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A cross-validation deletion-substitution-addition model selection algorithm: Application to marginal structural

Thaddeus J Haight1, Yue Wang2, Mark J van der Laan3

  • 1Division of Epidemiology, School of Public Health, University of California-Berkeley, United States.

Computational Statistics & Data Analysis
|December 16, 2014
PubMed
Summary
This summary is machine-generated.

The cross-validation deletion-substitution-addition (cvDSA) algorithm offers data-adaptive methods for selecting and estimating marginal structural models (MSMs) and conditional mean models, improving causal inference in statistical analyses.

Keywords:
Cardiovascular mortalityCross-validationLung functionMachine learningMarginal structural models

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Area of Science:

  • Causal Inference
  • Statistical Modeling
  • Data Science

Background:

  • Marginal structural models (MSMs) are crucial for causal inference, particularly in observational studies.
  • Selecting appropriate models for MSMs and conditional means can be challenging, risking overfitting.
  • Existing methods may lack robust data-adaptive selection and estimation procedures.

Purpose of the Study:

  • To introduce and evaluate the cross-validation deletion-substitution-addition (cvDSA) algorithm for data-adaptive model selection and estimation.
  • To provide R software packages (cvDSA and DSA) for implementing these advanced statistical methodologies.
  • To compare investigator-defined MSMs with those selected by the cvDSA algorithm using real and simulated data.

Main Methods:

  • The cvDSA algorithm employs data-adaptive estimation, user-defined criteria, and loss function-based procedures for model selection.
  • Cross-validation is utilized within the cvDSA algorithm to prevent model overfitting.
  • The R packages support various estimation procedures, including inverse-probability of treatment weighting (IPTW), G-computation, and double robust IPTW.

Main Results:

  • The cvDSA algorithm effectively selects and estimates MSMs and conditional mean models.
  • Analyses demonstrated the utility of cvDSA in comparing assumed versus data-selected causal effect models.
  • The alternative DSA package offers faster computation and enhanced features for conditional mean modeling.

Conclusions:

  • The cvDSA algorithm provides a robust, data-adaptive approach for selecting and estimating statistical models in causal inference.
  • The available R packages facilitate the application of these advanced methods in research.
  • These tools enhance the reliability and accuracy of causal effect estimation in observational studies.