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Regression analysis of current status data with latent variables.

Chunjie Wang1, Bo Zhao2,3, Linlin Luo2

  • 1The School of Mathematics and Statistics, Changchun University of Technology, Changchun, 130012, China. wangchunjie@ccut.edu.cn.

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|April 25, 2021
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Summary

This study introduces a new regression analysis for current status data with latent variables. The method enhances understanding of covariate effects in complex health and financial datasets.

Keywords:
Additive hazard modelCorrected estimating equationsCurrent status dataFactor analysisLatent variables

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Current status data are prevalent across diverse fields like epidemiology, finance, and sociology.
  • Analyzing such data, especially with unobserved latent variables, presents unique statistical challenges.
  • Existing methods may not fully capture the complexities introduced by latent structures in current status data.

Purpose of the Study:

  • To develop a robust regression analysis framework for current status data incorporating latent variables.
  • To model latent variables using multiple surrogates via a factor analytic approach.
  • To investigate covariate effects on event hazards using an additive hazard model.

Main Methods:

  • A novel estimation procedure combining the expectation-maximization algorithm with correlated estimating equations.
  • Development of a borrow-strength estimation technique tailored for current status data with latent variables.
  • Theoretical establishment of the consistency and asymptotic normality of the proposed estimators.

Main Results:

  • The proposed method effectively handles current status data with latent variables.
  • The borrow-strength estimation procedure demonstrates favorable performance in simulations.
  • Consistency and asymptotic normality of estimators provide theoretical validation for the approach.

Conclusions:

  • The developed methodology offers a powerful tool for analyzing complex current status data in various scientific domains.
  • The approach is validated through simulation studies and demonstrated on a real-world chronic kidney disease dataset.
  • This work contributes to advancing statistical methods for handling latent variables in survival analysis.