A Comparison of Frequentist and Bayesian
Approaches to Latent Class Modelling of
Susceptibility to Asthma and Patterns of
Antibiotic Prescriptions in Early Life

Danielle  Belgrave; Christopher Bishop; Adnan Custovic; Angela Simpson; Aida  Semic-Jusufagic; Andrew  Pickles; Iain  Buchan

A Comparison of Frequentist and Bayesian Approaches to Latent Class Modelling of Susceptibility to Asthma and Patterns of Antibiotic Prescriptions in Early Life

Danielle Belgrave, Christopher Bishop, Adnan Custovic, Angela Simpson, Aida Semic-Jusufagic, Andrew Pickles, Iain Buchan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The assessment of patterns of antibiotic use in early life may have major implications for understanding the development of asthma. This paper compares a classical generalized latent variable modelling framework and a Bayesian machine learning approach to define latent classes of susceptibility to asthma based on patterns of antibiotic use in early life. We compare the potential advantages of each method for elucidating clinically meaningful phenotypes or classes.

Original language	English
Title of host publication	Proceedings of the 26th International Workshop on Statistical Modelling. Valencia (Spain)
Pages	75-78
Number of pages	4
Publication status	Published - 2011

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Belgrave, D., Bishop, C., Custovic, A., Simpson, A., Semic-Jusufagic, A., Pickles, A., & Buchan, I. (2011). A Comparison of Frequentist and Bayesian Approaches to Latent Class Modelling of Susceptibility to Asthma and Patterns of Antibiotic Prescriptions in Early Life. In Proceedings of the 26th International Workshop on Statistical Modelling. Valencia (Spain) (pp. 75-78) http://www.statmod.org/workshops_archive_proceedings_2011.htm

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title = "A Comparison of Frequentist and Bayesian Approaches to Latent Class Modelling of Susceptibility to Asthma and Patterns of Antibiotic Prescriptions in Early Life",

abstract = "The assessment of patterns of antibiotic use in early life may have major implications for understanding the development of asthma. This paper compares a classical generalized latent variable modelling framework and a Bayesian machine learning approach to define latent classes of susceptibility to asthma based on patterns of antibiotic use in early life. We compare the potential advantages of each method for elucidating clinically meaningful phenotypes or classes.",

author = "Danielle Belgrave and Christopher Bishop and Adnan Custovic and Angela Simpson and Aida Semic-Jusufagic and Andrew Pickles and Iain Buchan",

year = "2011",

language = "English",

isbn = "978-84-694-5129-8",

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booktitle = "Proceedings of the 26th International Workshop on Statistical Modelling. Valencia (Spain)",

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Belgrave, D, Bishop, C, Custovic, A, Simpson, A, Semic-Jusufagic, A, Pickles, A & Buchan, I 2011, A Comparison of Frequentist and Bayesian Approaches to Latent Class Modelling of Susceptibility to Asthma and Patterns of Antibiotic Prescriptions in Early Life. in Proceedings of the 26th International Workshop on Statistical Modelling. Valencia (Spain). pp. 75-78. <http://www.statmod.org/workshops_archive_proceedings_2011.htm>

A Comparison of Frequentist and Bayesian Approaches to Latent Class Modelling of Susceptibility to Asthma and Patterns of Antibiotic Prescriptions in Early Life. / Belgrave, Danielle ; Bishop, Christopher; Custovic, Adnan et al.
Proceedings of the 26th International Workshop on Statistical Modelling. Valencia (Spain). 2011. p. 75-78.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A Comparison of Frequentist and Bayesian Approaches to Latent Class Modelling of Susceptibility to Asthma and Patterns of Antibiotic Prescriptions in Early Life

AU - Belgrave, Danielle

AU - Bishop, Christopher

AU - Custovic, Adnan

AU - Simpson, Angela

AU - Semic-Jusufagic, Aida

AU - Pickles, Andrew

AU - Buchan, Iain

PY - 2011

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N2 - The assessment of patterns of antibiotic use in early life may have major implications for understanding the development of asthma. This paper compares a classical generalized latent variable modelling framework and a Bayesian machine learning approach to define latent classes of susceptibility to asthma based on patterns of antibiotic use in early life. We compare the potential advantages of each method for elucidating clinically meaningful phenotypes or classes.

AB - The assessment of patterns of antibiotic use in early life may have major implications for understanding the development of asthma. This paper compares a classical generalized latent variable modelling framework and a Bayesian machine learning approach to define latent classes of susceptibility to asthma based on patterns of antibiotic use in early life. We compare the potential advantages of each method for elucidating clinically meaningful phenotypes or classes.

M3 - Conference contribution

SN - 978-84-694-5129-8

SP - 75

EP - 78

BT - Proceedings of the 26th International Workshop on Statistical Modelling. Valencia (Spain)

ER -

A Comparison of Frequentist and Bayesian Approaches to Latent Class Modelling of Susceptibility to Asthma and Patterns of Antibiotic Prescriptions in Early Life

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