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Model Checking with Residuals for g-estimation of Optimal Dynamic Treatment Regimes

Author

Listed:
  • Rich Benjamin

    (McGill University)

  • Moodie Erica E. M.

    (McGill University)

  • Stephens David A

    (McGill University)

  • Platt Robert W

    (McGill University)

Abstract

In this paper, we discuss model checking with residual diagnostic plots for g-estimation of optimal dynamic treatment regimes. The g-estimation method requires three different model specifications at each treatment interval under consideration: (1) the blip model; (2) the expected counterfactual model; and (3) the propensity model. Of these, the expected counterfactual model is especially difficult to specify correctly in practice and so far there has been little guidance as to how to check for model misspecification. Residual plots are a useful and standard tool for model diagnostics in the classical regression setting; we have adapted this approach for g-estimation. We demonstrate the usefulness of our approach in a simulation study, and apply it to real data in the context of estimating the optimal time to stop breastfeeding.

Suggested Citation

  • Rich Benjamin & Moodie Erica E. M. & Stephens David A & Platt Robert W, 2010. "Model Checking with Residuals for g-estimation of Optimal Dynamic Treatment Regimes," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-24, March.
    • Handle: RePEc:bpj:ijbist:v:6:y:2010:i:2:n:12
    DOI: 10.2202/1557-4679.1210
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    References listed on IDEAS

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    1. Erica E. M. Moodie & Thomas S. Richardson & David A. Stephens, 2007. "Demystifying Optimal Dynamic Treatment Regimes," Biometrics, The International Biometric Society, vol. 63(2), pages 447-455, June.
    2. Erica E. M. Moodie & Thomas S. Richardson, 2010. "Estimating Optimal Dynamic Regimes: Correcting Bias under the Null," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(1), pages 126-146, March.
    3. Moodie, Erica E. M. & Platt, Robert W. & Kramer, Michael S., 2009. "Estimating Response-Maximized Decision Rules With Applications to Breastfeeding," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 155-165.
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    Citations

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    Cited by:

    1. Michael P. Wallace & Erica E. M. Moodie, 2015. "Doubly‐robust dynamic treatment regimen estimation via weighted least squares," Biometrics, The International Biometric Society, vol. 71(3), pages 636-644, September.
    2. Xin Qiu & Donglin Zeng & Yuanjia Wang, 2018. "Estimation and evaluation of linear individualized treatment rules to guarantee performance," Biometrics, The International Biometric Society, vol. 74(2), pages 517-528, June.
    3. Wallace, Michael P. & Moodie, Erica E. M. & Stephens, David A., 2017. "Dynamic Treatment Regimen Estimation via Regression-Based Techniques: Introducing R Package DTRreg," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 80(i02).

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