Comparative effectiveness of dynamic treatment regimes: an application of the parametric g-formula

By Jessica G. Young, L. Cain, James M. Robins, É. O’Reilly, Miguel A. Hern'an. 2011

155 citations recorded by the source corpus.

Ideally, randomized trials would be used to compare the long-term effectiveness of dynamic treatment regimes on clinically relevant outcomes. However, because randomized trials are not always feasible or timely, we often must rely on observational data to compare dynamic treatment regimes. An example of a dynamic treatment regime is “start combined antiretroviral therapy (cART) within 6 months of CD4 cell count first dropping below x cells/mm3 or diagnosis of an AIDS-defining illness, whichever happens first” where x can take values between 200 and 500. Recently, Cain et al. (Ann. Intern. Med. 154(8):509–515, 2011) used inverse probability (IP) weighting of dynamic marginal structural models to find the x that minimizes 5-year mortality risk under similar dynamic regimes using observational data. Unlike standard methods, IP weighting can appropriately adjust for measured time-varying confounders (e.g., CD4 cell count, viral load) that are affected by prior treatment. Here we describe an alternative method to IP weighting for comparing the effectiveness of dynamic cART regimes: the parametric g-formula. The parametric g-formula naturally handles dynamic regimes and, like IP weighting, can appropriately adjust for measured time-varying confounders. However, estimators based on the parametric g-formula are more efficient than IP weighted estimators. This is often at the expense of more parametric assumptions. Here we describe how to use the parametric g-formula to estimate risk by the end of a user-specified follow-up period under dynamic treatment regimes. We describe an application of this method to answer the “when to start” question using data from the HIV-CAUSAL Collaboration.

Subfields: Economics and Econometrics, Infectious Diseases, Statistics and Probability

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Citations in the corpus

G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes (2020, methodology): “Illustrative applications of the general approach are provided in [6, 16], and summaries of g-computation (and other “g-methods” for estimating time-varying treatment effects) can be found in [2, 3].”
Bootstrap Inference When Using Multiple Imputation (2016, background): “We refer the reader to [17], [23], and [44] to”
A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures (2019, unknown): “…authors in both frequentist and Bayesian settings (Keil et al. 2014, 2018a; Snowden et al. 2011; Taubman et al. 2009; Westreich et al. 2012; Young et al. 2011), we note here that it is a generalization of standardization that uses the law of total probability to compute estimates of the…”