Adjusting for measurement error in baseline prognostic biomarkers included in a time-to-event analysis: a joint modelling approach

作者：Crowther Michael J^*; Lambert Paul C; Abrams Keith R

来源：BMC Medical Research Methodology, 2013, 13: 146.

DOI：10.1186/1471-2288-13-146

Background: Methodological development of joint models of longitudinal and survival data has been rapid in recent years; however, their full potential in applied settings are yet to be fully explored. We describe a novel use of a specific association structure, linking the two component models through the subject specific intercept, and thus extend joint models to account for measurement error in a biomarker, even when only the baseline value of the biomarker is of interest. This is a common occurrence in registry data sources, where often repeated measurements exist but are simply ignored. %26lt;br%26gt;Methods: The proposed specification is evaluated through simulation and applied to data from the General Practice Research Database, investigating the association between baseline Systolic Blood Pressure (SBP) and the time-to-stroke in a cohort of obese patients with type 2 diabetes mellitus. %26lt;br%26gt;Results: By directly modelling the longitudinal component we reduce bias in the hazard ratio for the effect of baseline SBP on the time-to-stroke, showing the large potential to improve on previous prognostic models which use only observed baseline biomarker values. %26lt;br%26gt;Conclusions: The joint modelling of longitudinal and survival data is a valid approach to account for measurement error in the analysis of a repeatedly measured biomarker and a time-to-event. User friendly Stata software is provided.

• 出版日期2013-12-1