AJP - Endocrinology and Metabolism, Vol 270, Issue 4 E723-E732, Copyright © 1996 by American Physiological Society
Applications of segmented regression models for biomedical studies
N. G. Berman, W. K. Wong, S. Bhasin and E. Ipp
Department of Pediatrics, Harbor-University of California Los Angeles Medical Center, Torrance 90502, USA.
In many biological models, a relationship between variables may be modeled
as a linear or polynomial function that changes abruptly when an
independent variable obtains a threshold level. Usually, the transition
point is unknown, and a major objective of the analysis is its estimation.
This type of model is known as a segmented regression model. We present two
methods, Gallant and Fuller's (J Am. Stat. Assoc. 68: 144-147, 1973) method
and Tishler and Zang's (J. Am. Stat. Assoc. 76: 980-987, 1981) method,
using nonlinear least-squares techniques for estimating the transition
point. We give the following three examples: a hypoglycemia study, a
testosterone study, and an estimate of age-cortisol relationship.
Simulation techniques are used to compare the two methods. We conclude that
these models provide useful information and that the two methods studied
produce essentially equivalent results. We recommend that both methods be
used to analyze a data set if possible to avoid problems due to local
minima and that if the results do not agree, then evaluation of the
likelihood function in the range of the estimates be used to determine the
best estimate.
