Development of a mixture model allowing for smoothing functions of longitudinal trajectories
Year: 2020
Type: article
Abstract: ABSTRACT In the health and social sciences, two types of mixture model have been widely used by researchers to identify heterogeneous trajectories of participants within a population: latent class growth analysis (LCGA) and the growth mixture model (GMM). Both methods parametrically model trajectories of individuals, and capture latent trajectory classes, by using an expectation-maximization (E-M) algorithm. However, parametric modeling of trajectories using polynomial functions or monotonic spline functions results in limited flexibility for modelling trajectories; as a result, group membership may not be classified accurately due to model misspecification. In this paper, we propose a mixture model (SMM) allowing for smoothing functions of trajectories using a modified E-M algorithm. In the E step, participants are reassigned to only one group for which the estimated trajectory is the most similar to the observed one; in the M step, trajectories are fitted using generalized additive mixed models (GAMM) with smoothing functions of time. This modified E-M algorithm is straightforward to implement using the recently released “gamm4” macro in R. The SMM can incorporate time-varying covariates and be applied to longitudinal data with normal, Bernoulli, and Poisson distributions. Simulation results show favorable performance of the SMM in terms of classification of group membership. The proposed method is illustrated by its application to body mass index data of individuals followed from adolescence to young adulthood and the relationship with incidence of cardiometabolic disease.
Institutions: Harvard University, Brigham and Women's Hospital, McLean Hospital
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: Boston Nutrition Obesity Research Center, Examining Trajectories of Lifestyle Factors Associated with Healthy Aging and Longevity Using a Novel Mixture Model, Reduction in frequency of drug use as a primary outcome and its relation to changes in health-related and other functional outcomes in stimulant use disorder trials, G9000045640, Integrating lifecourse approaches, biologic and digital phenotypes in support of heart and lung disease epidemiologic research
OpenAlex ID: W3093659242