TITLE:
Adaptive Regression for Nonlinear Interrupted Time Series Analyses with Application to Birth Defects in Children of Vietnam War Veterans
AUTHORS:
George J. Knafl
KEYWORDS:
Adaptive Regression, Air Force Health Study, Birth Defects, Dioxin, Inter-rupted Time Series
JOURNAL NAME:
Open Journal of Statistics,
Vol.12 No.6,
December
30,
2022
ABSTRACT: The
purpose of this article is to provide an overview of adaptive regression
modeling and demonstrate its use in conducting nonlinear analyses of
interrupted time series (ITS) data. Adaptive regression modeling is based on
heuristic search over alternative models for data controlled by
likelihood-cross validation (LCV) scores with larger scores indicating better
models. Extended linear mixed models are used for correlated data like ITS
data. Power transforms of predictor variables are used to account for
nonlinearity. The use of adaptive regression modeling for assessing ITS effects
is demonstrated using data on annual proportions of major birth defects in
children fathered by male Air Force veterans of the Vietnam War over a 59-year
period. The interruption for this ITS is conception after versus before the
start of a participant’s first tour in the Vietnam War. Whether the ITS effect
is related to dioxin exposure is also addressed. Dioxin is a highly toxic
contaminant of the herbicide Agent Orange used in the Vietnam War. The core
findings of the reported analyses are that a substantial adverse ITS
interruption effect is identified and that this adverse effect can reasonably
be attributed to participants having a high dioxin exposure level. Moreover,
these results indicate that adaptive regression modeling can identify nonlinear
ITS effects in general situations that can lead to consequential insights into
nonlinear relationships over time, possibly varying with other available
predictors.