title: Data-driven desirability function to measure patients' disease progression in a longitudinal study.
abstract: Multiple outcomes are increasingly used to assess chronic disease progression. We discuss and show how desirability functions can be used to assess a patient overall response to a treatment using multiple outcome measures and each of them may contribute unequally to the final assessment. Because judgments on disease progression and the relative contribution of each outcome can be subjective, we propose a data-driven approach to minimize the biases by using desirability functions with estimated shapes and weights based on a given gold standard. Our method provides each patient with a meaningful overall progression score that facilitates comparison and clinical interpretation. We also extend the methodology in a novel way to monitor patients' disease progression when there are multiple time points and illustrate our method using a longitudinal data set from a randomized two-arm clinical trial for scleroderma patients.
authors.affiliation: Department of Industrial and Systems Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan.
authors.forename: Hsiu-Wen
authors.initials: HW
authors.lastname: Chen
authors.affiliation: Department of Biostatistics, University of California, Los Angeles, CA 90095, USA.
authors.forename: Weng Kee
authors.initials: WK
authors.lastname: Wong
authors.affiliation: Department of Statistics, University of California, Los Angeles, CA 90095, USA.
authors.forename: Hongquan
authors.initials: H
authors.lastname: Xu
keywords: desirability function; longitudinal data; multiple outcomes; nonlinear least squares; scleroderma