Evaluation of multivariate longitudinal data accounting for missingness: methods and applications
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Abstract
Missing data are frequently encountered in biomedical research, especially in longitudinal studies. Multiple imputation (MI) is widely used to handle missing data due to missing at random (MAR). Two-stage MI is a flexible method that accounts for two types of missing data in a two-step process, allowing for diverse assumptions regarding missing mechanisms, such as MAR and missing not at random (MNAR). This method has immense potential, but its current application and extension are limited. Joint models provide another framework to address MNAR by simultaneously modeling both longitudinal and missingness processes. Joint models have been implemented in longitudinal studies for dementia progression to handle missing data due to MNAR, such as informative dropout due to dementia or death. Nonlinear mixed-effects models with latent time shifts are proposed to investigate long-term dementia progression. However, few studies incorporate these models into joint models to handle informative dropout. Furthermore, joint models with changepoints are proposed to identify the acceleration of cognitive decline before dementia onset, while accounting for informative dropout. Nevertheless, few joint models with changepoints consider semi-competing risks by distinguishing transitions between various health states. To address these knowledge gaps, this dissertation focuses on the methods and applications for handling missingness data in multivariate longitudinal data. This focus is reflected in three distinct projects. In project 1, we evaluate the performance of two-stage MI in a novel context. Specifically, we impute a longitudinal composite variable for cardiovascular health constructed from several continuous and binary components, while handling missing data due to MAR and MNAR. In project 2, we propose a joint model for cognitive decline that incorporates a multivariate nonlinear mixed-effects model with latent time shifts. We investigate different association structures between the longitudinal and missingness processes across various simulation settings. We also compare the proposed joint model with separate models that ignore the association between the longitudinal and missingness processes. In project 3, we propose a joint model that accounts for both changepoints and semi-competing risks by combining a multivariate random changepoint model for cognitive decline with an illness-death model for estimating health state transitions. We examine the proposed model with various types of random changepoint formulations and association structures. Overall, these projects provide insights into assessing cardiovascular and cognitive health in the presence of missingness.
Description
2024
License
Attribution-NoDerivatives 4.0 International