Tracking: Prediction of Future Values from Serial Measurements

Repeated measurements of the same characteristic, obtained over time from each of a cohort of individuals, often show systematic change that facilitates prediction of future values. Several investigators have called this regular behavior 'tracking'. Most definitions of the term are related to the idea that a single individual's repeated measurements have expectations equal to a constant percentile of the population distribution asit changes over time. This paper investigates the relationship between this concept and the pattern of change implicit in growth-curve models. Growth-curve models are shown to be more general in some ways, more restrictive in others. We illustrate prediction of future values by growth techniques with an analysis of serial blood pressure measurements from the Framingham Heart Study. We also compare growth-curve analysis with recent work by McMahan (1981, Biometrics 37, 447-455) and suggest a more general class of random-effects models that may be useful in the study of tracking.

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