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

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

Computational Statistics & Data Analysis, 2010, vol. 54, issue 12, 3080-3094

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.

Keywords: Cross-validation; Machine; learning; Marginal; structural; models; Lung; function; Cardiovascular; mortality (search for similar items in EconPapers)
Date: 2010
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