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Causes of variability in latent phenotypes of childhood wheeze

Causes of variability in latent phenotypes of childhood wheeze
Causes of variability in latent phenotypes of childhood wheeze
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: 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, 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 23-24 years. Results: relatively large sample size (>2000) underestimated the number of phenotypes under some conditions (e.g. 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 asthmatics at age 23-24 years differed between phenotypes, while 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 LCA in longitudinal data.
0091-6749
1783-1790
Oksel, Ceyda
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Granell, Raquel
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Mahmoud, Osama
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Custovic, Adnan
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Henderson, A. John
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Arshad, Syed Hasan
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Colicino, Silvia
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Cullinan, Paul
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Curtin, John
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Devereux, Graham
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Holloway, John
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Murray, Clare S.
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Roberts, Graham
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Simpson, Angela
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Turner, Steve
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Bush, Andrew
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Ghazal, Peter
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Grigg, Jonathan
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Lloyd, Clare M.
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Marsland, Benjamin
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Power, Ultan
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Saglani, Sejal
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Schwarze, Jurgen
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Shields, Mike
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Oksel, Ceyda
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Granell, Raquel
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Mahmoud, Osama
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Custovic, Adnan
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Henderson, A. John
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Arshad, Syed Hasan
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Colicino, Silvia
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Cullinan, Paul
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Curtin, John
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Devereux, Graham
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Holloway, John
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Murray, Clare S.
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Roberts, Graham
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Simpson, Angela
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Turner, Steve
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Bush, Andrew
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Ghazal, Peter
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Grigg, Jonathan
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Lloyd, Clare M.
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Marsland, Benjamin
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Power, Ultan
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Saglani, Sejal
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Schwarze, Jurgen
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Shields, Mike
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Oksel, Ceyda, Granell, Raquel, Mahmoud, Osama, Custovic, Adnan, Henderson, A. John, Arshad, Syed Hasan, Colicino, Silvia, Cullinan, Paul, Curtin, John, Devereux, Graham, Holloway, John, Murray, Clare S., Roberts, Graham, Simpson, Angela, Turner, Steve, Bush, Andrew, Ghazal, Peter, Grigg, Jonathan, Lloyd, Clare M., Marsland, Benjamin, Power, Ultan, Saglani, Sejal, Schwarze, Jurgen and Shields, Mike (2019) Causes of variability in latent phenotypes of childhood wheeze. Journal of Allergy and Clinical Immunology, 143 (5), 1783-1790. (doi:10.1016/j.jaci.2018.10.059).

Record type: Article

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: 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, 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 23-24 years. Results: relatively large sample size (>2000) underestimated the number of phenotypes under some conditions (e.g. 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 asthmatics at age 23-24 years differed between phenotypes, while 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 LCA in longitudinal data.

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1-s2.0-S0091674918317238-main - Accepted Manuscript
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Accepted/In Press date: 12 October 2018
e-pub ahead of print date: 5 December 2018
Published date: 1 May 2019

Identifiers

Local EPrints ID: 427027
URI: http://eprints.soton.ac.uk/id/eprint/427027
ISSN: 0091-6749
PURE UUID: 3b17ebb9-3878-435f-a3ac-021c6f93f0e3
ORCID for John Holloway: ORCID iD orcid.org/0000-0001-9998-0464
ORCID for Graham Roberts: ORCID iD orcid.org/0000-0003-2252-1248

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Date deposited: 20 Dec 2018 17:30
Last modified: 22 Nov 2021 02:52

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Contributors

Author: Ceyda Oksel
Author: Raquel Granell
Author: Osama Mahmoud
Author: Adnan Custovic
Author: A. John Henderson
Author: Silvia Colicino
Author: Paul Cullinan
Author: John Curtin
Author: Graham Devereux
Author: John Holloway ORCID iD
Author: Clare S. Murray
Author: Graham Roberts ORCID iD
Author: Angela Simpson
Author: Steve Turner
Author: Andrew Bush
Author: Peter Ghazal
Author: Jonathan Grigg
Author: Clare M. Lloyd
Author: Benjamin Marsland
Author: Ultan Power
Author: Sejal Saglani
Author: Jurgen Schwarze
Author: Mike Shields

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