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01.12.2013 | Research article | Ausgabe 1/2013 Open Access

BMC Medical Research Methodology 1/2013

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

BMC Medical Research Methodology > Ausgabe 1/2013
Michael J Crowther, Paul C Lambert, Keith R Abrams
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1471-2288-13-146) contains supplementary material, which is available to authorized users.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

All authors were involved in conception and design of the project. MJC conducted the simulation study, analysed the clinical dataset and wrote the first draft of the manscript. PCL and KRA both revised the manuscript. All authors read and approved the final manuscript.



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.


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.


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.


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.
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