@article{ff02dcb58164479cad5325c70fb6d715,
title = "Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists",
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.",
author = "Dziak, {John J.} and Coffman, {Donna L.} and Matthew Reimherr and Justin Petrovich and Runze Li and Saul Shiffman and Shiyko, {Mariya P.}",
note = "Funding Information: This project was supported by Award R03CA171809-01 funded by the National Cancer Institute, by Awards P50DA039838, R21DA024260, and R01DA039901 from the National Institute on Drug Abuse, and by the National Institutes of Health (NIH) grant 1R01CA229542-01 funded by the National Cancer Institute and the Office of Behavioral and Social Science Research (OBSSR). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse, the National Cancer Institute, the National Institutes of Health, or the Office of Behavioral and Social Science Research. R 3.6.1 software, including the gee package (originally written by V. J. Carey and ported by T. Lumley and B. Ripley) and plotrix package (written by J. Lemon) was used for analyses and graphs. John Dziak thanks Dr. Stephanie T. Lanza for discussions which were enormously helpful in beginning this paper. John Dziak also thanks Jessica Dolan for assistance in implementing the paper. Publisher Copyright: {\textcopyright} 2019, Institute of Mathematical Statistics. All rights reserved.",
year = "2019",
doi = "10.1214/19-SS126",
language = "English (US)",
volume = "13",
pages = "150--180",
journal = "Statistics Surveys",
issn = "1935-7516",
publisher = "Institute of Mathematical Statistics",
}