A smoothing dynamic model for irregularly time-spaced longitudinal data.
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.
a Centre for Biostatistics, Institute of Population Health , University of Manchester , Manchester , United Kingdom.
Bo
B
Fu
Wenyang
W
Zhang
D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D008962:Models, Theoretical; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D013997:Time Factors
Dynamic model; Irregularly spaced measurements; Longitudinal data; Varying coefficient