Flexible modeling of multiple nonlinear longitudinal trajectories with censored and non-ignorable missing outcomes

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 missed scheduled visits intermittently or discontinued participation. This article proposes an extension of the MNLMM approach, 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 calculation of standard errors of fixed effects, estimation of unobservable random effects, imputation of censored and missing responses and prediction of future values 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 our method on the provision of more adequate estimation is validated by a simulation study.

Likelihood‐based inference for linear mixed‐effects models using the generalized hyperbolic distribution

“…Future extensions of the work include the development of some diagnostics and tests for the model's adequacy. Moreover, we consider multiple outcomes from different populations, as in Wang et al (2018), Wang and Lin (2020), Wang and Lin (2022) and Wang and Lin (2023).…”

“…Moreover, we consider multiple outcomes from different populations, as in Wang et al (2018), Wang and Lin (2020), Wang and Lin (2022) and Wang and Lin (2023).…”