Longer-term growth prediction using GAUSS

In several areas of biomedicine, one needs to predict future measurements for a growing individual on the basis of longitudinal data. Here we consider the problem of estimating the values of a given measurement for a particular individual at T-T ∗ points in time, given T ∗ observations on that individual, and all T values for a sample of N “similar” individuals. This extends our previous discussion [Schneiderman et al., Comput. Biol. Med. 22, 181–188 (1992)], which was limited to the case T ∗ = T − 1 , to longer-term predictions. We again make a user-friendly GAUSS program available to perform the associated computations. Examples illustrating the use of the program and the accuracy of the predictions it provides are included.