Why model growth data?

Growth can be considered as the process that makes children change in size and shape over time. The dynamics of growth is best understood from the analysis of longitudinal data, i.e. from serial measurements taken at regular intervals on the same subject. Table 8.1 gives an example of longitudinal growth data for height of a boy measured at birth and at each birthday thereafter up to the age of 18 years. Such data usually form the basis to estimate the underlying process of growth, which is supposed to be continuous. Recent analysis of frequent measurements of size (at daily or weekly intervals) with high-precision techniques (such as knemometry where measurement error is about 0.1 mm) has shown that the growth process is, at microlevel, not as smooth as we usually assume (Hermanussen, 1998; Lampl, 1999). However, we may readily assume that the growth process is continuous when we are dealing with measurements taken at yearly intervals, or even 3- to 6-monthly intervals, using classical anthropometric techniques. Various mathematical models have been proposed to estimate such a smooth growth curve on the basis of a set of discrete measurements of growth of the same subject over time (Marubini and Milani, 1986; Hauspie, 1989, 1998; Simondon et al., 1992; Bogin, 1999).