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Model selection criterion for causal parameters in structural mean models based on a quasi-likelihood.

Masataka Taguri1, Yutaka Matsuyama2, Yasuo Ohashi2

  • 1Department of Biostatistics and Epidemiology, Graduate School of Medicine, Yokohama City University, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa 236-0004, Japan.

Biometrics
|March 14, 2014
PubMed
Summary

This study introduces a new model selection method for structural mean models (SMMs) in clinical trials with non-compliance. The approach enhances causal parameter estimation and identifies personalized treatment effects using baseline covariates.

Keywords:
Akaike's information criterionCausal inferenceEffect modificationG‐estimationInstrumental variableModel selectionNon‐complianceQuasi‐likelihoodStructural mean models

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Causal Inference

Background:

  • Structural mean models (SMMs) are used for causal inference in clinical trials with non-ignorable non-compliance.
  • Valid causal estimates from SMMs depend on correct structural model specification.

Purpose of the Study:

  • To propose a novel quasi-likelihood based model selection criterion for SMMs.
  • To extend Akaike's information criterion for SMMs.
  • To enable understanding of treatment effect variation across covariates and quantify effects for targeted interventions.

Main Methods:

  • Developed a model selection criterion extending Akaike's information criterion.
  • Utilized subset selection of baseline covariates.
  • Employed quasi-likelihood for SMMs.

Main Results:

  • The proposed method demonstrated good performance in simulations.
  • Achieved favorable results in selecting the correct model and predicting individual treatment effects.
  • Outperformed other testing methods in key metrics.

Conclusions:

  • The new model selection approach effectively addresses SMM specification in clinical trials.
  • Facilitates personalized treatment effect estimation by leveraging baseline covariates.
  • Offers a valuable tool for optimizing treatment strategies in trials like the pravastatin study.