Recommended Citation: Cain, Lauren E.; Robins, James M.; Lanoy, Emilie; Logan, Roger; Costagliola, Dominique; and Hernán, Miguel A. (2010) an approach for using observational data to emulate randomized clinical trials that compare dynamic regimes of the form "initiate treatment within a certain time period of some time-varying covariate first crossing a particular threshold." We applied this method to data from the French Hospital database on HIV (FHDH-ANRS CO4), an observational study of HIV-infected patients, in order to compare dynamic regimes of the form "initiate treatment within m months after the recorded CD4 cell count first drops below x cells/mm 3 " where x takes values from 200 to 500 in increments of 10 and m takes values 0 or 3. We describe the method in the context of this example and discuss some complications that arise in emulating a randomized experiment using observational data.
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.
Background Most clinical guidelines recommend that AIDS-free, HIV-infected persons with CD4 cell counts below 0.350 × 109 cells/L initiate combined antiretroviral therapy (cART), but the optimal CD4 cell count at which cART should be initiated remains a matter of debate. Objective To identify the optimal CD4 cell count at which cART should be initiated. Design Prospective observational data from the HIV-CAUSAL Collaboration and dynamic marginal structural models were used to compare cART initiation strategies for CD4 thresholds between 0.200 and 0.500 × 109 cells/L. Setting HIV clinics in Europe and the Veterans Health Administration system in the United States. Patients 20 971 HIV-infected, therapy-naive persons with baseline CD4 cell counts at or above 0.500 × 109 cells/L and no previous AIDS-defining illnesses, of whom 8392 had a CD4 cell count that decreased into the range of 0.200 to 0.499 × 109 cells/L and were included in the analysis. Measurements Hazard ratios and survival proportions for all-cause mortality and a combined end point of AIDS-defining illness or death. Results Compared with initiating cART at the CD4 cell count threshold of 0.500 × 109 cells/L, the mortality hazard ratio was 1.01 (95% CI, 0.84 to 1.22) for the 0.350 threshold and 1.20 (CI, 0.97 to 1.48) for the 0.200 threshold. The corresponding hazard ratios were 1.38 (CI, 1.23 to 1.56) and 1.90 (CI, 1.67 to 2.15), respectively, for the combined end point of AIDS-defining illness or death. Limitations CD4 cell count at cART initiation was not randomized. Residual confounding may exist. Conclusion Initiation of cART at a threshold CD4 count of 0.500 × 109 cells/L increases AIDS-free survival. However, mortality did not vary substantially with the use of CD4 thresholds between 0.300 and 0.500 ×109 cells/L. Primary Funding Source National Institutes of Health.
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