Adaptive Regression for Nonlinear Interrupted Time Series Analyses with Application to Birth Defects in Children of Vietnam War Veterans

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.

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