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

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  • Haight, Thaddeus J.
  • Wang, Yue
  • van der Laan, Mark J.
  • Tager, Ira B.

Abstract

The cross-validation deletion-substitution-addition (cvDSA) algorithm is based on data-adaptive estimation methodology to select and estimate marginal structural models (MSMs) for point treatment studies as well as models for conditional means where the outcome is continuous or binary. The algorithm builds and selects models based on user-defined criteria for model selection, and utilizes a loss function-based estimation procedure to distinguish between different model fits. In addition, the algorithm selects models based on cross-validation methodology to avoid "over-fitting" data. The cvDSA routine is an R software package available for download. An alternative R-package (DSA) based on the same principles as the cvDSA routine (i.e., cross-validation, loss function), but one that is faster and with additional refinements for selection and estimation of conditional means, is also available for download. Analyses of real and simulated data were conducted to demonstrate the use of these algorithms, and to compare MSMs where the causal effects were assumed (i.e., investigator-defined), with MSMs selected by the cvDSA. The package was used also to select models for the nuisance parameter (treatment) model to estimate the MSM parameters with inverse-probability of treatment weight (IPTW) estimation. Other estimation procedures (i.e., G-computation and double robust IPTW) are available also with the package.

Suggested Citation

  • Haight, Thaddeus J. & Wang, Yue & van der Laan, Mark J. & Tager, Ira B., 2010. "A cross-validation deletion-substitution-addition model selection algorithm: Application to marginal structural models," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3080-3094, December.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:12:p:3080-3094
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    References listed on IDEAS

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    1. Yue Wang & Mark van der Laan, 2004. "Data Adaptive Estimation of the Treatment Specific Mean," U.C. Berkeley Division of Biostatistics Working Paper Series 1159, Berkeley Electronic Press.
    2. Weihua Cao & Anastasios A. Tsiatis & Marie Davidian, 2009. "Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data," Biometrika, Biometrika Trust, vol. 96(3), pages 723-734.
    3. van der Laan Mark J. & Rubin Daniel, 2006. "Targeted Maximum Likelihood Learning," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-40, December.
    4. Brookhart, M. Alan & van der Laan, Mark J., 2006. "A semiparametric model selection criterion with applications to the marginal structural model," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 475-498, January.
    5. Mark van der Laan & Sandrine Dudoit & Aad van der Vaart, 2004. "The Cross-Validated Adaptive Epsilon-Net Estimator," U.C. Berkeley Division of Biostatistics Working Paper Series 1141, Berkeley Electronic Press.
    6. Sandra Sinisi & Mark van der Laan, 2004. "Loss-Based Cross-Validated Deletion/Substitution/Addition Algorithms in Estimation," U.C. Berkeley Division of Biostatistics Working Paper Series 1142, Berkeley Electronic Press.
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