Bayesian approach for a 2 x 2 crossover design with repeated measures: a simulation study
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta scientiarum. Technology (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61512 |
Resumo: | In crossover designs, the subjects receive all treatments, according to the groups of sequences formed. Therefore, if carryover effects are present in the model, inferences about the treatments effects become difficult. Furthermore, repeated measures of the response variable can be taken over time in the same experimental unit; however, these measures may be correlated. In this way, we aimed to analyze a 2 x 2 crossover design with repeated measures within the treatment period, using a Bayesian approach. A simulation study was performed to evaluate the performance. The posterior estimates of the model parameters were obtained under non-informative prior distributions and the normal likelihood function. The model performed well with a sample size of 20 subjects, showing even better results with samples of 100 subjects. With larger samples, exact tests for the differences in carryover effects and time effects were obtained. However, the test of time effect proved to be powerful even with small samples. In turn, considering carryover effects different from zero did not influence the estimates of treatment differences, although biased estimates of the period effect were obtained. |
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Bayesian approach for a 2 x 2 crossover design with repeated measures: a simulation studyBayesian approach for a 2 x 2 crossover design with repeated measures: a simulation studycarryover effects; longitudinal data; MCMC; prior distribution.carryover effects; longitudinal data; MCMC; prior distribution.In crossover designs, the subjects receive all treatments, according to the groups of sequences formed. Therefore, if carryover effects are present in the model, inferences about the treatments effects become difficult. Furthermore, repeated measures of the response variable can be taken over time in the same experimental unit; however, these measures may be correlated. In this way, we aimed to analyze a 2 x 2 crossover design with repeated measures within the treatment period, using a Bayesian approach. A simulation study was performed to evaluate the performance. The posterior estimates of the model parameters were obtained under non-informative prior distributions and the normal likelihood function. The model performed well with a sample size of 20 subjects, showing even better results with samples of 100 subjects. With larger samples, exact tests for the differences in carryover effects and time effects were obtained. However, the test of time effect proved to be powerful even with small samples. In turn, considering carryover effects different from zero did not influence the estimates of treatment differences, although biased estimates of the period effect were obtained.In crossover designs, the subjects receive all treatments, according to the groups of sequences formed. Therefore, if carryover effects are present in the model, inferences about the treatments effects become difficult. Furthermore, repeated measures of the response variable can be taken over time in the same experimental unit; however, these measures may be correlated. In this way, we aimed to analyze a 2 x 2 crossover design with repeated measures within the treatment period, using a Bayesian approach. A simulation study was performed to evaluate the performance. The posterior estimates of the model parameters were obtained under non-informative prior distributions and the normal likelihood function. The model performed well with a sample size of 20 subjects, showing even better results with samples of 100 subjects. With larger samples, exact tests for the differences in carryover effects and time effects were obtained. However, the test of time effect proved to be powerful even with small samples. In turn, considering carryover effects different from zero did not influence the estimates of treatment differences, although biased estimates of the period effect were obtained.Universidade Estadual De Maringá2023-11-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/6151210.4025/actascitechnol.v46i1.61512Acta Scientiarum. Technology; Vol 46 No 1 (2024): Em proceso; e61512Acta Scientiarum. Technology; v. 46 n. 1 (2024): Publicação contínua; e615121806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61512/751375156632Copyright (c) 2024 Acta Scientiarum. Technologyhttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessLopez, Yaciled Miranda Nogueira, Denismar Alves Beijo, Luiz Alberto2024-02-08T19:23:44Zoai:periodicos.uem.br/ojs:article/61512Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2024-02-08T19:23:44Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Bayesian approach for a 2 x 2 crossover design with repeated measures: a simulation study Bayesian approach for a 2 x 2 crossover design with repeated measures: a simulation study |
title |
Bayesian approach for a 2 x 2 crossover design with repeated measures: a simulation study |
spellingShingle |
Bayesian approach for a 2 x 2 crossover design with repeated measures: a simulation study Lopez, Yaciled Miranda carryover effects; longitudinal data; MCMC; prior distribution. carryover effects; longitudinal data; MCMC; prior distribution. |
title_short |
Bayesian approach for a 2 x 2 crossover design with repeated measures: a simulation study |
title_full |
Bayesian approach for a 2 x 2 crossover design with repeated measures: a simulation study |
title_fullStr |
Bayesian approach for a 2 x 2 crossover design with repeated measures: a simulation study |
title_full_unstemmed |
Bayesian approach for a 2 x 2 crossover design with repeated measures: a simulation study |
title_sort |
Bayesian approach for a 2 x 2 crossover design with repeated measures: a simulation study |
author |
Lopez, Yaciled Miranda |
author_facet |
Lopez, Yaciled Miranda Nogueira, Denismar Alves Beijo, Luiz Alberto |
author_role |
author |
author2 |
Nogueira, Denismar Alves Beijo, Luiz Alberto |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Lopez, Yaciled Miranda Nogueira, Denismar Alves Beijo, Luiz Alberto |
dc.subject.por.fl_str_mv |
carryover effects; longitudinal data; MCMC; prior distribution. carryover effects; longitudinal data; MCMC; prior distribution. |
topic |
carryover effects; longitudinal data; MCMC; prior distribution. carryover effects; longitudinal data; MCMC; prior distribution. |
description |
In crossover designs, the subjects receive all treatments, according to the groups of sequences formed. Therefore, if carryover effects are present in the model, inferences about the treatments effects become difficult. Furthermore, repeated measures of the response variable can be taken over time in the same experimental unit; however, these measures may be correlated. In this way, we aimed to analyze a 2 x 2 crossover design with repeated measures within the treatment period, using a Bayesian approach. A simulation study was performed to evaluate the performance. The posterior estimates of the model parameters were obtained under non-informative prior distributions and the normal likelihood function. The model performed well with a sample size of 20 subjects, showing even better results with samples of 100 subjects. With larger samples, exact tests for the differences in carryover effects and time effects were obtained. However, the test of time effect proved to be powerful even with small samples. In turn, considering carryover effects different from zero did not influence the estimates of treatment differences, although biased estimates of the period effect were obtained. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-11-03 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61512 10.4025/actascitechnol.v46i1.61512 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61512 |
identifier_str_mv |
10.4025/actascitechnol.v46i1.61512 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/61512/751375156632 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2024 Acta Scientiarum. Technology http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2024 Acta Scientiarum. Technology http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
publisher.none.fl_str_mv |
Universidade Estadual De Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Technology; Vol 46 No 1 (2024): Em proceso; e61512 Acta Scientiarum. Technology; v. 46 n. 1 (2024): Publicação contínua; e61512 1806-2563 1807-8664 reponame:Acta scientiarum. Technology (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta scientiarum. Technology (Online) |
collection |
Acta scientiarum. Technology (Online) |
repository.name.fl_str_mv |
Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
||actatech@uem.br |
_version_ |
1799315338109648896 |