Oksel, Ceyda and Granell, Raquel and Mahmoud, Osama and Custovic, Adnan and Henderson, A John and Breathing Together investigators, STELAR (2019) Causes of variability in latent phenotypes of childhood wheeze. Allergy & clinical immunology international, 143 (5). 1783-1790.e11. DOI https://doi.org/10.1016/j.jaci.2018.10.059
Oksel, Ceyda and Granell, Raquel and Mahmoud, Osama and Custovic, Adnan and Henderson, A John and Breathing Together investigators, STELAR (2019) Causes of variability in latent phenotypes of childhood wheeze. Allergy & clinical immunology international, 143 (5). 1783-1790.e11. DOI https://doi.org/10.1016/j.jaci.2018.10.059
Oksel, Ceyda and Granell, Raquel and Mahmoud, Osama and Custovic, Adnan and Henderson, A John and Breathing Together investigators, STELAR (2019) Causes of variability in latent phenotypes of childhood wheeze. Allergy & clinical immunology international, 143 (5). 1783-1790.e11. DOI https://doi.org/10.1016/j.jaci.2018.10.059
Abstract
Background Latent class analysis (LCA) has been used extensively to identify (latent) phenotypes of childhood wheezing. However, the number and trajectory of discovered phenotypes differed substantially between studies. Objective We sought to investigate sources of variability affecting the classification of phenotypes, identify key time points for data collection to understand wheeze heterogeneity, and ascertain the association of childhood wheeze phenotypes with asthma and lung function in adulthood. Methods We used LCA to derive wheeze phenotypes among 3167 participants in the ALSPAC cohort who had complete information on current wheeze recorded at 14 time points from birth to age 16½ years. We examined the effects of sample size and data collection age and intervals on the results and identified time points. We examined the associations of derived phenotypes with asthma and lung function at age 23 to 24 years. Results A relatively large sample size (>2000) underestimated the number of phenotypes under some conditions (eg, number of time points <11). Increasing the number of data points resulted in an increase in the optimal number of phenotypes, but an identical number of randomly selected follow-up points led to different solutions. A variable selection algorithm identified 8 informative time points (months 18, 42, 57, 81, 91, 140, 157, and 166). The proportion of asthmatic patients at age 23 to 24 years differed between phenotypes, whereas lung function was lower among persistent wheezers. Conclusions Sample size, frequency, and timing of data collection have a major influence on the number and type of wheeze phenotypes identified by using LCA in longitudinal data.
Item Type: | Article |
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Uncontrolled Keywords: | Adult; Age of Onset; Asthma; Bias; Child; Cohort Studies; Data Collection; Female; Humans; Latent Class Analysis; Male; Models, Statistical; Phenotype; Prevalence; Respiratory Sounds; Risk Factors; Sample Size; Young Adult; Breathing Together investigators; STELAR |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Mathematics, Statistics and Actuarial Science, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 05 Feb 2023 18:39 |
Last Modified: | 30 Oct 2024 16:18 |
URI: | http://repository.essex.ac.uk/id/eprint/32201 |
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