Using marginal structural models to adjust for treatment drop-in when developing clinical prediction models.

ABSTRACT: Clinical prediction models (CPMs) can inform decision making about treatment initiation, which requires predicted risks assuming no treatment is given. However, this is challenging since CPMs are usually derived using data sets where patients received treatment, often initiated postbaseline as "treatment drop-ins." This study proposes the use of marginal structural models (MSMs) to adjust for treatment drop-in. We illustrate the use of MSMs in the CPM framework through simulation studies that represent randomized controlled trials and real-world observational data and the example of statin initiation for cardiovascular disease prevention. The simulations include a binary treatment and a covariate, each recorded at two timepoints and having a prognostic effect on a binary outcome. The bias

SUBMITTER: Sperrin M 

PROVIDER: S-EPMC6282523 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature