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2019 Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists
John J. Dziak, Donna L. Coffman, Matthew Reimherr, Justin Petrovich, Runze Li, Saul Shiffman, Mariya P. Shiyko
Statist. Surv. 13: 150-180 (2019). DOI: 10.1214/19-SS126

Abstract

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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John J. Dziak. Donna L. Coffman. Matthew Reimherr. Justin Petrovich. Runze Li. Saul Shiffman. Mariya P. Shiyko. "Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists." Statist. Surv. 13 150 - 180, 2019. https://doi.org/10.1214/19-SS126

Information

Received: 1 November 2018; Published: 2019
First available in Project Euclid: 6 November 2019

zbMATH: 07136600
MathSciNet: MR4029159
Digital Object Identifier: 10.1214/19-SS126

Subjects:
Primary: 62-02
Secondary: 62G08, 62M10

JOURNAL ARTICLE
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Vol.13 • 2019
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