ArticleStatistics in medicine2021
Capturing heterogeneity in repeated measures data by fusion penalty.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- A partially heterogeneous weighted fusion learning method for potential heterogeneous treatment effect in multi-site survival study.BMC medical research methodology · 2025Article
- High-dimensional mediation analysis for longitudinal mediators and survival outcomes.Briefings in bioinformatics · 2025Article
- Regression Trees With Fused Leaves.Statistics in medicine · 2024Article
- High-dimensional variable selection accounting for heterogeneity in regression coefficients across multiple data sources.The Canadian journal of statistics = Revue canadienne de statistique · 2024Article
- Health Care Provider Clustering Using Fusion Penalty in Quasi-Likelihood.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Healthcare center clustering for Cox's proportional hazards model by fusion penalty.Statistics in medicine · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
In this article, we are interested in capturing heterogeneity in clustered or longitudinal data. Traditionally such heterogeneity is modeled by either fixed effects (FE) or random effects (RE). In FE models, the degree of freedom for the heterogeneity equals the number of clusters/subjects minus 1, which could result in less efficiency. In RE models, the heterogeneity across different clusters/subjects is described by, for example, a random intercept with 1 parameter (for the variance of the random intercept), which could lead to oversimplification and biases (for the estimates of subject-specific effects). Our "fused effects" model stands in between these two approaches: we assume that there are unknown number of distinct levels of heterogeneity, and use the fusion penalty approach for estimation and inference. We evaluate and compare the performance of our method to the FE and RE models by simulation studies. We apply our method to the Ocular Hypertension Treatment Study to capture the heterogeneity in the progression rate of primary open-angle glaucoma of left and right eyes of different subjects.
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Registered trials
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.