A smoothing dynamic model for irregularly time-spaced longitudinal data

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Publication details

JournalJournal of Biopharmaceutical Statistics
DatePublished - 2014
Issue number4
Volume24
Number of pages14
Pages (from-to)944-957
Original languageEnglish

Abstract

In nondesigned longitudinal observational studies, irregularly spaced measurements are commonly present over a period of follow-up time. We propose a smoothing dynamic model, based on the idea of varying coefficients, to analyze this highly unbalanced longitudinal data. The estimate of model parameters can be obtained by implementing a well-developed B-splines technique. Our method is illustrated with data from a primary care based longitudinal cohort of rheumatoid arthritis patients. The results show that the effects of some risk factors might be underestimated by an intention-to-treat analysis using a last-value-carried-forward method.

    Research areas

  • Dynamic model, Irregularly spaced measurements, Longitudinal data, Varying coefficient

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