Structural accelerated failure time models for survival analysis in studies with time-varying treatments.
BACKGROUND
In the absence of unmeasured confounding factors and model misspecification, standard methods for estimating the causal effect of time-varying treatments on survival are biased when (i) there exists a time-dependent risk factor for survival that also predicts subsequent treatment and (ii) past treatment history predicts subsequent risk factor level. In contrast, structural models provide consistent estimates of causal effects when unmeasured confounding and model misspecification are absent. The parameters of nested structural models are estimated by g-estimation and those of marginal structural models by inverse probability weighting.


METHODS
We describe a nested structural accelerated failure time model and use it to estimate the total causal effect of highly active antiretroviral therapy (HAART) on the time to AIDS or death among human immunodeficiency virus (HIV)-infected participants of the Multicenter AIDS Cohort and Women's Interagency HIV Studies. The Appendix describes g-estimation and methods to deal with censoring.


RESULTS
Comparing the regime 'always treated' to 'never treated,' the AIDS-free survival time ratio was 2.5 (95% confidence interval [CI]: 1.7, 3.3).


CONCLUSIONS
Our finding of a strongly beneficial effect is consistent with results from randomized trials and from a previous analysis of the same data using a marginal structural Cox model. In contrast, a previous analysis using a standard (non-structural) model did not find an effect of treatment on survival.
Hernán
Miguel A
MA
Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA. miguel_hernan@post.harvard.edu
Cole
Stephen R
SR
Margolick
Joseph
J
Cohen
Mardge
M
Robins
James M
JM
D023241:Antiretroviral Therapy, Highly Active / Q000706:statistics & numerical data; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D005260:Female; D015658:HIV Infections / Q000188:drug therapy* / Q000401:mortality*; D006801:Humans; D008297:Male; D015337:Multicenter Studies as Topic; D016016:Proportional Hazards Models; D011446:Prospective Studies; D016032:Randomized Controlled Trials as Topic; D012307:Risk Factors; D016019:Survival Analysis; D013997:Time Factors