| アブストラクト | 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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| ジャーナル名 | BMC medical research methodology |
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| Pubmed追加日 | 2013/12/3 |
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| 投稿者 | Crowther, Michael J; Lambert, Paul C; Abrams, Keith R |
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| 組織名 | University of Leicester, Department of Health Sciences, Adrian Building,;University Road, Leicester LE1 7RH, UK. michael.crowther@le.ac.uk. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/24289257/ |
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