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A smoothing dynamic model for irregularly time-spaced longitudinal data
1a Centre for Biostatistics, Institute of Population Health , University of Manchester , Manchester , United Kingdom.
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.
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