Semiparametric Regression Modeling of the Global Percentile Outcome

Authors

Xiangyu Liu
Jing Ning
Xuming He
Barbara C Tilley
Ruosha Li

Publication Date

1-1-2023

Journal

Journal of Statistical Planning and Inference

Abstract

When no single outcome is sufficient to capture the multidimensional impairments of a disease, investigators often rely on multiple outcomes for comprehensive assessment of global disease status. Methods for assessing covariate effects on global disease status include the composite outcome and global test procedures. One global test procedure is the O'Brien's rank-sum test, which combines information from multiple outcomes using a global rank-sum score. However, existing methods for the global rank-sum do not lend themselves to regression modeling. We consider sensible regression strategies for the global percentile outcome (GPO), under the transformed linear model and the monotonic index model. Posing minimal assumptions, we develop estimation and inference procedures that account for the special features of the GPO. Asymptotics are established using U-statistic and U-process techniques. We illustrate the practical utilities of the proposed methods via extensive simulations and application to a Parkinson's disease study.

Keywords

Global percentile outcome, Rank-sum, Monotonic index model, Non-smooth objective function, Transformed linear model