Scalar-On-Function Regression For Predicting Distal Outcomes From Intensively Gathered Longitudinal Data: Interpretability For Applied Scientists
STATISTICS SURVEYS(2019)
摘要
Researchers are sometimes interested in predicting a distal or external outcome (such as smoking cessation at follow-up) from the trajectory of an intensively recorded longitudinal variable (such as urge to smoke). This can be done in a semiparametric way via scalar-on-function regression. However, the resulting fitted coefficient regression function requires special care for correct interpretation, as it represents the joint relationship of time points to the outcome, rather than a marginal or cross-sectional relationship. We provide practical guidelines, based on experience with scientific applications, for helping practitioners interpret their results and illustrate these ideas using data from a smoking cessation study.
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关键词
Distal outcomes, functional regression, intensive longitudinal data, scalar-on-function regression, trajectories
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