PC program for growth prediction in the two-stage polynomial growth curve model

Abstract

We consider the problem of growth prediction in the context of the two-stage (or random coefficients) one-sample polynomial growth curve model and provide a PC program, written in GAUSS386i, to perform the associated computations. The problem considered is that of estimating the value of the measurement under consideration for a `new' individual at the Tth time point given measurements on that individual at T - 1 previous points in time and the values of the measurement on N `similar' individuals at all T time points. The times of measurement t1, t2,..., tT need not be equally spaced, but we assume that each of the N individuals comprising the normative sample were measured at these times. The method and the program are illustrated using the data set previously considered (Schneiderman and Kowalski, Am J Phys Anthrop, 67 (1985) 323-333) consisting of mandibular ramus height measurements (in mm) for 12 male rhesus monkeys at T = 5 yearly intervals (coded 1, 2, 3, 4, and 5). Results are compared with those obtained under a less restrictive set of assumptions concerning the covariance matrix of the observations than is made in the context of the two-stage model. It is seen that the accuracies of prediction of the two methods, for this and other data sets, are quite close, suggesting that the less restrictive model may be preferred in many situations.