A comparison of methods for clustering longitudinal data with slowly changing trends

Teuling, N. G. P. Den; Pauws, Steffen; Heuvel, Edwin R. van den

doi:10.1080/03610918.2020.1861464

Cited by 24 publications

(23 citation statements)

References 47 publications

(78 reference statements)

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“…Future studies will focus on the comparison between the different models and approaches presented here. Simulation work has already been done [ 86 , 87 , 88 , 89 ], but not in this specific framework.…”

Section: Discussionmentioning

confidence: 99%

Methodological Issues in Analyzing Real-World Longitudinal Occupational Health Data: A Useful Guide to Approaching the Topic

Colin

Dutheil

Cambier³

et al. 2022

IJERPH

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Ever greater technological advances and democratization of digital tools such as computers and smartphones offer researchers new possibilities to collect large amounts of health data in order to conduct clinical research. Such data, called real-world data, appears to be a perfect complement to traditional randomized clinical trials and has become more important in health decisions. Due to its longitudinal nature, real-world data is subject to specific and well-known methodological issues, namely issues with the analysis of cluster-correlated data, missing data and longitudinal data itself. These concepts have been widely discussed in the literature and many methods and solutions have been proposed to cope with these issues. As examples, mixed and trajectory models have been developed to explore longitudinal data sets, imputation methods can resolve missing data issues, and multilevel models facilitate the treatment of cluster-correlated data. Nevertheless, the analysis of real-world longitudinal occupational health data remains difficult, especially when the methodological challenges overlap. The purpose of this article is to present various solutions developed in the literature to deal with cluster-correlated data, missing data and longitudinal data, sometimes overlapped, in an occupational health context. The novelty and usefulness of our approach is supported by a step-by-step search strategy and an example from the Wittyfit database, which is an epidemiological database of occupational health data. Therefore, we hope that this article will facilitate the work of researchers in the field and improve the accuracy of future studies.

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Section: Discussionmentioning

confidence: 99%

Methodological Issues in Analyzing Real-World Longitudinal Occupational Health Data: A Useful Guide to Approaching the Topic

Colin

Dutheil

Cambier³

et al. 2022

IJERPH

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show abstract

“…Even recently, Rodriguez [73] tested several clustering methods including

-means, Hierarchical, and other probabilistic methods while the authors in Den Teuling et al. [74] investigated the performance of five longitudinal clustering methods using Monte Carlo simulations on synthetic datasets, representing various scenarios involving polynomial time profiles.…”

Section: Methodsmentioning

confidence: 99%

New confinement index and new perspective for comparing countries - COVID-19

Costa

Garcia

2021

Computer Methods and Programs in Biomedicine

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Background and objective: In the difficult problem of comparing countries regarding their lockdown measures or deaths caused by the COVID-19, there is still no agreement on what is the best strategy to follow. Thus, we propose a new way of comparison countries that avoids the main difficulties in the comparison by using three-dimensional trajectories for this type of data. Methods: We introduce a new index to analyze the level of confinement that each country was subject to overtime, based on the Community Mobility Reports published by Google resorting to Principal Component Analysis. Subsequently, by using longitudinal clustering, we divide the European countries into similar groups according to the COVID-19 obits and also to the confinement index. However, to make the most out of the clustering methods we resort to artificial longitudinal data to evaluate both the methods and the indices. Results: By using artificial data, we discover that Calinski–Harabasz outperformed other internal indices in indicating the real number of clusters. The tests also suggested that -means with Euclidean distance was the best method among the ones studied. With the application to both the mobility and fatalities datasets, we found two groups in each one. Conclusions: Our analysis enables us to discover that European northern countries had more mobility during the first confinement and that the deaths caused by COVID-19 started to drop around the 40th day since the first death.

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“…A number of authors compare the assumptions and use of the main approaches to GMM ( 18 , 32 , 43 , 69 ). Nagin and Odgers ( 43 ) argue that while there are technical differences between these approaches (i.e., they make different assumptions about the distribution of trajectories in the population), they are all designed to assign individuals into trajectory groups.…”

Section: Person-centered Approaches and Their Application In Asr Phenotype Researchmentioning

confidence: 99%

“…Berlin et al discuss when and how a researcher might use LCGA and GMM, including a step-by-step account of processes in the identification of latent trajectory subgroups ( 18 , 48 ). After conducting Monte Carlo simulations of synthetic data, Den Teuling et al ( 69 ) concluded that GMM provided the “best overall performance.”…”

Section: Person-centered Approaches and Their Application In Asr Phenotype Researchmentioning

confidence: 99%

Conceptualizing and Measuring Appetite Self-Regulation Phenotypes and Trajectories in Childhood: A Review of Person-Centered Strategies

2021

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This review uses person-centered research and data analysis strategies to discuss the conceptualization and measurement of appetite self-regulation (ASR) phenotypes and trajectories in childhood (from infancy to about ages 6 or 7 years). Research that is person-centered provides strategies that increase the possibilities for investigating ASR phenotypes. We first examine the utility of examining underlying phenotypes using latent profile/class analysis drawing on cross-sectional data. The use of trajectory analysis to investigate developmental change is then discussed, with attention to phenotypes using trajectories of individual behaviors as well as phenotypes based on multi-trajectory modeling. Data analysis strategies and measurement approaches from recent examples of these person-centered approaches to the conceptualization and investigation of appetite self-regulation and its development in childhood are examined. Where relevant, examples from older children as well as developmental, clinical and educational psychology are drawn on to discuss when and how person-centered approaches can be used. We argue that there is scope to incorporate recent advances in biological and psychoneurological knowledge about appetite self-regulation as well as fundamental processes in the development of general self-regulation to enhance the examination of phenotypes and their trajectories across childhood (and beyond). The discussion and conclusion suggest directions for future research and highlight the potential of person-centered approaches to progress knowledge about the development of appetite self-regulation in childhood.

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A comparison of methods for clustering longitudinal data with slowly changing trends

Cited by 24 publications

References 47 publications

Methodological Issues in Analyzing Real-World Longitudinal Occupational Health Data: A Useful Guide to Approaching the Topic

Methodological Issues in Analyzing Real-World Longitudinal Occupational Health Data: A Useful Guide to Approaching the Topic

New confinement index and new perspective for comparing countries - COVID-19

Conceptualizing and Measuring Appetite Self-Regulation Phenotypes and Trajectories in Childhood: A Review of Person-Centered Strategies

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