Individualized, Computerized Growth Prediction

Abstract

This investigation was conducted to individualize growth prediction by use of regression formulas and therefore supplement the present method of using mean incremental data obtained from case study. There were 30 normal individuals, ages 8 to 19 years, 14 males and 16 females. Based on the analysis of serial headplates, the incremental growth change for 12 variables to be used in growth prediction was calculated for each individual for each 3 year period until adulthood was reached. Cephalometric measurements, consisting of 39 variables, were made at the beginning of each 3 year period. For every age group the following information was fed into a computer: a.) The known incremental growth change for each 3 year for the 12 variables to be used in prediction. b.) The known measurements of the 39 variables at the beginning of each 3 year period. The computer selected from the 39 variables only those which best predicted the already known incremental growth change of the 12 variables to be used in prediction. A total of 101 regression formulas of a possible 108 was obtained for males, and 102 for females, with a high multiple correlation. A sign test at .05 level of confidence was used to determine if this regression formula method was significantly better than the mean incremental method presently used at Indiana University. The results showed that, in the majority of the cases, the regression method proved to be significantly better than the mean incremental method. In none of the cases was the man incremental method significantly better.