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

Crowther MJ, Lambert PC, Abrams KR - BMC Med Res Methodol (2013)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of Leicester, Department of Health Sciences, Adrian Building, University Road, Leicester LE1 7RH, UK. michael.crowther@le.ac.uk.

ABSTRACT

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.

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.

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.

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.

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Related in: MedlinePlus

Longitudinal response measurements for SBP for 9 randomly selected patients who had at least 10 measurements. The dashed line represents the fitted longitudinal trajectories based on the joint model.
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Figure 2: Longitudinal response measurements for SBP for 9 randomly selected patients who had at least 10 measurements. The dashed line represents the fitted longitudinal trajectories based on the joint model.

Mentions: In Figure2 we show the observed SBP measurements for 9 randomly selected patients, who had at least 10 measurements, illustrating some nonlinear trajectories. To accommodate such nonlinearities we can use restricted cubic splines in the linear mixed effects submodel. In particular, we specify the following longitudinal submodel


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

Crowther MJ, Lambert PC, Abrams KR - BMC Med Res Methodol (2013)

Longitudinal response measurements for SBP for 9 randomly selected patients who had at least 10 measurements. The dashed line represents the fitted longitudinal trajectories based on the joint model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4219390&req=5

Figure 2: Longitudinal response measurements for SBP for 9 randomly selected patients who had at least 10 measurements. The dashed line represents the fitted longitudinal trajectories based on the joint model.
Mentions: In Figure2 we show the observed SBP measurements for 9 randomly selected patients, who had at least 10 measurements, illustrating some nonlinear trajectories. To accommodate such nonlinearities we can use restricted cubic splines in the linear mixed effects submodel. In particular, we specify the following longitudinal submodel

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of Leicester, Department of Health Sciences, Adrian Building, University Road, Leicester LE1 7RH, UK. michael.crowther@le.ac.uk.

ABSTRACT

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.

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.

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.

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.

Show MeSH
Related in: MedlinePlus