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

View 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/mm(3) 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.

Investigators
Abbreviation
Stat Biosci
Publication Date
2011-09-01
Volume
3
Issue
1
Page Numbers
119-143
Pubmed ID
24039638
Medium
Print
Full Title
Comparative effectiveness of dynamic treatment regimes: an application of the parametric g-formula.
Authors
Young JG, Cain LE, Robins JM, O'Reilly EJ, Hernán MA