B0550
Title: Extending multivariate nonlinear mixed models with censored and non-ignorable missing outcomes
Authors: Wan-Lun Wang - National Cheng Kung University (Taiwan) [presenting]
Tsung-I Lin - National Chung Hsing University (Taiwan)
Abstract: Multivariate nonlinear mixed-effects models (MNLMMs) have become a promising tool for analyzing multi-outcome longitudinal data following nonlinear trajectory patterns. However, such a classical analysis can be challenging due to censorship induced by detection limits of the quantification assay or non-response occurring when participants miss scheduled visits intermittently or discontinue participation. An extension of the MNLMM approach is proposed, called the MNLMM-CM, by taking the censored and non-ignorable missing responses into account simultaneously. The non-ignorable missingness is described by the selection-modeling factorization to tackle the missing not at random mechanism. A Monte Carlo expectation conditional maximization algorithm coupled with the first-order Taylor approximation is developed for parameter estimation. The techniques for the estimation of unobservable random effects, recovery of censored data, and imputation of missing responses are also provided. The proposed methodology is motivated and illustrated by the analysis of a clinical HIV/AIDS dataset with censored RNA viral loads and the presence of missing CD4 and CD8 cell counts. The superiority of the method in the provision of more adequate estimation is validated by a simulation study.
