Joint modeling of multivariate nonparametric longitudinal data and survival data: A local smoothing approach. (25th September 2021)

• Record Type: Journal Article
• Authors: You, Lu; Qiu, Peihua
• Is Part Of: Statistics in medicine. Volume 40:Number 29(2021)
• Page Start: 6689
• Page End: 6706
• Publication Date: 2021-09-25
• Subjects: joint modeling -- local kernel smoothing -- longitudinal data -- multiple outcomes -- nonparametric mixed‐effects model -- survival data; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.9206
• Languages: English
• ISSNs: 0277-6715

Abstract

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.