Cargando…
An innovative approach based on real-world big data mining for calculating the sample size of the reference interval established using transformed parametric and non-parametric methods
BACKGROUND: Currently, the direct method is the main approach for establishment of reference interval (RI). However, only a handful of studies have described the effects of sample size on establishment of RI and estimation of sample size. We describe a novel approach for estimation of the sample siz...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585851/ https://www.ncbi.nlm.nih.gov/pubmed/36266618 http://dx.doi.org/10.1186/s12874-022-01751-1 |
_version_ | 1784813583215886336 |
---|---|
author | Ma, Chaochao Hou, Li’an Zou, Yutong Ma, Xiaoli Wang, Danchen Hu, Yingying Song, Ailing Cheng, Xinqi Qiu, Ling |
author_facet | Ma, Chaochao Hou, Li’an Zou, Yutong Ma, Xiaoli Wang, Danchen Hu, Yingying Song, Ailing Cheng, Xinqi Qiu, Ling |
author_sort | Ma, Chaochao |
collection | PubMed |
description | BACKGROUND: Currently, the direct method is the main approach for establishment of reference interval (RI). However, only a handful of studies have described the effects of sample size on establishment of RI and estimation of sample size. We describe a novel approach for estimation of the sample size when establishing RIs using the transformed parametric and non-parametric methods. METHODS: A total of 3,697 healthy participants were enrolled in this study. We adopted a two-layer nested loop sample size estimation method to determine the effects of sample size on RI, using thyroid-related hormone as an example. The sample size was selected as the calculation result when the width of the confidence interval (CI) of the upper and lower limit of the RI were both stably < 0.2 times the width of RI. Then, we calculated the sample size for establishing RIs via transformed parametric and non-parametric methods for thyroid-related hormones. RESULTS: Sample sizes for thyroid stimulating hormone (TSH), as required by parametric and non-parametric methods to establish RIs were 239 and 850, respectively. Sample sizes required by the transformed parametric method for free triiodothyronine (FT3), free thyroxine (FT4), total triiodothyronine (TT3) and total thyroxine (TT4) were all less than 120, while those required by the non-parametric method were more than 120. CONCLUSION: We describe a novel approach for estimating sample sizes for establishment of RI. A corresponding open-source code has been developed and is available for applications. The established method is suitable for most analytes, with evidence based on thyroid-related hormones indicating that different sample sizes are required to establish RIs using different methods for analytes with different variations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01751-1. |
format | Online Article Text |
id | pubmed-9585851 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-95858512022-10-22 An innovative approach based on real-world big data mining for calculating the sample size of the reference interval established using transformed parametric and non-parametric methods Ma, Chaochao Hou, Li’an Zou, Yutong Ma, Xiaoli Wang, Danchen Hu, Yingying Song, Ailing Cheng, Xinqi Qiu, Ling BMC Med Res Methodol Research BACKGROUND: Currently, the direct method is the main approach for establishment of reference interval (RI). However, only a handful of studies have described the effects of sample size on establishment of RI and estimation of sample size. We describe a novel approach for estimation of the sample size when establishing RIs using the transformed parametric and non-parametric methods. METHODS: A total of 3,697 healthy participants were enrolled in this study. We adopted a two-layer nested loop sample size estimation method to determine the effects of sample size on RI, using thyroid-related hormone as an example. The sample size was selected as the calculation result when the width of the confidence interval (CI) of the upper and lower limit of the RI were both stably < 0.2 times the width of RI. Then, we calculated the sample size for establishing RIs via transformed parametric and non-parametric methods for thyroid-related hormones. RESULTS: Sample sizes for thyroid stimulating hormone (TSH), as required by parametric and non-parametric methods to establish RIs were 239 and 850, respectively. Sample sizes required by the transformed parametric method for free triiodothyronine (FT3), free thyroxine (FT4), total triiodothyronine (TT3) and total thyroxine (TT4) were all less than 120, while those required by the non-parametric method were more than 120. CONCLUSION: We describe a novel approach for estimating sample sizes for establishment of RI. A corresponding open-source code has been developed and is available for applications. The established method is suitable for most analytes, with evidence based on thyroid-related hormones indicating that different sample sizes are required to establish RIs using different methods for analytes with different variations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01751-1. BioMed Central 2022-10-20 /pmc/articles/PMC9585851/ /pubmed/36266618 http://dx.doi.org/10.1186/s12874-022-01751-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Ma, Chaochao Hou, Li’an Zou, Yutong Ma, Xiaoli Wang, Danchen Hu, Yingying Song, Ailing Cheng, Xinqi Qiu, Ling An innovative approach based on real-world big data mining for calculating the sample size of the reference interval established using transformed parametric and non-parametric methods |
title | An innovative approach based on real-world big data mining for calculating the sample size of the reference interval established using transformed parametric and non-parametric methods |
title_full | An innovative approach based on real-world big data mining for calculating the sample size of the reference interval established using transformed parametric and non-parametric methods |
title_fullStr | An innovative approach based on real-world big data mining for calculating the sample size of the reference interval established using transformed parametric and non-parametric methods |
title_full_unstemmed | An innovative approach based on real-world big data mining for calculating the sample size of the reference interval established using transformed parametric and non-parametric methods |
title_short | An innovative approach based on real-world big data mining for calculating the sample size of the reference interval established using transformed parametric and non-parametric methods |
title_sort | innovative approach based on real-world big data mining for calculating the sample size of the reference interval established using transformed parametric and non-parametric methods |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585851/ https://www.ncbi.nlm.nih.gov/pubmed/36266618 http://dx.doi.org/10.1186/s12874-022-01751-1 |
work_keys_str_mv | AT machaochao aninnovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT houlian aninnovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT zouyutong aninnovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT maxiaoli aninnovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT wangdanchen aninnovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT huyingying aninnovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT songailing aninnovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT chengxinqi aninnovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT qiuling aninnovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT machaochao innovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT houlian innovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT zouyutong innovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT maxiaoli innovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT wangdanchen innovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT huyingying innovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT songailing innovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT chengxinqi innovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods AT qiuling innovativeapproachbasedonrealworldbigdataminingforcalculatingthesamplesizeofthereferenceintervalestablishedusingtransformedparametricandnonparametricmethods |