Optimising assessment of dark adaptation data using time to event analysis

Abstract In age-related macular degeneration (AMD) research, dark adaptation has been found to be a promising functional measurement. In more severe cases of AMD, dark adaptation cannot always be recorded within a maximum allowed time for the test (~ 20–30 min). These data are recorded either as cen...

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Autores principales:	Bethany E. Higgins, Giovanni Montesano, Alison M. Binns, David P. Crabb
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fe920f79278b453ebd197ac51dae3f3a
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spelling	oai:doaj.org-article:fe920f79278b453ebd197ac51dae3f3a2021-12-02T14:26:25ZOptimising assessment of dark adaptation data using time to event analysis10.1038/s41598-021-86193-32045-2322https://doaj.org/article/fe920f79278b453ebd197ac51dae3f3a2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86193-3https://doaj.org/toc/2045-2322Abstract In age-related macular degeneration (AMD) research, dark adaptation has been found to be a promising functional measurement. In more severe cases of AMD, dark adaptation cannot always be recorded within a maximum allowed time for the test (~ 20–30 min). These data are recorded either as censored data-points (data capped at the maximum test time) or as an estimated recovery time based on the trend observed from the data recorded within the maximum recording time. Therefore, dark adaptation data can have unusual attributes that may not be handled by standard statistical techniques. Here we show time-to-event analysis is a more powerful method for analysis of rod-intercept time data in measuring dark adaptation. For example, at 80% power (at α = 0.05) sample sizes were estimated to be 20 and 61 with uncapped (uncensored) and capped (censored) data using a standard t-test; these values improved to 12 and 38 when using the proposed time-to-event analysis. Our method can accommodate both skewed data and censored data points and offers the advantage of significantly reducing sample sizes when planning studies where this functional test is an outcome measure. The latter is important because designing trials and studies more efficiently equates to newer treatments likely being examined more efficiently.Bethany E. HigginsGiovanni MontesanoAlison M. BinnsDavid P. CrabbNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bethany E. Higgins Giovanni Montesano Alison M. Binns David P. Crabb Optimising assessment of dark adaptation data using time to event analysis
description	Abstract In age-related macular degeneration (AMD) research, dark adaptation has been found to be a promising functional measurement. In more severe cases of AMD, dark adaptation cannot always be recorded within a maximum allowed time for the test (~ 20–30 min). These data are recorded either as censored data-points (data capped at the maximum test time) or as an estimated recovery time based on the trend observed from the data recorded within the maximum recording time. Therefore, dark adaptation data can have unusual attributes that may not be handled by standard statistical techniques. Here we show time-to-event analysis is a more powerful method for analysis of rod-intercept time data in measuring dark adaptation. For example, at 80% power (at α = 0.05) sample sizes were estimated to be 20 and 61 with uncapped (uncensored) and capped (censored) data using a standard t-test; these values improved to 12 and 38 when using the proposed time-to-event analysis. Our method can accommodate both skewed data and censored data points and offers the advantage of significantly reducing sample sizes when planning studies where this functional test is an outcome measure. The latter is important because designing trials and studies more efficiently equates to newer treatments likely being examined more efficiently.
format	article
author	Bethany E. Higgins Giovanni Montesano Alison M. Binns David P. Crabb
author_facet	Bethany E. Higgins Giovanni Montesano Alison M. Binns David P. Crabb
author_sort	Bethany E. Higgins
title	Optimising assessment of dark adaptation data using time to event analysis
title_short	Optimising assessment of dark adaptation data using time to event analysis
title_full	Optimising assessment of dark adaptation data using time to event analysis
title_fullStr	Optimising assessment of dark adaptation data using time to event analysis
title_full_unstemmed	Optimising assessment of dark adaptation data using time to event analysis
title_sort	optimising assessment of dark adaptation data using time to event analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fe920f79278b453ebd197ac51dae3f3a
work_keys_str_mv	AT bethanyehiggins optimisingassessmentofdarkadaptationdatausingtimetoeventanalysis AT giovannimontesano optimisingassessmentofdarkadaptationdatausingtimetoeventanalysis AT alisonmbinns optimisingassessmentofdarkadaptationdatausingtimetoeventanalysis AT davidpcrabb optimisingassessmentofdarkadaptationdatausingtimetoeventanalysis
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Optimising assessment of dark adaptation data using time to event analysis

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