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Structural accelerated failure time models for survival analysis in studies with time‐varying treatments

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.