department of Statistics and Actuarial Science, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada.

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|May 28, 2021
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Summary

This study introduces a new statistical method to address left-truncation in time-to-event data using auxiliary information. The approach enhances estimation efficiency and reduces bias by employing a Monte-Carlo Expectation-Maximization algorithm.

Keywords:
Augmented likelihoodAuxiliary informationLeft-truncationTime-to-event data

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Time-to-event data frequently exhibit left-truncation, potentially causing bias and reduced estimation efficiency.
  • Auxiliary data from similar cohorts can be valuable but present challenges in data accessibility and handling diverse sampling conditions.

Purpose of the Study:

  • To develop a likelihood-based method for incorporating auxiliary data to overcome left-truncation in time-to-event analyses.
  • To improve the efficiency and reduce bias in statistical estimations when dealing with left-truncated data.

Main Methods:

  • A novel likelihood-based approach is proposed to integrate information from auxiliary data sources.
  • A one-step Monte-Carlo Expectation-Maximization algorithm is utilized to compute an augmented likelihood.
  • Pseudo-datasets are generated to extend the observed sample's characteristics and conditions.

Main Results:

  • The developed method effectively addresses the left-truncation issue in time-to-event data.
  • Incorporating auxiliary data through the proposed method leads to improved estimation efficiency.
  • Simulation studies and real-world data analysis demonstrate the method's validity and performance.

Conclusions:

  • The introduced likelihood-based method offers a robust solution for left-truncated time-to-event data by leveraging auxiliary information.
  • The Monte-Carlo Expectation-Maximization algorithm provides a practical computational framework for the proposed statistical approach.
  • This work contributes to more accurate and efficient survival analysis, particularly when dealing with complex sampling conditions.