Recycling of predictors used to estimate glomerular filtration rate: Insight into lateral collinearity
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1371/journal.pone.0228842 http://hdl.handle.net/11449/198507 |
Resumo: | Background One overlooked problem in statistical analysis is lateral collinearity, a phenomenon that may occur when the outcome variable derives from the predictors. In nephrology this issue is seen with the use of estimated glomerular filtration rate (eGFR) as an outcome and age, sex, and ethnicity as predictors. In this study with simulated data, we aim to illustrate this problem. Methods We randomly generated unrelated data to estimate eGFR by common equations. Results Using simulated data, we show that age, gender, and ethnicity (recycled predictors variables) are statistically significantly correlated with eGFR in linear regression analysis. Whereas the initial obvious conclusion is that age, sex, and ethnicity are strong predictors of eGFR, more rigorous interpretation suggests that this is a byproduct of the mathematical model produced when deriving new predictors from another. Conclusion While statistical models have the ability to identify vertical collinearity (predictor-predictor), lateral collinearity (predictor-outcome) is seldom identified and discussed in statistical analysis. Therefore, caution is needed when interpreting the correlation between age, gender, and ethnicity with eGFR derived from regression analyses. |
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Repositório Institucional da UNESP |
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2946 |
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Recycling of predictors used to estimate glomerular filtration rate: Insight into lateral collinearityBackground One overlooked problem in statistical analysis is lateral collinearity, a phenomenon that may occur when the outcome variable derives from the predictors. In nephrology this issue is seen with the use of estimated glomerular filtration rate (eGFR) as an outcome and age, sex, and ethnicity as predictors. In this study with simulated data, we aim to illustrate this problem. Methods We randomly generated unrelated data to estimate eGFR by common equations. Results Using simulated data, we show that age, gender, and ethnicity (recycled predictors variables) are statistically significantly correlated with eGFR in linear regression analysis. Whereas the initial obvious conclusion is that age, sex, and ethnicity are strong predictors of eGFR, more rigorous interpretation suggests that this is a byproduct of the mathematical model produced when deriving new predictors from another. Conclusion While statistical models have the ability to identify vertical collinearity (predictor-predictor), lateral collinearity (predictor-outcome) is seldom identified and discussed in statistical analysis. Therefore, caution is needed when interpreting the correlation between age, gender, and ethnicity with eGFR derived from regression analyses.Department of Internal Medicine UNESP Univ Estadual PaulistaHospital do Rim Universidade Federal de São PauloUniv Estadual PaulistaUniversidade Federal de São PauloDepartment of Internal Medicine UNESP Univ Estadual PaulistaUniv Estadual PaulistaUniversidade Estadual Paulista (Unesp)Universidade Federal de São Paulo (UNIFESP)de Andrade, Luis Gustavo Modelli [UNESP]Tedesco-Silva, Helio2020-12-12T01:14:44Z2020-12-12T01:14:44Z2020-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1371/journal.pone.0228842PLoS ONE, v. 15, n. 2, 2020.1932-6203http://hdl.handle.net/11449/19850710.1371/journal.pone.02288422-s2.0-85079302639Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPLoS ONEinfo:eu-repo/semantics/openAccess2021-10-22T13:12:57Zoai:repositorio.unesp.br:11449/198507Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:35:35.588173Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Recycling of predictors used to estimate glomerular filtration rate: Insight into lateral collinearity |
title |
Recycling of predictors used to estimate glomerular filtration rate: Insight into lateral collinearity |
spellingShingle |
Recycling of predictors used to estimate glomerular filtration rate: Insight into lateral collinearity de Andrade, Luis Gustavo Modelli [UNESP] |
title_short |
Recycling of predictors used to estimate glomerular filtration rate: Insight into lateral collinearity |
title_full |
Recycling of predictors used to estimate glomerular filtration rate: Insight into lateral collinearity |
title_fullStr |
Recycling of predictors used to estimate glomerular filtration rate: Insight into lateral collinearity |
title_full_unstemmed |
Recycling of predictors used to estimate glomerular filtration rate: Insight into lateral collinearity |
title_sort |
Recycling of predictors used to estimate glomerular filtration rate: Insight into lateral collinearity |
author |
de Andrade, Luis Gustavo Modelli [UNESP] |
author_facet |
de Andrade, Luis Gustavo Modelli [UNESP] Tedesco-Silva, Helio |
author_role |
author |
author2 |
Tedesco-Silva, Helio |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Federal de São Paulo (UNIFESP) |
dc.contributor.author.fl_str_mv |
de Andrade, Luis Gustavo Modelli [UNESP] Tedesco-Silva, Helio |
description |
Background One overlooked problem in statistical analysis is lateral collinearity, a phenomenon that may occur when the outcome variable derives from the predictors. In nephrology this issue is seen with the use of estimated glomerular filtration rate (eGFR) as an outcome and age, sex, and ethnicity as predictors. In this study with simulated data, we aim to illustrate this problem. Methods We randomly generated unrelated data to estimate eGFR by common equations. Results Using simulated data, we show that age, gender, and ethnicity (recycled predictors variables) are statistically significantly correlated with eGFR in linear regression analysis. Whereas the initial obvious conclusion is that age, sex, and ethnicity are strong predictors of eGFR, more rigorous interpretation suggests that this is a byproduct of the mathematical model produced when deriving new predictors from another. Conclusion While statistical models have the ability to identify vertical collinearity (predictor-predictor), lateral collinearity (predictor-outcome) is seldom identified and discussed in statistical analysis. Therefore, caution is needed when interpreting the correlation between age, gender, and ethnicity with eGFR derived from regression analyses. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T01:14:44Z 2020-12-12T01:14:44Z 2020-02-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1371/journal.pone.0228842 PLoS ONE, v. 15, n. 2, 2020. 1932-6203 http://hdl.handle.net/11449/198507 10.1371/journal.pone.0228842 2-s2.0-85079302639 |
url |
http://dx.doi.org/10.1371/journal.pone.0228842 http://hdl.handle.net/11449/198507 |
identifier_str_mv |
PLoS ONE, v. 15, n. 2, 2020. 1932-6203 10.1371/journal.pone.0228842 2-s2.0-85079302639 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
PLoS ONE |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129534638161920 |