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Application of Standardization for Causal Inference in Observational Studies: A Step-by-step Tutorial for Analysis

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Summary

This study explains standardization for estimating causal effects in epidemiology. It provides a tutorial using R, detailing risk differences, ratios, and subgroup analysis for better causal inference.

Keywords:
CausalityConfounding factorsEpidemiologyObservational studyStandardization

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Research Methods

Background:

  • Epidemiological studies aim to determine the causal impact of exposures on health outcomes.
  • Standardization is a key method for estimating causal effects, but practical guidance is often lacking.
  • Existing explanations may not be user-friendly for implementing standardization.

Purpose of the Study:

  • To provide a user-centric explanation of the standardization method for causal estimand estimation.
  • To demonstrate standardization using basic R functions and the stdReg package.
  • To offer a tutorial on calculating causal risk differences, ratios, and odds ratios, including subgroup analysis.

Main Methods:

  • Explanation of the standardization method for causal inference.
  • Implementation of standardization using fundamental R functions.
  • Utilizing the R package stdReg for consistent procedural application.
  • Step-by-step tutorial for estimating causal risk differences, risk ratios, and odds ratios.
  • Detailed guidance on conducting subgroup analyses.

Main Results:

  • Demonstrated how to estimate causal risk differences, risk ratios, and odds ratios using standardization in R.
  • Provided clear, step-by-step instructions for practical implementation.
  • Illustrated the connection between basic R functions and the stdReg package.
  • Showcased methods for performing subgroup analyses within the standardization framework.

Conclusions:

  • Standardization is an accessible method for causal effect estimation in epidemiology.
  • The tutorial facilitates the practical application of standardization using R.
  • This work enhances the understanding and implementation of causal inference techniques, particularly subgroup analysis.