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Systematic Error: Methodological and Sampling Errors01:15

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Risk functions with outcome measurement error.

Jessie K Edwards1, Stephen R Cole1, Paul N Zivich1

  • 1Department of Epidemiology, University of North Carolina at Chapel Hill, 135 Dauer Dr., 2101 McGavran-Greenberg Hall CB#7435, Chapel Hill, NC 27510, United States.

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Summary
This summary is machine-generated.

This study introduces a new method to correct for errors in death record data, improving the accuracy of mortality risk and survival estimates in clinical research. The approach enhances reliability for studies using vital statistics registries.

Keywords:
data linkagemortalityoutcome measurement errorssurvival analysis

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Studies linking to vital statistics registries for death ascertainment can suffer from outcome measurement error.
  • This error can lead to biased estimates of mortality risk and survival due to uncaptured deaths, false positives, or incorrect timing of recorded deaths.

Purpose of the Study:

  • To extend the Rogan-Gladen estimator to address outcome measurement error in risk and survival functions, specifically in the presence of right censoring.
  • To apply and evaluate this extended estimator for mortality risk in a cohort of individuals receiving HIV care, accounting for potential linkage errors to death registries.

Main Methods:

  • Developed an extension of the Rogan-Gladen estimator to incorporate outcome measurement error.
  • Applied the method to data from the University of North Carolina Center for AIDS Research HIV Clinical Cohort (2001-2022).
  • Conducted simulation studies to assess the performance of the proposed approach under various conditions, including higher mortality risk in validation subsets.

Main Results:

  • The extended Rogan-Gladen estimator effectively accounts for outcome measurement error in mortality risk and survival estimation.
  • The method demonstrated robust performance in simulations, even with validation samples having higher baseline mortality risk.
  • The approach is flexible, allowing parameterization with internal or external validation data, or use as quantitative bias analysis.

Conclusions:

  • The proposed method provides a valuable tool for correcting bias in mortality estimates caused by vital statistics linkage errors.
  • This technique enhances the accuracy and reliability of survival analysis in epidemiological and clinical research, particularly in cohorts with potential data quality issues.
  • The approach offers a quantitative bias analysis framework for studies relying on registry data for outcome ascertainment.