Leveraging vibration of effects analysis for robust discovery in observational biomedical data science.

Hypothesis generation in observational, biomedical data science often starts with computing an association or identifying the statistical relationship between a dependent and an independent variable. However, the outcome of this process depends fundamentally on modeling strategy, with differing stra...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Braden T Tierney, Elizabeth Anderson, Yingxuan Tan, Kajal Claypool, Sivateja Tangirala, Aleksandar D Kostic, Arjun K Manrai, Chirag J Patel
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/7a473add305f4aa796f527371dfa9008
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7a473add305f4aa796f527371dfa9008
record_format	dspace
spelling	oai:doaj.org-article:7a473add305f4aa796f527371dfa90082021-12-02T19:54:35ZLeveraging vibration of effects analysis for robust discovery in observational biomedical data science.1544-91731545-788510.1371/journal.pbio.3001398https://doaj.org/article/7a473add305f4aa796f527371dfa90082021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pbio.3001398https://doaj.org/toc/1544-9173https://doaj.org/toc/1545-7885Hypothesis generation in observational, biomedical data science often starts with computing an association or identifying the statistical relationship between a dependent and an independent variable. However, the outcome of this process depends fundamentally on modeling strategy, with differing strategies generating what can be called "vibration of effects" (VoE). VoE is defined by variation in associations that often lead to contradictory results. Here, we present a computational tool capable of modeling VoE in biomedical data by fitting millions of different models and comparing their output. We execute a VoE analysis on a series of widely reported associations (e.g., carrot intake associated with eyesight) with an extended additional focus on lifestyle exposures (e.g., physical activity) and components of the Framingham Risk Score for cardiovascular health (e.g., blood pressure). We leveraged our tool for potential confounder identification, investigating what adjusting variables are responsible for conflicting models. We propose modeling VoE as a critical step in navigating discovery in observational data, discerning robust associations, and cataloging adjusting variables that impact model output.Braden T TierneyElizabeth AndersonYingxuan TanKajal ClaypoolSivateja TangiralaAleksandar D KosticArjun K ManraiChirag J PatelPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Biology, Vol 19, Iss 9, p e3001398 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Braden T Tierney Elizabeth Anderson Yingxuan Tan Kajal Claypool Sivateja Tangirala Aleksandar D Kostic Arjun K Manrai Chirag J Patel Leveraging vibration of effects analysis for robust discovery in observational biomedical data science.
description	Hypothesis generation in observational, biomedical data science often starts with computing an association or identifying the statistical relationship between a dependent and an independent variable. However, the outcome of this process depends fundamentally on modeling strategy, with differing strategies generating what can be called "vibration of effects" (VoE). VoE is defined by variation in associations that often lead to contradictory results. Here, we present a computational tool capable of modeling VoE in biomedical data by fitting millions of different models and comparing their output. We execute a VoE analysis on a series of widely reported associations (e.g., carrot intake associated with eyesight) with an extended additional focus on lifestyle exposures (e.g., physical activity) and components of the Framingham Risk Score for cardiovascular health (e.g., blood pressure). We leveraged our tool for potential confounder identification, investigating what adjusting variables are responsible for conflicting models. We propose modeling VoE as a critical step in navigating discovery in observational data, discerning robust associations, and cataloging adjusting variables that impact model output.
format	article
author	Braden T Tierney Elizabeth Anderson Yingxuan Tan Kajal Claypool Sivateja Tangirala Aleksandar D Kostic Arjun K Manrai Chirag J Patel
author_facet	Braden T Tierney Elizabeth Anderson Yingxuan Tan Kajal Claypool Sivateja Tangirala Aleksandar D Kostic Arjun K Manrai Chirag J Patel
author_sort	Braden T Tierney
title	Leveraging vibration of effects analysis for robust discovery in observational biomedical data science.
title_short	Leveraging vibration of effects analysis for robust discovery in observational biomedical data science.
title_full	Leveraging vibration of effects analysis for robust discovery in observational biomedical data science.
title_fullStr	Leveraging vibration of effects analysis for robust discovery in observational biomedical data science.
title_full_unstemmed	Leveraging vibration of effects analysis for robust discovery in observational biomedical data science.
title_sort	leveraging vibration of effects analysis for robust discovery in observational biomedical data science.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7a473add305f4aa796f527371dfa9008
work_keys_str_mv	AT bradenttierney leveragingvibrationofeffectsanalysisforrobustdiscoveryinobservationalbiomedicaldatascience AT elizabethanderson leveragingvibrationofeffectsanalysisforrobustdiscoveryinobservationalbiomedicaldatascience AT yingxuantan leveragingvibrationofeffectsanalysisforrobustdiscoveryinobservationalbiomedicaldatascience AT kajalclaypool leveragingvibrationofeffectsanalysisforrobustdiscoveryinobservationalbiomedicaldatascience AT sivatejatangirala leveragingvibrationofeffectsanalysisforrobustdiscoveryinobservationalbiomedicaldatascience AT aleksandardkostic leveragingvibrationofeffectsanalysisforrobustdiscoveryinobservationalbiomedicaldatascience AT arjunkmanrai leveragingvibrationofeffectsanalysisforrobustdiscoveryinobservationalbiomedicaldatascience AT chiragjpatel leveragingvibrationofeffectsanalysisforrobustdiscoveryinobservationalbiomedicaldatascience
_version_	1718375908745674752

Leveraging vibration of effects analysis for robust discovery in observational biomedical data science.

Ejemplares similares