Power and sample size determination in the Rasch model: evaluation of the robustness of a numerical method to non-normality of the latent trait.

Patient-reported outcomes (PRO) have gained importance in clinical and epidemiological research and aim at assessing quality of life, anxiety or fatigue for instance. Item Response Theory (IRT) models are increasingly used to validate and analyse PRO. Such models relate observed variables to a laten...

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Autores principales:	Alice Guilleux, Myriam Blanchin, Jean-Benoit Hardouin, Véronique Sébille
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/300ed274f9294bfcbac453582912f4d0
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spelling	oai:doaj.org-article:300ed274f9294bfcbac453582912f4d02021-11-18T08:38:04ZPower and sample size determination in the Rasch model: evaluation of the robustness of a numerical method to non-normality of the latent trait.1932-620310.1371/journal.pone.0083652https://doaj.org/article/300ed274f9294bfcbac453582912f4d02014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24427276/?tool=EBIhttps://doaj.org/toc/1932-6203Patient-reported outcomes (PRO) have gained importance in clinical and epidemiological research and aim at assessing quality of life, anxiety or fatigue for instance. Item Response Theory (IRT) models are increasingly used to validate and analyse PRO. Such models relate observed variables to a latent variable (unobservable variable) which is commonly assumed to be normally distributed. A priori sample size determination is important to obtain adequately powered studies to determine clinically important changes in PRO. In previous developments, the Raschpower method has been proposed for the determination of the power of the test of group effect for the comparison of PRO in cross-sectional studies with an IRT model, the Rasch model. The objective of this work was to evaluate the robustness of this method (which assumes a normal distribution for the latent variable) to violations of distributional assumption. The statistical power of the test of group effect was estimated by the empirical rejection rate in data sets simulated using a non-normally distributed latent variable. It was compared to the power obtained with the Raschpower method. In both cases, the data were analyzed using a latent regression Rasch model including a binary covariate for group effect. For all situations, both methods gave comparable results whatever the deviations from the model assumptions. Given the results, the Raschpower method seems to be robust to the non-normality of the latent trait for determining the power of the test of group effect.Alice GuilleuxMyriam BlanchinJean-Benoit HardouinVéronique SébillePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 1, p e83652 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alice Guilleux Myriam Blanchin Jean-Benoit Hardouin Véronique Sébille Power and sample size determination in the Rasch model: evaluation of the robustness of a numerical method to non-normality of the latent trait.
description	Patient-reported outcomes (PRO) have gained importance in clinical and epidemiological research and aim at assessing quality of life, anxiety or fatigue for instance. Item Response Theory (IRT) models are increasingly used to validate and analyse PRO. Such models relate observed variables to a latent variable (unobservable variable) which is commonly assumed to be normally distributed. A priori sample size determination is important to obtain adequately powered studies to determine clinically important changes in PRO. In previous developments, the Raschpower method has been proposed for the determination of the power of the test of group effect for the comparison of PRO in cross-sectional studies with an IRT model, the Rasch model. The objective of this work was to evaluate the robustness of this method (which assumes a normal distribution for the latent variable) to violations of distributional assumption. The statistical power of the test of group effect was estimated by the empirical rejection rate in data sets simulated using a non-normally distributed latent variable. It was compared to the power obtained with the Raschpower method. In both cases, the data were analyzed using a latent regression Rasch model including a binary covariate for group effect. For all situations, both methods gave comparable results whatever the deviations from the model assumptions. Given the results, the Raschpower method seems to be robust to the non-normality of the latent trait for determining the power of the test of group effect.
format	article
author	Alice Guilleux Myriam Blanchin Jean-Benoit Hardouin Véronique Sébille
author_facet	Alice Guilleux Myriam Blanchin Jean-Benoit Hardouin Véronique Sébille
author_sort	Alice Guilleux
title	Power and sample size determination in the Rasch model: evaluation of the robustness of a numerical method to non-normality of the latent trait.
title_short	Power and sample size determination in the Rasch model: evaluation of the robustness of a numerical method to non-normality of the latent trait.
title_full	Power and sample size determination in the Rasch model: evaluation of the robustness of a numerical method to non-normality of the latent trait.
title_fullStr	Power and sample size determination in the Rasch model: evaluation of the robustness of a numerical method to non-normality of the latent trait.
title_full_unstemmed	Power and sample size determination in the Rasch model: evaluation of the robustness of a numerical method to non-normality of the latent trait.
title_sort	power and sample size determination in the rasch model: evaluation of the robustness of a numerical method to non-normality of the latent trait.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/300ed274f9294bfcbac453582912f4d0
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Power and sample size determination in the Rasch model: evaluation of the robustness of a numerical method to non-normality of the latent trait.

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