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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Causal graphs (Directed Acyclic Graphs - DAGs) are vital for covariate selection in research.
  • Limited applied research exists on optimal DAG creation methodologies.
  • The Coronary Drug Project trial data was used to evaluate DAG creation approaches.

Purpose of the Study:

  • To assess various approaches for creating DAGs for covariate selection.
  • To evaluate the impact of different DAG creation strategies on estimating causal effects.
  • To determine the best methods for identifying variables and links in DAGs.

Main Methods:

  • Developed multiple DAGs for placebo adherence's effect on mortality.
  • Employed diverse strategies for variable and link inclusion/exclusion in DAGs.
  • Identified minimal adjustment sets for causal effect estimation using each DAG.

Main Results:

  • Using only baseline covariates, all adjustment sets yielded similar (biased) estimates.
  • Including nonconfounding prognostic factors reduced variance without increasing bias.
  • Adjusting for time-varying covariates with inverse probability weighting showed best performance when DAGs focused on prognostic factors.

Conclusions:

  • Empirically confirmed theoretical advice on covariate selection in DAGs.
  • Prognostic factors not predicting exposure can reduce variance; exposure predictors not prognostic may offer less bias control.
  • Recommend prioritizing identification of all potential outcome prognostic factors when hand-creating DAGs.