Data-driven simulations to assess the impact of study imperfections in time-to-event analyses.

ABSTRACT: Quantitative bias analysis (QBA) permits assessment of the expected impact of various imperfections of the available data on the results and conclusions of a particular real-world study. This article extends QBA methodology to multivariable time-to-event analyses with right-censored endpoints, possibly including time-varying exposures or covariates. The proposed approach employs data-driven simulations, which preserve important features of the data at hand while offering flexibility in controlling the parameters and assumptions that may affect the results. First, the steps required to perform data-driven simulations are described, and then two examples of real-world time-to-event analyses illustrate their implementation and the insights they may offer. The first example focuses on the omis

SUBMITTER: Abrahamowicz M 

PROVIDER: S-EPMC7617302 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature