Applications of segmented regression models for biomedical
studies.
Berman, Nancy G., Weng Kee Wong, Shalandar Bhasin, Eli Ipp.
Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA
90502, Department of Biostatistics, UCLA, Los Angeles, CA 90024-1772,
Department of Medicine, King-Drew Medical Center, Los Angeles, CA
90059, Department of Medicine, Harbor-UCLA Medical Center, Torrance,
CA 90502
APStracts 2:0258E, 1995.
In many biological models a relationship between variables may be
modelled as a linear or polynomial function which 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 method and
Tishler and Zang's method, using nonlinear least-squares techniques
for estimating the transition point. We give 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.
Received 10 January 1995; accepted in final form 20 November
1995.
APS Manuscript Number E6-5.
Article publication pending Am. J. Physiol. (Endocrinol. Metab.).
ISSN 1080-4757 Copyright 1995 The American Physiological Society.
Published in APStracts on 23 December 95