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Empirical Sandwich Variance Estimator for Iterated Conditional Expectation g-Computation.

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Summary
This summary is machine-generated.

Iterated conditional expectation (ICE) g-computation offers a new way to handle time-varying confounders. The empirical sandwich variance estimator provides a faster and viable alternative for variance estimation in ICE g-computation.

Keywords:
M‐estimationestimating equationsg‐computationg‐formulatime‐varying confounding

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Time-varying confounding presents challenges in longitudinal and time-to-event data analysis.
  • Existing g-computation methods often require specifying complex models for time-varying covariates.
  • Bootstrapping for variance estimation in ICE g-computation can be computationally intensive.

Purpose of the Study:

  • To present Iterated Conditional Expectation (ICE) g-computation using stacked estimating equations.
  • To introduce the empirical sandwich variance estimator as an efficient alternative for variance estimation in ICE g-computation.
  • To evaluate the performance and computational efficiency of the proposed variance estimator.

Main Methods:

  • Formulated ICE g-computation as a set of stacked estimating equations.
  • Applied the empirical sandwich variance estimator for variance estimation.
  • Conducted a simulation study to assess the performance of the variance estimator.
  • Demonstrated the approach using an example of smoking and hypertension.

Main Results:

  • The empirical sandwich variance estimator consistently estimated variance in simulations.
  • The sandwich estimator was substantially faster than bootstrapping in the applied example.
  • ICE g-computation avoids the need for covariate-specific models, simplifying analysis.

Conclusions:

  • The empirical sandwich variance estimator is a computationally efficient and viable option for variance estimation with ICE g-computation.
  • This approach simplifies the analysis of time-varying confounding in longitudinal and time-to-event data.
  • The proposed method offers a practical advancement for causal inference in observational studies.