A generalized model to estimate the statistical power in mitochondrial disease studies involving 2×k tables.

<h4>Background</h4>Mitochondrial DNA (mtDNA) variation (i.e. haplogroups) has been analyzed in regards to a number of multifactorial diseases. The statistical power of a case-control study determines the a priori probability to reject the null hypothesis of homogeneity between cases and...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jacobo Pardo-Seco, Jorge Amigo, Wenceslao González-Manteiga, Antonio Salas
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/294671a23263432891bf8d3d19a85b25
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:294671a23263432891bf8d3d19a85b25
record_format	dspace
spelling	oai:doaj.org-article:294671a23263432891bf8d3d19a85b252021-11-18T08:53:25ZA generalized model to estimate the statistical power in mitochondrial disease studies involving 2×k tables.1932-620310.1371/journal.pone.0073567https://doaj.org/article/294671a23263432891bf8d3d19a85b252013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24086285/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Mitochondrial DNA (mtDNA) variation (i.e. haplogroups) has been analyzed in regards to a number of multifactorial diseases. The statistical power of a case-control study determines the a priori probability to reject the null hypothesis of homogeneity between cases and controls.<h4>Methods/principal findings</h4>We critically review previous approaches to the estimation of the statistical power based on the restricted scenario where the number of cases equals the number of controls, and propose a methodology that broadens procedures to more general situations. We developed statistical procedures that consider different disease scenarios, variable sample sizes in cases and controls, and variable number of haplogroups and effect sizes. The results indicate that the statistical power of a particular study can improve substantially by increasing the number of controls with respect to cases. In the opposite direction, the power decreases substantially when testing a growing number of haplogroups. We developed mitPower (http://bioinformatics.cesga.es/mitpower/), a web-based interface that implements the new statistical procedures and allows for the computation of the a priori statistical power in variable scenarios of case-control study designs, or e.g. the number of controls needed to reach fixed effect sizes.<h4>Conclusions/significance</h4>The present study provides with statistical procedures for the computation of statistical power in common as well as complex case-control study designs involving 2×k tables, with special application (but not exclusive) to mtDNA studies. In order to reach a wide range of researchers, we also provide a friendly web-based tool--mitPower--that can be used in both retrospective and prospective case-control disease studies.Jacobo Pardo-SecoJorge AmigoWenceslao González-ManteigaAntonio SalasPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 9, p e73567 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jacobo Pardo-Seco Jorge Amigo Wenceslao González-Manteiga Antonio Salas A generalized model to estimate the statistical power in mitochondrial disease studies involving 2×k tables.
description	<h4>Background</h4>Mitochondrial DNA (mtDNA) variation (i.e. haplogroups) has been analyzed in regards to a number of multifactorial diseases. The statistical power of a case-control study determines the a priori probability to reject the null hypothesis of homogeneity between cases and controls.<h4>Methods/principal findings</h4>We critically review previous approaches to the estimation of the statistical power based on the restricted scenario where the number of cases equals the number of controls, and propose a methodology that broadens procedures to more general situations. We developed statistical procedures that consider different disease scenarios, variable sample sizes in cases and controls, and variable number of haplogroups and effect sizes. The results indicate that the statistical power of a particular study can improve substantially by increasing the number of controls with respect to cases. In the opposite direction, the power decreases substantially when testing a growing number of haplogroups. We developed mitPower (http://bioinformatics.cesga.es/mitpower/), a web-based interface that implements the new statistical procedures and allows for the computation of the a priori statistical power in variable scenarios of case-control study designs, or e.g. the number of controls needed to reach fixed effect sizes.<h4>Conclusions/significance</h4>The present study provides with statistical procedures for the computation of statistical power in common as well as complex case-control study designs involving 2×k tables, with special application (but not exclusive) to mtDNA studies. In order to reach a wide range of researchers, we also provide a friendly web-based tool--mitPower--that can be used in both retrospective and prospective case-control disease studies.
format	article
author	Jacobo Pardo-Seco Jorge Amigo Wenceslao González-Manteiga Antonio Salas
author_facet	Jacobo Pardo-Seco Jorge Amigo Wenceslao González-Manteiga Antonio Salas
author_sort	Jacobo Pardo-Seco
title	A generalized model to estimate the statistical power in mitochondrial disease studies involving 2×k tables.
title_short	A generalized model to estimate the statistical power in mitochondrial disease studies involving 2×k tables.
title_full	A generalized model to estimate the statistical power in mitochondrial disease studies involving 2×k tables.
title_fullStr	A generalized model to estimate the statistical power in mitochondrial disease studies involving 2×k tables.
title_full_unstemmed	A generalized model to estimate the statistical power in mitochondrial disease studies involving 2×k tables.
title_sort	generalized model to estimate the statistical power in mitochondrial disease studies involving 2×k tables.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/294671a23263432891bf8d3d19a85b25
work_keys_str_mv	AT jacobopardoseco ageneralizedmodeltoestimatethestatisticalpowerinmitochondrialdiseasestudiesinvolving2ktables AT jorgeamigo ageneralizedmodeltoestimatethestatisticalpowerinmitochondrialdiseasestudiesinvolving2ktables AT wenceslaogonzalezmanteiga ageneralizedmodeltoestimatethestatisticalpowerinmitochondrialdiseasestudiesinvolving2ktables AT antoniosalas ageneralizedmodeltoestimatethestatisticalpowerinmitochondrialdiseasestudiesinvolving2ktables AT jacobopardoseco generalizedmodeltoestimatethestatisticalpowerinmitochondrialdiseasestudiesinvolving2ktables AT jorgeamigo generalizedmodeltoestimatethestatisticalpowerinmitochondrialdiseasestudiesinvolving2ktables AT wenceslaogonzalezmanteiga generalizedmodeltoestimatethestatisticalpowerinmitochondrialdiseasestudiesinvolving2ktables AT antoniosalas generalizedmodeltoestimatethestatisticalpowerinmitochondrialdiseasestudiesinvolving2ktables
_version_	1718421190948683776

A generalized model to estimate the statistical power in mitochondrial disease studies involving 2×k tables.

Ejemplares similares