Joint Modeling of Multivariate Nonparametric Longitudinal Data and Survival Data: A Local Smoothing Approach

• Activity Number: 242 - Semiparametric, Nonparametric, and Other Flexible Approaches
• Type: Contributed
• Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Lu You* and Peihua Qiu
• Companies: University of South Florida and University of Florida
• Keywords: joint modeling; local kernel smoothing; longitudinal data; nonparametric mixed-effects model; multiple outcomes; survival data

Abstract

In many clinical studies, evaluating the association between longitudinal and survival outcomes is of primary concern. For analyzing data from such studies, joint modeling of longitudinal and survival data becomes an appealing approach. In some applications, there are multiple longitudinal outcomes whose longitudinal pattern is difficult to describe by a parametric form. For such applications, existing research on joint modeling is limited. In this article, we develop a novel joint modeling method to fill the gap. In the new method, a local polynomial mixed-effects model is used for describing the nonparametric longitudinal pattern of the multiple longitudinal outcomes. Two model estimation procedures, that is, the local EM algorithm and the local penalized quasi-likelihood estimation, are explored. Practical guidelines for choosing tuning parameters and for variable selection are provided. The new method is justified by some theoretical arguments and numerical studies.