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Dynamic Random Effects Models in Longitudinal Studies

In this work, we consider the problem of using routinely collected primary care data to monitor the progression of undiagnosed incipient renal failure in a large sample of people in primary care. Our proposed strategy, is to build a dynamic regression model in which a subject?s rate of change in eGFR, relative to the expected profile of eGFR for all subjects with the same values for age, sex and other explanatory variables, is modelled as a stochastic process B(t), which is realised independently for each subject. 

Key-Words: longitudinal studies; random effects; Kalman-Filter algorithm
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