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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Covariate measurement error correction methods in mediation analysis with failure time data.

Shanshan Zhao1, Ross L Prentice

  • 1Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A.

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|August 21, 2014
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Summary

Measurement error can hinder mediation analysis. This study develops correction methods for mediator measurement error in failure time outcomes, improving understanding of causal mechanisms.

Keywords:
Cox modelMean-variance estimating functionsMeasurement errorMediation analysisRegression calibration

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Causal Inference

Background:

  • Mediation analysis identifies mechanisms linking exposure to outcome.
  • Measurement error in mediators can bias these analyses, particularly with time-to-event data.
  • Existing methods often fail to adequately address mediator measurement error in survival analysis.

Purpose of the Study:

  • To develop and evaluate methods for correcting measurement error in mediators within survival mediation analysis.
  • To address broad definitions of measurement error, including technical and temporal variations.
  • To enhance the accuracy of causal mechanism assessment in observational studies.

Main Methods:

  • Proposed regression calibration approaches (mean-variance and follow-up time) to approximate partial likelihood for induced hazard.
  • Extended methods for multiple biomarkers and case-cohort/nested case-control designs.
  • Utilized Cox proportional hazards models for the underlying 'true' mediator.

Main Results:

  • Simulation studies demonstrated the value of the proposed correction methods.
  • The methods successfully assessed mediation effects despite measurement error.
  • The approach was generalized to complex study designs and multiple mediators.

Conclusions:

  • Developed novel statistical methods to correct for mediator measurement error in failure time survival mediation analysis.
  • These methods improve the reliability of assessing causal pathways in epidemiological research.
  • Applied to Women's Health Initiative data to analyze hormone therapy, sex hormones, and breast cancer risk.