ArticleStatistical methods in medical research2021
Development of a mixture model allowing for smoothing functions of longitudinal trajectories.
Article in Statistical methods in medical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Modeling qualitative between-person heterogeneity in time series using latent class vector autoregressive models.Behavior research methods · 2025Article
- Maternal weight latent trajectories and associations with adverse pregnancy outcomes using a smoothing mixture model.Scientific reports · 2023Article
- Using linear and natural cubic splines, SITAR, and latent trajectory models to characterise nonlinear longitudinal growth trajectories in cohort studies.BMC medical research methodology · 2022Article
- A Longitudinal Study of Trajectories and Factors Influencing Patient-Reported Outcomes in Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2022Article
- Associations between patterns of modifiable risk factors in mid-life to late life and longevity: 36 year prospective cohort study.BMJ medicine · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
In the health and social sciences, two types of mixture models have been widely used by researchers to identify participants within a population with heterogeneous longitudinal trajectories: latent class growth analysis and the growth mixture model. Both methods parametrically model trajectories of individuals, and capture latent trajectory classes, using an expectation-maximization algorithm. However, parametric modeling of trajectories using polynomial functions or monotonic spline functions results in limited flexibility for modeling trajectories; as a result, group membership may not be classified accurately due to model misspecification. In this paper, we propose a smoothing mixture model allowing for smoothing functions of trajectories using a modified algorithm in the M step. Specifically, participants are reassigned to only one group for which the estimated trajectory is the most similar to the observed one; trajectories are fitted using generalized additive mixed models with smoothing functions of time within each of the resulting subsamples. The smoothing mixture model is straightforward to implement using the recently released "
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