ArticleStatistical methods in medical research2018
Dynamic longitudinal discriminant analysis using multiple longitudinal markers of different types.
Article in Statistical methods in medical research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Identification of patients who will not achieve seizure remission within 5 years on AEDs.Neurology · 2018Trial
- A Review of R Packages for Bayesian Model-based Clustering of High-dimensional Multivariate Environmental Exposures.Current environmental health reports · 2026Review
- A comparison of semi-parametric statistical modeling approaches to dynamic classification of irregularly and sparsely sampled curves.Statistical methods in medical research · 2025Article
- Building and validating trend-based multiple sclerosis case definitions: a population-based cohort study for Manitoba, Canada.BMJ open · 2024Article
- Adaptive data-driven selection of sequences of biological and cognitive markers in pre-clinical diagnosis of dementia.Scientific reports · 2023Article
- Fast approximate inference for multivariate longitudinal data.Biostatistics (Oxford, England) · 2022Article
- Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer's disease.Nature communications · 2022Article
- Repeated measures discriminant analysis using multivariate generalized estimation equations.Statistical methods in medical research · 2022Article
- Digital phenotyping of sleep patterns among heterogenous samples of Latinx adults using unsupervised learning.Sleep medicine · 2021Article
- EffUnet-SpaGen: An Efficient and Spatial Generative Approach to Glaucoma Detection.Journal of imaging · 2021Article
- A population-based study to develop juvenile arthritis case definitions for administrative health data using model-based dynamic classification.BMC medical research methodology · 2021Article
- The effect of random-effects misspecification on classification accuracy.The international journal of biostatistics · 2021Article
- Serum Levels of α-Fetoprotein Increased More Than 10 Years Before Detection of Hepatocellular Carcinoma.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2021Article
- Article
- Personalized risk-based screening for diabetic retinopathy: A multivariate approach versus the use of stratification rules.Diabetes, obesity & metabolism · 2019Article
- Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile.PloS one · 2019Article
- A comparison of group prediction approaches in longitudinal discriminant analysis.Biometrical journal. Biometrische Zeitschrift · 2018Article
- Dynamic classification using credible intervals in longitudinal discriminant analysis.Statistics in medicine · 2017Article
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4 authors.
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Abstract
There is an emerging need in clinical research to accurately predict patients' disease status and disease progression by optimally integrating multivariate clinical information. Clinical data are often collected over time for multiple biomarkers of different types (e.g. continuous, binary and counts). In this paper, we present a flexible and dynamic (time-dependent) discriminant analysis approach in which multiple biomarkers of various types are jointly modelled for classification purposes by the multivariate generalized linear mixed model. We propose a mixture of normal distributions for the random effects to allow additional flexibility when modelling the complex correlation between longitudinal biomarkers and to robustify the model and the classification procedure against misspecification of the random effects distribution. These longitudinal models are subsequently used in a multivariate time-dependent discriminant scheme to predict, at any time point, the probability of belonging to a particular risk group. The methodology is illustrated using clinical data from patients with epilepsy, where the aim is to identify patients who will not achieve remission of seizures within a five-year follow-up period.
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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.