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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

Example simulated observed longitudinal measurements with varying measurement error standard deviation.
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Related In: Results  -  Collection

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Figure 1: Example simulated observed longitudinal measurements with varying measurement error standard deviation.

Mentions: To illustrate the varying measurement error standard deviations used in the simulation scenarios, we show in Figure1 observed longitudinal measurements from the same 100 patients with σe = {0.1,0.5,1}, and when α = 0.25. Figure1 illustrates that as the measurement error standard deviation increases, the variability in the observed biomarker values increases.


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)

Example simulated observed longitudinal measurements with varying measurement error standard deviation.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Example simulated observed longitudinal measurements with varying measurement error standard deviation.
Mentions: To illustrate the varying measurement error standard deviations used in the simulation scenarios, we show in Figure1 observed longitudinal measurements from the same 100 patients with σe = {0.1,0.5,1}, and when α = 0.25. Figure1 illustrates that as the measurement error standard deviation increases, the variability in the observed biomarker values increases.

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