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Let Continuous Outcome Variables Remain Continuous

The complementary log-log is an alternative to logistic model. In many areas of research, the outcome data are continuous. We aim to provide a procedure that allows the researcher to estimate the coefficients of the complementary log-log model without dichotomizing and without loss of information. W...

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Detalles Bibliográficos
Autores principales: Bakhshi, Enayatollah, McArdle, Brian, Mohammad, Kazem, Seifi, Behjat, Biglarian, Akbar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3368309/
https://www.ncbi.nlm.nih.gov/pubmed/22693539
http://dx.doi.org/10.1155/2012/639124
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author Bakhshi, Enayatollah
McArdle, Brian
Mohammad, Kazem
Seifi, Behjat
Biglarian, Akbar
author_facet Bakhshi, Enayatollah
McArdle, Brian
Mohammad, Kazem
Seifi, Behjat
Biglarian, Akbar
author_sort Bakhshi, Enayatollah
collection PubMed
description The complementary log-log is an alternative to logistic model. In many areas of research, the outcome data are continuous. We aim to provide a procedure that allows the researcher to estimate the coefficients of the complementary log-log model without dichotomizing and without loss of information. We show that the sample size required for a specific power of the proposed approach is substantially smaller than the dichotomizing method. We find that estimators derived from proposed method are consistently more efficient than dichotomizing method. To illustrate the use of proposed method, we employ the data arising from the NHSI.
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spelling pubmed-33683092012-06-12 Let Continuous Outcome Variables Remain Continuous Bakhshi, Enayatollah McArdle, Brian Mohammad, Kazem Seifi, Behjat Biglarian, Akbar Comput Math Methods Med Research Article The complementary log-log is an alternative to logistic model. In many areas of research, the outcome data are continuous. We aim to provide a procedure that allows the researcher to estimate the coefficients of the complementary log-log model without dichotomizing and without loss of information. We show that the sample size required for a specific power of the proposed approach is substantially smaller than the dichotomizing method. We find that estimators derived from proposed method are consistently more efficient than dichotomizing method. To illustrate the use of proposed method, we employ the data arising from the NHSI. Hindawi Publishing Corporation 2012 2012-05-29 /pmc/articles/PMC3368309/ /pubmed/22693539 http://dx.doi.org/10.1155/2012/639124 Text en Copyright © 2012 Enayatollah Bakhshi et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Bakhshi, Enayatollah
McArdle, Brian
Mohammad, Kazem
Seifi, Behjat
Biglarian, Akbar
Let Continuous Outcome Variables Remain Continuous
title Let Continuous Outcome Variables Remain Continuous
title_full Let Continuous Outcome Variables Remain Continuous
title_fullStr Let Continuous Outcome Variables Remain Continuous
title_full_unstemmed Let Continuous Outcome Variables Remain Continuous
title_short Let Continuous Outcome Variables Remain Continuous
title_sort let continuous outcome variables remain continuous
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3368309/
https://www.ncbi.nlm.nih.gov/pubmed/22693539
http://dx.doi.org/10.1155/2012/639124
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