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In a real-life setting, little is known regarding the effectiveness ofstatins for primary prevention among older adults, and analysis ofobservational data can add crucial information on the benefits of actualpatterns of use. Latent class growth models (LCGM) are increasingly proposed asa solution to summarize the observed longitudinal treatment in a few distinctgroups. When combined with standard approaches like Cox proportional hazardsmodels, LCGM can fail to control time-dependent confounding bias because oftime-varying covariates that have a double role of confounders and mediators.We propose to use LCGM to classify individuals into a few latent classes basedon their medication adherence pattern, then choose a working marginalstructural model (MSM) that relates the outcome to these groups. The parameterof interest is nonparametrically defined as the projection of the true MSM ontothe chosen working model. The combination of LCGM with MSM is a convenient wayto describe treatment adherence and can effectively control time-dependentconfounding. Simulation studies were used to illustrate our approach andcompare it with unadjusted, baseline covariates-adjusted, time-varyingcovariates adjusted and inverse probability of trajectory groups weightingadjusted models. We found that our proposed approach yielded estimators withlittle or no bias.

Marginal structural models with Latent Class Growth Modeling of Treatment Trajectories