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Comparison of Instrumental Variable Methods With Continuous Exposure and Binary Outcome: A Simulation Study.

Shunichiro Orihara1, Atsushi Goto2

  • 1Department of Health Data Science, Tokyo Medical University.

Journal of Epidemiology
|April 21, 2024
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Summary
This summary is machine-generated.

Instrumental variable (IV) methods for binary outcomes show varying biases. No single method is superior; using multiple IV methods for primary and sensitivity analyses is recommended for robust causal effect estimation.

Keywords:
inverse-variance weighted methodlimited information maximum likelihoodtwo-stage least squaretwo-stage residual inclusionweak instrument bias

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Instrumental variable (IV) methods are crucial for estimating causal effects in the presence of unmeasured confounding.
  • Existing comparisons of IV methods for binary outcomes are insufficient, necessitating further evaluation.

Purpose of the Study:

  • To conduct a detailed simulation study comparing six IV methods for binary outcomes.
  • To evaluate method performance under varying instrument strengths and effect homogeneity.

Main Methods:

  • Compared six IV methods: 2SLS, 2SPS, 2SRI, LIML, IVW_LI, and IVW_LL across 32 simulation scenarios.
  • Assessed parameter estimates, causal risk ratios, and causal risk differences.

Main Results:

  • IV methods grouped into three performance categories based on bias and instrument strength.
  • 2SLS/IVW_LI showed bias due to model misspecification; 2SRI/2SPS performed well with strong IVs but biased with weak IVs.
  • LIML/IVW_LL were conservative and less affected by weak IV issues.

Conclusions:

  • No single IV method is universally optimal for binary outcomes.
  • Recommend employing multiple IV methods for primary and sensitivity analyses to ensure reliable causal effect estimation.