Within-subject biological variation estimates using an indirect data mining strategy. Spanish multicenter pilot study (BiVaBiDa)

The estimates of biological variation (BV) have traditionally been determined using direct methods, which present limitations. In response to this issue, two papers have been published addressing these limitations by employing indirect methods. Here, we present a new procedure, based on indirect met...

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Published inClinical chemistry and laboratory medicine Vol. 60; no. 11; pp. 1804 - 1812
Main Authors Marqués-García, Fernando, Nieto-Librero, Ana, González-García, Nerea, Galindo-Villardón, Purificación, Martínez-Sánchez, Luisa María, Tejedor-Ganduxé, Xavier, Boned, Beatriz, Muñoz-Calero, María, García-Lario, Jose-Vicente, González-Lao, Elisabet, González-Tarancón, Ricardo, Fernández-Fernández, M. Pilar, Perich, Maria Carmen, Simón, Margarida, Díaz-Garzón, Jorge, Fernández-Calle, Pilar
Format Journal Article
LanguageEnglish
Published Berlin De Gruyter 26.10.2022
Walter De Gruyter & Company
Subjects
Online AccessGet full text
ISSN1434-6621
1437-4331
1437-4331
DOI10.1515/cclm-2021-0863

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Abstract The estimates of biological variation (BV) have traditionally been determined using direct methods, which present limitations. In response to this issue, two papers have been published addressing these limitations by employing indirect methods. Here, we present a new procedure, based on indirect methods that analyses data collected within a multicenter pilot study. Using this method, we obtain CVI estimates and calculate confidence intervals (CI), using the EFLM-BVD CVI estimates as gold standard for comparison.Data were collected over a 18-month period for 7 measurands, from 3 Spanish hospitals; inclusion criteria: patients 18–75 years with more than two determinations. For each measurand, four different strategies were carried out based on the coefficient of variation ratio (rCoeV) and based on the use of the bootstrap method (OS1, RS2 and RS3). RS2 and RS3 use symmetry reference change value (RCV) to clean database.RS2 and RS3 had the best correlation for the CVI estimates with respect to EFLM-BVD. RS2 used the symmetric RCV value without eliminating outliers, while RS3 combined RCV and outliers. When using the rCoeV and OS1 strategies, an overestimation of the CVI value was obtained.Our study presents a new strategy for obtaining robust CVI estimates using an indirect method together with the value of symmetric RCV to select the target population. The CVI estimates obtained show a good correlation with those published in the EFLM-BVD database. Furthermore, our strategy can resolve some of the limitations encountered when using direct methods such as calculating confidence intervals.
AbstractList The estimates of biological variation (BV) have traditionally been determined using direct methods, which present limitations. In response to this issue, two papers have been published addressing these limitations by employing indirect methods. Here, we present a new procedure, based on indirect methods that analyses data collected within a multicenter pilot study. Using this method, we obtain CVI estimates and calculate confidence intervals (CI), using the EFLM-BVD CVI estimates as gold standard for comparison.Data were collected over a 18-month period for 7 measurands, from 3 Spanish hospitals; inclusion criteria: patients 18–75 years with more than two determinations. For each measurand, four different strategies were carried out based on the coefficient of variation ratio (rCoeV) and based on the use of the bootstrap method (OS1, RS2 and RS3). RS2 and RS3 use symmetry reference change value (RCV) to clean database.RS2 and RS3 had the best correlation for the CVI estimates with respect to EFLM-BVD. RS2 used the symmetric RCV value without eliminating outliers, while RS3 combined RCV and outliers. When using the rCoeV and OS1 strategies, an overestimation of the CVI value was obtained.Our study presents a new strategy for obtaining robust CVI estimates using an indirect method together with the value of symmetric RCV to select the target population. The CVI estimates obtained show a good correlation with those published in the EFLM-BVD database. Furthermore, our strategy can resolve some of the limitations encountered when using direct methods such as calculating confidence intervals.
The estimates of biological variation (BV) have traditionally been determined using direct methods, which present limitations. In response to this issue, two papers have been published addressing these limitations by employing indirect methods. Here, we present a new procedure, based on indirect methods that analyses data collected within a multicenter pilot study. Using this method, we obtain CVI estimates and calculate confidence intervals (CI), using the EFLM-BVD CVI estimates as gold standard for comparison.OBJECTIVESThe estimates of biological variation (BV) have traditionally been determined using direct methods, which present limitations. In response to this issue, two papers have been published addressing these limitations by employing indirect methods. Here, we present a new procedure, based on indirect methods that analyses data collected within a multicenter pilot study. Using this method, we obtain CVI estimates and calculate confidence intervals (CI), using the EFLM-BVD CVI estimates as gold standard for comparison.Data were collected over a 18-month period for 7 measurands, from 3 Spanish hospitals; inclusion criteria: patients 18-75 years with more than two determinations. For each measurand, four different strategies were carried out based on the coefficient of variation ratio (rCoeV) and based on the use of the bootstrap method (OS1, RS2 and RS3). RS2 and RS3 use symmetry reference change value (RCV) to clean database.METHODSData were collected over a 18-month period for 7 measurands, from 3 Spanish hospitals; inclusion criteria: patients 18-75 years with more than two determinations. For each measurand, four different strategies were carried out based on the coefficient of variation ratio (rCoeV) and based on the use of the bootstrap method (OS1, RS2 and RS3). RS2 and RS3 use symmetry reference change value (RCV) to clean database.RS2 and RS3 had the best correlation for the CVI estimates with respect to EFLM-BVD. RS2 used the symmetric RCV value without eliminating outliers, while RS3 combined RCV and outliers. When using the rCoeV and OS1 strategies, an overestimation of the CVI value was obtained.RESULTSRS2 and RS3 had the best correlation for the CVI estimates with respect to EFLM-BVD. RS2 used the symmetric RCV value without eliminating outliers, while RS3 combined RCV and outliers. When using the rCoeV and OS1 strategies, an overestimation of the CVI value was obtained.Our study presents a new strategy for obtaining robust CVI estimates using an indirect method together with the value of symmetric RCV to select the target population. The CVI estimates obtained show a good correlation with those published in the EFLM-BVD database. Furthermore, our strategy can resolve some of the limitations encountered when using direct methods such as calculating confidence intervals.CONCLUSIONSOur study presents a new strategy for obtaining robust CVI estimates using an indirect method together with the value of symmetric RCV to select the target population. The CVI estimates obtained show a good correlation with those published in the EFLM-BVD database. Furthermore, our strategy can resolve some of the limitations encountered when using direct methods such as calculating confidence intervals.
Author Simón, Margarida
García-Lario, Jose-Vicente
Perich, Maria Carmen
Marqués-García, Fernando
Fernández-Calle, Pilar
González-Tarancón, Ricardo
González-García, Nerea
Boned, Beatriz
Díaz-Garzón, Jorge
Fernández-Fernández, M. Pilar
Muñoz-Calero, María
Galindo-Villardón, Purificación
Tejedor-Ganduxé, Xavier
González-Lao, Elisabet
Martínez-Sánchez, Luisa María
Nieto-Librero, Ana
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Cites_doi 10.1515/cclm-2018-0058
10.1093/clinchem/hvab100
10.1515/cclm-2016-0035
10.1373/clinchem.2018.290841
10.1373/clinchem.2015.252296
10.1309/AJCPHZLQAEYH94HI
10.1093/clinchem/hvaa233
10.1373/clinchem.2017.281808
10.1373/clinchem.2013.214312
10.1373/clinchem.2018.300145
10.3109/10408368909106595
10.1373/clinchem.2006.069369
10.1007/978-1-4899-4541-9_1
10.1016/j.clinbiochem.2020.06.014
10.21037/jlpm.2017.08.13
10.1373/clinchem.2011.168641
10.1198/106186004X12632
10.1515/almed-2020-0034
10.1515/almed-2020-0063
10.1515/cclm-2021-0442
10.1016/j.csda.2007.11.008
10.1373/clinchem.2012.187781
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References 2023033113340194633_j_cclm-2021-0863_ref_018
2023033113340194633_j_cclm-2021-0863_ref_019
2023033113340194633_j_cclm-2021-0863_ref_016
2023033113340194633_j_cclm-2021-0863_ref_017
2023033113340194633_j_cclm-2021-0863_ref_010
2023033113340194633_j_cclm-2021-0863_ref_011
2023033113340194633_j_cclm-2021-0863_ref_014
2023033113340194633_j_cclm-2021-0863_ref_015
2023033113340194633_j_cclm-2021-0863_ref_012
2023033113340194633_j_cclm-2021-0863_ref_013
2023033113340194633_j_cclm-2021-0863_ref_007
2023033113340194633_j_cclm-2021-0863_ref_008
2023033113340194633_j_cclm-2021-0863_ref_005
2023033113340194633_j_cclm-2021-0863_ref_006
2023033113340194633_j_cclm-2021-0863_ref_009
2023033113340194633_j_cclm-2021-0863_ref_021
2023033113340194633_j_cclm-2021-0863_ref_022
2023033113340194633_j_cclm-2021-0863_ref_020
2023033113340194633_j_cclm-2021-0863_ref_003
2023033113340194633_j_cclm-2021-0863_ref_025
2023033113340194633_j_cclm-2021-0863_ref_004
2023033113340194633_j_cclm-2021-0863_ref_026
2023033113340194633_j_cclm-2021-0863_ref_001
2023033113340194633_j_cclm-2021-0863_ref_023
2023033113340194633_j_cclm-2021-0863_ref_002
2023033113340194633_j_cclm-2021-0863_ref_024
References_xml – ident: 2023033113340194633_j_cclm-2021-0863_ref_026
  doi: 10.1515/cclm-2018-0058
– ident: 2023033113340194633_j_cclm-2021-0863_ref_010
  doi: 10.1093/clinchem/hvab100
– ident: 2023033113340194633_j_cclm-2021-0863_ref_009
  doi: 10.1515/cclm-2016-0035
– ident: 2023033113340194633_j_cclm-2021-0863_ref_014
  doi: 10.1373/clinchem.2018.290841
– ident: 2023033113340194633_j_cclm-2021-0863_ref_021
  doi: 10.1373/clinchem.2015.252296
– ident: 2023033113340194633_j_cclm-2021-0863_ref_013
  doi: 10.1309/AJCPHZLQAEYH94HI
– ident: 2023033113340194633_j_cclm-2021-0863_ref_020
– ident: 2023033113340194633_j_cclm-2021-0863_ref_001
– ident: 2023033113340194633_j_cclm-2021-0863_ref_002
  doi: 10.1093/clinchem/hvaa233
– ident: 2023033113340194633_j_cclm-2021-0863_ref_006
  doi: 10.1373/clinchem.2017.281808
– ident: 2023033113340194633_j_cclm-2021-0863_ref_023
  doi: 10.1373/clinchem.2013.214312
– ident: 2023033113340194633_j_cclm-2021-0863_ref_005
  doi: 10.1373/clinchem.2018.300145
– ident: 2023033113340194633_j_cclm-2021-0863_ref_004
  doi: 10.3109/10408368909106595
– ident: 2023033113340194633_j_cclm-2021-0863_ref_024
  doi: 10.1373/clinchem.2006.069369
– ident: 2023033113340194633_j_cclm-2021-0863_ref_022
  doi: 10.1007/978-1-4899-4541-9_1
– ident: 2023033113340194633_j_cclm-2021-0863_ref_011
  doi: 10.1016/j.clinbiochem.2020.06.014
– ident: 2023033113340194633_j_cclm-2021-0863_ref_017
– ident: 2023033113340194633_j_cclm-2021-0863_ref_007
– ident: 2023033113340194633_j_cclm-2021-0863_ref_008
  doi: 10.21037/jlpm.2017.08.13
– ident: 2023033113340194633_j_cclm-2021-0863_ref_016
  doi: 10.1373/clinchem.2011.168641
– ident: 2023033113340194633_j_cclm-2021-0863_ref_019
  doi: 10.1198/106186004X12632
– ident: 2023033113340194633_j_cclm-2021-0863_ref_012
  doi: 10.1515/almed-2020-0034
– ident: 2023033113340194633_j_cclm-2021-0863_ref_003
  doi: 10.1515/almed-2020-0063
– ident: 2023033113340194633_j_cclm-2021-0863_ref_015
  doi: 10.1515/cclm-2021-0442
– ident: 2023033113340194633_j_cclm-2021-0863_ref_018
  doi: 10.1016/j.csda.2007.11.008
– ident: 2023033113340194633_j_cclm-2021-0863_ref_025
  doi: 10.1373/clinchem.2012.187781
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Snippet The estimates of biological variation (BV) have traditionally been determined using direct methods, which present limitations. In response to this issue, two...
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SubjectTerms Biological variation
Coefficient of variation
Confidence intervals
Data mining
Estimates
indirect method
Mathematical analysis
Outliers (statistics)
reference change value
Statistical analysis
Statistical methods
Symmetry
Variation
Title Within-subject biological variation estimates using an indirect data mining strategy. Spanish multicenter pilot study (BiVaBiDa)
URI https://www.degruyter.com/doi/10.1515/cclm-2021-0863
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