Considerations for analysis of time-to-event outcomes measured with error: Bias and correction with SIMEX.

ABSTRACT: For time-to-event outcomes, a rich literature exists on the bias introduced by covariate measurement error in regression models, such as the Cox model, and methods of analysis to address this bias. By comparison, less attention has been given to understanding the impact or addressing errors in the failure time outcome. For many diseases, the timing of an event of interest (such as progression-free survival or time to AIDS progression) can be difficult to assess or reliant on self-report and therefore prone to measurement error. For linear models, it is well known that random errors in the outcome variable do not bias regression estimates. With nonlinear models, however, even random error or misclassification can introduce bias into estimated parameters. We compare the performance of 2 comm

SUBMITTER: Oh EJ 

PROVIDER: S-EPMC5810403 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature