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. 2011 Sep 1;3(1):119-143.
doi: 10.1007/s12561-011-9040-7.

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

Jessica G Young, Lauren E Cain, James M Robins, Eilis J O'Reilly, Miguel A Hernán

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

Jessica G Young et al. Stat Biosci. .

Abstract

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 (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.

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Figures

Fig. 1
Fig. 1
Top panel: estimated observed (solid line) and “natural course” (dotted line) survival and mean cART indicator by month. Bottom panel: estimated differences between observed and “natural course” survival and mean cART indicator by month (solid line) and point-wise 95% confidence intervals based on 500 bootstrap samples (dotted lines).
Fig. 2
Fig. 2
Top panel: estimated observed (solid line) and “natural course” (dotted line) mean natural log viral load, natural log CD4 cell count and AIDS indicator by month. Bottom panel: estimated differences between observed and “natural course” mean natural log viral load, natural log CD4 cell count and AIDS indicator by month (solid line) and point-wise 95% confidence intervals based on 500 bootstrap samples (dotted lines).
Fig. 3
Fig. 3
Survival by each month of follow-up k = 0, … , 60 under regimes “start cART 6 months after CD4 cell count first drops below x or diagnosis of an AIDS-defining illness, whichever happens first, if the subject is still at risk and has not initiated treatment during the 6-month grace period on her own” for x = 500, 350, 200
See this image and copyright information in PMC

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