ArticleStatistics in medicine2017
Targeted use of growth mixture modeling: a learning perspective.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Article
- Cluster analysis for longitudinal data and its application in the detection of adiposity trajectories.Statistical methods in medical research · 2026Article
- Prognostic value of blood glucose trajectories in critically ill patients with intracerebral hemorrhage: A retrospective cohort study.PloS one · 2026Article
- Trajectories and predictors of frailty in older patients undergoing radical prostatectomy: A longitudinal study.Asia-Pacific journal of oncology nursing · 2025Article
- Impact of anion gap on in-hospital mortality in trauma-induced coagulopathy patients: a MIMIC-IV study.European journal of medical research · 2025Article
- Investigating the Evolution of Student Attitudes toward Science in a General Chemistry Course Using Latent Class and Latent Transition Analysis.Journal of chemical education · 2025Article
- Effect of changes trajectory of serum phosphate levels on the 28-day mortality risk in patients with sepsis: a retrospective cohort study from the MIMIC-IV database.BMC infectious diseases · 2025Article
- Cross-Lagged Panel Networks of Distinct Complex Post-Traumatic Stress Disorder Symptom Trajectories Among Young Adults With Adverse Childhood Experiences.Depression and anxiety · 2025Article
- Association between blood glucose level trajectories and 30-day mortality risk in patients with acute ischemic stroke: analysis of the MIMIC database 2001-2019.Diabetology & metabolic syndrome · 2024Article
- Trajectories of depression and predictors in lung cancer patients undergoing chemotherapy: growth mixture model.BMC psychiatry · 2024Article
- Characterizing the post-traumatic growth trajectory in gastric cancer survivors: a population-based longitudinal study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Article
- Prognostic subgroups of chronic pain patients using latent variable mixture modeling within a supervised machine learning framework.Scientific reports · 2024Article
- Differential symptom weighting in estimating empirical thresholds for underlying PTSD severity: Toward a "platinum" standard for diagnosis?International journal of methods in psychiatric research · 2023Article
- Facilitating Growth Mixture Model Convergence in Preventive Interventions.Prevention science : the official journal of the Society for Prevention Research · 2023Review
- Semiparametric Latent Class Analysis of Recurrent Event Data.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2022Article
- Trajectories and predictors of vicarious traumatization in Chinese college students during the COVID-19 pandemic: A longitudinal study.Frontiers in psychiatry · 2022Article
- Trajectories of Structural Disease Progression in Knee Osteoarthritis.Arthritis care & research · 2021Observational
- Supervised Machine Learning: A Brief Primer.Behavior therapy · 2020Review
- Defining persistent critical illness based on growth trajectories in patients with sepsis.Critical care (London, England) · 2020Article
- The use of clustering algorithms in critical care research to unravel patient heterogeneity.Intensive care medicine · 2019Article
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Authors and funding
8 authors.
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Abstract
From the statistical learning perspective, this paper shows a new direction for the use of growth mixture modeling (GMM), a method of identifying latent subpopulations that manifest heterogeneous outcome trajectories. In the proposed approach, we utilize the benefits of the conventional use of GMM for the purpose of generating potential candidate models based on empirical model fitting, which can be viewed as unsupervised learning. We then evaluate candidate GMM models on the basis of a direct measure of success; how well the trajectory types are predicted by clinically and demographically relevant baseline features, which can be viewed as supervised learning. We examine the proposed approach focusing on a particular utility of latent trajectory classes, as outcomes that can be used as valid prediction targets in clinical prognostic models. Our approach is illustrated using data from the Longitudinal Assessment of Manic Symptoms study. Copyright © 2016 John Wiley & Sons, Ltd.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.