Bootstrapping complex time-to-event data without individual patient data, with a view toward time-dependent exposures.

ABSTRACT: We consider nonparametric and semiparametric resampling of multistate event histories by simulating multistate trajectories from an empirical multivariate hazard measure. One advantage of our approach is that it does not necessarily require individual patient data, but may be based on published information. This is also attractive for both study planning and simulating realistic real-world event history data in general. The concept extends to left-truncation and right-censoring mechanisms, nondegenerate initial distributions, and nonproportional as well as non-Markov settings. A special focus is on its connection to simulating survival data with time-dependent covariates. For the case of qualitative time-dependent exposures, we demonstrate that our proposal gives a more natural interpretat

SUBMITTER: Bluhmki T 

PROVIDER: S-EPMC6771611 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature