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Related Experiment Videos

Survivor bias in Mendelian randomization analysis.

Stijn Vansteelandt1,2, Oliver Dukes1, Torben Martinussen3

  • 1Department of Applied Mathematics, Computer Sciences and Statistics, Ghent University, Krijgslaan 281 (S9), Gent, Belgium.

Biostatistics (Oxford, England)
|October 14, 2017
PubMed
Summary

Mendelian randomization studies can produce biased results in older adults due to survivor bias. New methods can correct this bias, providing more accurate insights into genetic exposures and mortality risk.

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Area of Science:

  • Epidemiology
  • Statistical Genetics
  • Biostatistics

Background:

  • Mendelian randomization (MR) uses genetic variants as instrumental variables to estimate causal effects of exposures on outcomes.
  • Standard MR analyses often overlook left truncation in elderly populations, potentially invalidating core assumptions.
  • Left truncation, or survivor bias, occurs when participants are selected based on survival to study enrollment.

Purpose of the Study:

  • To investigate the impact of left truncation on MR studies in adult and elderly populations.
  • To develop and evaluate methods for correcting survivor bias in MR analyses of mortality.
  • To re-examine the association between vitamin D and all-cause mortality using corrected MR methods.

Main Methods:

  • We theoretically demonstrate how left truncation biases MR effect estimates when genotypes influence mortality.

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  • We propose novel semi-parametric additive hazard models to adjust for truncation bias.
  • Simulation studies were conducted to assess the performance of the proposed methods.

    • Main Results:

    • Ignoring left truncation leads to biased exposure effect estimates in MR studies of mortality.
    • Standard causal null hypothesis tests in MR remain unbiased despite left truncation.
    • The proposed methods successfully corrected for survivor bias in simulation and a real-world case study.

    Conclusions:

    • Survivor bias is a critical issue in MR studies of mortality in older adults, invalidating standard instrumental variable assumptions.
    • The developed semi-parametric additive hazard models offer a robust solution to mitigate truncation bias.
    • Applying these methods to the Monica10 study provides a less biased estimate of vitamin D's effect on mortality.