Semiparametric regression modeling of the global percentile outcome.
Xiangyu Liu, Jing Ning, Xuming He, Barbara C Tilley, Ruosha Li
Author Information
Xiangyu Liu: Department of Biometrics, Gilead Sciences, Seattle, WA, United States of America.
Jing Ning: Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States of America.
Xuming He: Department of Statistics, University of Michigan, Ann Arbor, MI, United States of America.
Barbara C Tilley: Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, TX, United States of America.
Ruosha Li: Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, TX, United States of America.
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.
