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. 2019 Dec;75(4):1205-1215.
doi: 10.1111/biom.13104. Epub 2019 Sep 12.

Model selection for G-estimation of dynamic treatment regimes

Michael P Wallace  1 , Erica E M Moodie  2 , David A Stephens  3
Affiliations

Model selection for G-estimation of dynamic treatment regimes

Michael P Wallace et al. Biometrics. 2019 Dec.

Abstract

Dynamic treatment regimes (DTRs) aim to formalize personalized medicine by tailoring treatment decisions to individual patient characteristics. G-estimation for DTR identification targets the parameters of a structural nested mean model, known as the blip function, from which the optimal DTR is derived. Despite its potential, G-estimation has not seen widespread use in the literature, owing in part to its often complex presentation and implementation, but also due to the necessity for correct specification of the blip. Using a quadratic approximation approach inspired by iteratively reweighted least squares, we derive a quasi-likelihood function for G-estimation within the DTR framework, and show how it can be used to form an information criterion for blip model selection. We outline the theoretical properties of this model selection criterion and demonstrate its application in a variety of simulation studies as well as in data from the Sequenced Treatment Alternatives to Relieve Depression study.

Trial registration: ClinicalTrials.gov NCT00021528.

Keywords: adaptive treatment strategies; dynamic treatment regimes; iteratively reweighted least squares; quasi-likelihood information criterion; structural nested models.

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References

REFERENCES

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