ArticleBiometrical journal. Biometrische Zeitschrift2018
A comparison of group prediction approaches in longitudinal discriminant analysis.
Article in Biometrical journal. Biometrische Zeitschrift, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- A comparison of semi-parametric statistical modeling approaches to dynamic classification of irregularly and sparsely sampled curves.Statistical methods in medical research · 2025Article
- 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
- A comparison of group prediction approaches in longitudinal discriminant analysis.Biometrical journal. Biometrische Zeitschrift · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Longitudinal discriminant analysis (LoDA) can be used to classify patients into prognostic groups based on their clinical history, which often involves longitudinal measurements of various clinically relevant markers. Patients' longitudinal data is first modelled using multivariate generalised linear mixed models, allowing markers of different types (e.g. continuous, binary, counts) to be modelled simultaneously. We describe three approaches to calculating a patient's posterior group membership probabilities which have been outlined in previous studies, based on the marginal distribution of the longitudinal markers, conditional distribution and distribution of the random effects. Here we compare the three approaches, first using data from the Mayo Primary Biliary Cirrhosis study and then by way of simulation study to explore in which situations each of the three approaches is expected to give the best prediction. We demonstrate situations in which the marginal or random-effects approach perform well, but find that the conditional approach offers little extra information to the random-effects and marginal approaches.
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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.