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A functional inference for multivariate current status data with mismeasured covariate.

Chi-Chung Wen1, Yih-Huei Huang, Yuh-Jenn Wu

  • 1Department of Mathematics, Tamkang University, New Taipei , 25137, Taiwan, ccwen@mail.tku.edu.tw.

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This study introduces a new statistical method to analyze health data with multiple conditions (hyperglycemia, hypertension, hyperlipidemia) and inaccurate body mass index measurements. The approach improves analysis for complex health survey data.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Data Science

Background:

  • Current status failure time data analysis has addressed covariate measurement error, but multivariate extensions remain unexplored.
  • Health surveys often involve multiple correlated health outcomes (e.g., hyperglycemia, hypertension, hyperlipidemia) subject to current status censoring.
  • Covariates like body mass index in health surveys can be prone to self-reporting and measurement error.

Purpose of the Study:

  • To propose a functional inference method for multivariate current status data with mismeasured covariates.
  • To address correlated failure times and covariate measurement error simultaneously in health survey data.
  • To extend existing methods for current status data to a multivariate setting with measurement error.

Main Methods:

  • Utilized a proportional odds model for multivariate current status data.
  • Employed the working independence strategy to handle correlated observations within subjects.
  • Applied the conditional score approach to manage mismeasured covariates without distributional assumptions.
  • Combined Newton-Raphson and self-consistency algorithms for stable computation.

Main Results:

  • Developed a novel statistical framework for analyzing complex health data.
  • Established asymptotic theory for the proposed estimation method.
  • Demonstrated the method's stability and applicability through simulation studies and real-world data analysis (three-hypers dataset).

Conclusions:

  • The proposed functional inference method effectively handles multivariate current status data with mismeasured covariates.
  • The method provides a robust approach for analyzing correlated health outcomes in the presence of covariate error.
  • This work offers a valuable tool for researchers analyzing complex health survey data.