A pattern-mixture model for MNAR missing values in longitudinal categorical data: application to physical health and function trajectories in adults with cancer

Music City Center 

Longitudinal studies are highly exposed to missing values that may threaten the validity of statistical inferences. This project proposes a novel trajectory model for multivariate categorical longitudinal outcomes with non-ignorable missing values. In addition, the proposed model investigates associations between patient-level time-independent covariates and trajectory group memberships to provide a better understanding of resilience trajectories. The proposed model identifies trajectory groups based on categorical outcome variables and their missing patterns to deal with missing values in outcome variables that are possibly non-ignorable. To achieve this, it introduces two types of categorical latent variables. One is for summarizing response patterns and missing patterns (latent class variables), and the other is for summarizing longitudinal patterns (latent trajectories) of latent classes. In addition, the proposed model may investigate associations between latent trajectories and patient-level time-independent covariates. We employ the Expectation-Maximization algorithm to obtain the maximum likelihood estimates. We demonstrate the novelty of the proposed model via simulation studies and by analyzing the YUCAN data set.

Latent class analysis

Missing not at random

EM algorithm

Longitudinal data

Cancer resilience