Marginal structural models with latent class growth analysis of treatment trajectories: Statins for primary prevention among older adults.

ABSTRACT: Latent class growth analysis is increasingly proposed as a solution to summarize the observed longitudinal treatment into a few distinct groups. When latent class growth analysis is combined with standard approaches like Cox proportional hazards models, confounding bias is not properly addressed because of time-varying covariates that have a double role of confounders and mediators. We propose to use latent class growth analysis to classify individuals into a few latent classes based on their medication adherence pattern, then choose a working marginal structural model that relates the outcome to these groups. The parameter of interest is defined as a projection of the true marginal structural model onto the chosen working model. Simulation studies are used to illustrate our approach and c

SUBMITTER: Diop A 

PROVIDER: S-EPMC10683348 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature