Models to estimate biological variation components and interpretation of serial results: strengths and limitations

Biological variation (BV) has multiple applications in a variety of fields of clinical laboratory. The use of BV in statistical modeling is twofold. On the one hand, some models are used for the generation of BV estimates (within- and between-subject variability). Other models are built based on BV...

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Autores principales:	Díaz-Garzón Marco Jorge, Fernández-Calle Pilar, Ricós Carmen
Formato:	article
Lenguaje:	EN ES
Publicado:	De Gruyter 2020
Materias:	biological variation methods statistical design Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/9b586502ff424c0496877e243b315036
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spelling	oai:doaj.org-article:9b586502ff424c0496877e243b3150362021-12-05T14:10:39ZModels to estimate biological variation components and interpretation of serial results: strengths and limitations2628-491X10.1515/almed-2020-0063https://doaj.org/article/9b586502ff424c0496877e243b3150362020-08-01T00:00:00Zhttps://doi.org/10.1515/almed-2020-0063https://doaj.org/toc/2628-491XBiological variation (BV) has multiple applications in a variety of fields of clinical laboratory. The use of BV in statistical modeling is twofold. On the one hand, some models are used for the generation of BV estimates (within- and between-subject variability). Other models are built based on BV in combination with other factors to establish ranges of normality that will help the clinician interpret serial results for the same subject. There are two types of statistical models for the calculation of BV estimates: A. Direct methods, prospective studies designed to calculate BV estimates; i. Classic model: developed by Harris and Fraser, revised by the Working Group on Biological Variation of the European Federation of Laboratory Medicine. ii. Mixed-effect models. iii. Bayesian model. B. Indirect methods, retrospective studies to derive BV estimates from large databases of results. Big data. Understanding the characteristics of these models is crucial as they determine their applicability in different settings and populations. Models for defining ranges that help in the interpretation of individual serial results include: A. Reference change value and B. Bayesian data network. In summary, this review provides an overview of the models used to define BV components and others for the follow-up of patients. These models should be exploited in the future to personalize and improve the information provided by the clinical laboratory and get the best of the resources available.Díaz-Garzón Marco JorgeFernández-Calle PilarRicós CarmenDe Gruyterarticlebiological variationmethodsstatistical designMedical technologyR855-855.5ENESAdvances in Laboratory Medicine, Vol 1, Iss 3, Pp 409-37 (2020)
institution	DOAJ
collection	DOAJ
language	EN ES
topic	biological variation methods statistical design Medical technology R855-855.5
spellingShingle	biological variation methods statistical design Medical technology R855-855.5 Díaz-Garzón Marco Jorge Fernández-Calle Pilar Ricós Carmen Models to estimate biological variation components and interpretation of serial results: strengths and limitations
description	Biological variation (BV) has multiple applications in a variety of fields of clinical laboratory. The use of BV in statistical modeling is twofold. On the one hand, some models are used for the generation of BV estimates (within- and between-subject variability). Other models are built based on BV in combination with other factors to establish ranges of normality that will help the clinician interpret serial results for the same subject. There are two types of statistical models for the calculation of BV estimates: A. Direct methods, prospective studies designed to calculate BV estimates; i. Classic model: developed by Harris and Fraser, revised by the Working Group on Biological Variation of the European Federation of Laboratory Medicine. ii. Mixed-effect models. iii. Bayesian model. B. Indirect methods, retrospective studies to derive BV estimates from large databases of results. Big data. Understanding the characteristics of these models is crucial as they determine their applicability in different settings and populations. Models for defining ranges that help in the interpretation of individual serial results include: A. Reference change value and B. Bayesian data network. In summary, this review provides an overview of the models used to define BV components and others for the follow-up of patients. These models should be exploited in the future to personalize and improve the information provided by the clinical laboratory and get the best of the resources available.
format	article
author	Díaz-Garzón Marco Jorge Fernández-Calle Pilar Ricós Carmen
author_facet	Díaz-Garzón Marco Jorge Fernández-Calle Pilar Ricós Carmen
author_sort	Díaz-Garzón Marco Jorge
title	Models to estimate biological variation components and interpretation of serial results: strengths and limitations
title_short	Models to estimate biological variation components and interpretation of serial results: strengths and limitations
title_full	Models to estimate biological variation components and interpretation of serial results: strengths and limitations
title_fullStr	Models to estimate biological variation components and interpretation of serial results: strengths and limitations
title_full_unstemmed	Models to estimate biological variation components and interpretation of serial results: strengths and limitations
title_sort	models to estimate biological variation components and interpretation of serial results: strengths and limitations
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/9b586502ff424c0496877e243b315036
work_keys_str_mv	AT diazgarzonmarcojorge modelstoestimatebiologicalvariationcomponentsandinterpretationofserialresultsstrengthsandlimitations AT fernandezcallepilar modelstoestimatebiologicalvariationcomponentsandinterpretationofserialresultsstrengthsandlimitations AT ricoscarmen modelstoestimatebiologicalvariationcomponentsandinterpretationofserialresultsstrengthsandlimitations
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Models to estimate biological variation components and interpretation of serial results: strengths and limitations

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