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On a preference-based instrumental variable approach in reducing unmeasured confounding-by-indication.

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Group preferences can serve as valid instruments in instrumental variable (IV) analyses to control for unmeasured confounding. However, careful consideration of between-group and within-group confounding is crucial for accurate results.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Instrumental variable (IV) analyses commonly use treatment preferences of groups, such as clinical centers, to address unmeasured confounding-by-indication.
  • Formal evaluations of these group-preference-based instruments are scarce, despite their frequent proposal.
  • Existing challenges include outcome correlations within groups, the multi-value nature of instruments, and confounding within and between groups.

Purpose of the Study:

  • To introduce a framework for assessing the assumptions of group-preference-based instrumental variable analyses.
  • To evaluate the validity of group-preference-based instruments under different confounding scenarios.
  • To derive and assess the asymptotic bias of the two-stage generalized ordinary least squares estimator.

Main Methods:

  • Development of a framework distinguishing between-group and within-group confounding.
  • Derivation of a closed-form expression for the asymptotic bias of the two-stage generalized ordinary least squares estimator.
  • Monte Carlo simulations to evaluate the bias formula's performance in finite samples.

Main Results:

  • Group-preference-based IVs can be valid when unmeasured confounding is confined within groups, not between them.
  • The derived asymptotic bias formula accurately approximates finite-sample bias, especially with a moderate to large number of groups.
  • IV estimators remain biased with finite cluster sizes but offer advantages in reducing confounding-by-indication bias.

Conclusions:

  • The study provides practical guidance for enhancing the performance and validity of preference-based IV analyses.
  • Recommendations include adjusting for measured confounders, selecting groups with high treatment assignment variation, and increasing cluster size.
  • Minimizing unmeasured between-group confounding is essential for the validity of these instrumental variables.