Group-Specific Nonlinear Mixed Effects Models for Longitudinal Data With Realignment of Time

We propose group-specific nonlinear mixed effects models for longitudinal data whose indexing time is right-aligned to specific events, modeled as the time preceding those events. These models are motivated by dementia cohort studies that examine nonlinear trajectories of longitudinal cognitive test scores in late life, with separate trajectories for individuals with and without dementia in the period leading up to death. The model framework includes three sub-models: one for group membership, which represents dementia status in our motivating data, one for time to the indexing events, and one for the longitudinal outcome. Group membership is modeled using logistic regression or random forest. Conditional on group memberships, time to indexing events is modeled using accelerated failure time (AFT) models. Given both group membership and time to the indexing event, longitudinal data are modeled using nonlinear mixed effects models. By modeling and integrating out the time to indexing events, individuals whose indexing events have not been observed can be incorporated into the models. Stochastic approximation expectation maximization (SAEM) algorithms are adopted to maximize the likelihood for parameter estimation. In addition to modeling group-specific longitudinal trajectories aligned to the index time, the proposed models can also be used to predict group memberships or improve the prediction of time to the indexing events. Simulation studies are conducted to evaluate finite sample performance. An application to data from a dementia study is used for illustration.