Open AccessScalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists
Author(s) -
John J. Dziak,
Donna L. Coffman,
Matthew Reimherr,
Justin Petrovich,
Runze Li,
Saul Shiffman,
Mariya Shiyko
Publication year - 2019
Publication title -
statistics surveys
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.358
H-Index - 23
ISSN - 1935-7516
DOI - 10.1214/19-ss126
Subject(s) - interpretability , regression , outcome (game theory) , econometrics , linear regression , longitudinal data , regression analysis , statistics , mathematics , psychology , computer science , artificial intelligence , data mining , mathematical economics
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.