The primary analysis of most randomized trials estimates the intention-to-treat effect, that is, the comparative effect of being assigned to the treatment strategies of interest. However, in many randomized trials, patients and doctors are more interested in the per-protocol effect, that is, the comparative effect of following the assigned treatment strategies as indicated in the protocol during the follow-up period. Valid estimation of the per-protocol effect of sustained treatment strategies generally requires adjustment for pre- and post-randomization prognostic factors associated with adherence. Because post-randomization factors may be affected by prior treatment, conventional statistical methods for adjustment may introduce bias. In contrast, Robins’s g-methods (inverse-probability weighting, g-estimation, and the parametric g-formula) can appropriately adjust for time-varying factors affected by treatment. This talk will discuss various methodological approaches to estimate the per-protocol effect of sustained treatment strategies in randomized trials.