Profile clustering in clinical trials with longitudinal and functional data methods

Gong, Huan; Xun, Xiaolei; Zhou, Yingchun

doi:10.1080/10543406.2019.1572614

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…clusters that were as heterogeneous as possible from each other). KmL has been shown to have good clustering performance, especially when the sample size or number of measurement points is small [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

Physical activity from adolescence to young adulthood: patterns of change, and their associations with activity domains and sedentary time

Aira

Vasankari

Heinonen

et al. 2021

Int J Behav Nutr Phys Act

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Background Longitudinal studies demonstrate an average decline in physical activity (PA) from adolescence to young adulthood. However, while some subgroups of adolescents decrease activity, others increase or maintain high or low activity. Activity domains may differ between subgroups (exhibiting different PA patterns), and they offer valuable information for targeted health promotion. Hence, the aim of this study was to identify PA patterns from adolescence to young adulthood; also to explore the associations of (i) changes in PA domains and in sedentary time, (ii) sociodemographic factors, and (iii) self-rated health with diverging PA patterns. Methods The observational cohort study data encompassed 254 adolescents at age 15 and age 19. K-means cluster analysis for longitudinal data was performed to identify participant clusters (patterns) based on their accelerometry-measured moderate-to-vigorous PA (MVPA). Logistic regressions were applied in further analysis. Results Five PA patterns were identified: inactivity maintainers (n = 71), activity maintainers (n = 70), decreasers from moderate (to low) PA (n = 61), decreasers from high (to moderate) PA (n = 32), and increasers (n = 20). At age 15, participation in sports clubs (SC, 41–97%) and active commuting (AC, 47–75%) was common in all the patterns. By age 19, clear dropout from these activities was prevalent (SC participation mean 32%, AC 31–63%). Inactivity maintainers reported the lowest amount of weekly school physical education. Dropout from SC – in contrast to non-participation in SC – was associated with higher odds of being a decreaser from high PA, and with lower odds of being an inactivity maintainer. Maintained SC participation was associated with higher odds of belonging to the decreasers from high PA, and to the combined group of activity maintainers and increasers; also with lower odds of being an inactivity maintainer. Maintenance/adoption of AC was associated with decreased odds of being an inactivity maintainer. Self-reported health at age 19 was associated with the patterns of maintained activity and inactivity. Conclusions PA patterns diverge over the transition to adulthood. Changes in SC participation and AC show different associations with diverging PA patterns. Hence, tailored PA promotion is recommended.

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Section: Methodsmentioning

confidence: 99%

Physical activity from adolescence to young adulthood: patterns of change, and their associations with activity domains and sedentary time

Aira

Vasankari

Heinonen

et al. 2021

Int J Behav Nutr Phys Act

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“…Among the limitations of this study are the use of the last menstrual period and Dubowitz methods to derive GA, which is less precise compared to early pregnancy ultrasound assessment. Moreover, the clustering approach tested here was not compared to other methods for clustering trajectories, although the performance of k-means using Fréchet distance is closely related to that of latent class growth analysis when trajectories vary smoothly with time 18 . However, the variability of clusters is expected within each clustering method and no standard for clustering has been established.…”

Section: Discussionmentioning

confidence: 99%

A novel shape-based approach to identify gestational age-adjusted growth patterns from birth to 11 years of age

López-Domínguez

Bassani

Bourdon

et al. 2023

Sci Rep

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Child growth patterns assessment is critical to design public health interventions. However, current analytical approaches may overlook population heterogeneity. To overcome this limitation, we developed a growth trajectories clustering pipeline that incorporates a shape-respecting distance, baseline centering (i.e., birth-size normalized trajectories) and Gestational Age (GA)-correction to characterize shape-based child growth patterns. We used data from 3945 children (461 preterm) in the 2004 Pelotas Birth Cohort with at least 3 measurements between birth (included) and 11 years of age. Sex-adjusted weight-, length/height- and body mass index-for-age z-scores were derived at birth, 3 months, and at 1, 2, 4, 6 and 11 years of age (INTERGROWTH-21st and WHO growth standards). Growth trajectories clustering was conducted for each anthropometric index using k-means and a shape-respecting distance, accounting or not for birth size and/or GA-correction. We identified 3 trajectory patterns for each anthropometric index: increasing (High), stable (Middle) and decreasing (Low). Baseline centering resulted in pattern classification that considered early life growth traits. GA-correction increased the intercepts of preterm-born children trajectories, impacting their pattern classification. Incorporating shape-based clustering, baseline centering and GA-correction in growth patterns analysis improves the identification of subgroups meaningful for public health interventions.

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“…The LCMM method was used to achieve a model-based longitudinal clustering of participant profiles in different groups according to their temporal evolution in DASS-21 depression, anxiety and stress scores ( 33 , 35 , 36 ).…”

Section: Methodsmentioning

confidence: 99%

Stress, anxiety, and depression trajectories during the “first wave” of the COVID-19 pandemic: what drives resilient, adaptive and maladaptive responses in the Portuguese population?

Fernandes,

Ferreira,

Moreira

et al. 2024

Front. Public Health

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IntroductionThe COVID-19 outbreak and the community mitigation strategies implemented to reduce new SARS-CoV-2 infections can be regarded as powerful stressors with negative consequences on people's mental health. Although it has been shown that negative emotional symptoms subside during lockdown, it is likely the existence of inter-individual differences in stress, anxiety and depression trajectories throughout lockdown.ObjectivesWe aimed to cluster participants' according to their trajectories of stress, anxiety and depression scores throughout lockdown, and identify the sociodemographic, clinical, and lifestyle factors that may distinguish the subjects included in the different clusters.MethodsFrom March 23, 2020, to May 31, 2020, participants completed weekly online questionnaires on sociodemographic information (age, sex, education level, and employment status), psychological functioning (DASS-21, NEO-FFI-20), and clinical data (psychiatric disorders, psychiatric medication, physical disorders). Data regarding smoking status, alcohol consumption, physical activity, and time spent daily looking for COVID-19-related information were also collected. Stress, anxiety and depression trajectories were determined using latent class mixed models.ResultsA total of 2040 participants answered the survey at baseline and 603 participants answered all surveys. Three groups (“Resilient,” “Recovered,” and “Maladaptive”) with distinct mental health trajectories were identified. Younger participants, women, participants with lower education level, not working, studying, diagnosed with a mental disorder, taking psychiatric medication, smokers, those who spent more time consuming COVID-19-related information and those with higher neuroticism tended to cluster in the “Maladaptive” group, placing them at higher risk of persistent negative emotional symptoms during compulsory confinement.ConclusionAccordingly, a tailored approach to emotional suffering for vulnerable subjects during the COVID-19 and future pandemics must be devised.

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Profile clustering in clinical trials with longitudinal and functional data methods

Cited by 9 publications

References 18 publications

Physical activity from adolescence to young adulthood: patterns of change, and their associations with activity domains and sedentary time

Physical activity from adolescence to young adulthood: patterns of change, and their associations with activity domains and sedentary time

A novel shape-based approach to identify gestational age-adjusted growth patterns from birth to 11 years of age

Stress, anxiety, and depression trajectories during the “first wave” of the COVID-19 pandemic: what drives resilient, adaptive and maladaptive responses in the Portuguese population?

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