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...

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Publicado en:BMC Medical Research Methodology Vol. 22; no. 1; pp. 1 - 10
Autores principales: Ma, Chaochao, Hou, Li'an, Zou, Yutong, Ma, Xiaoli, Wang, Danchen, Hu, Yingying, Song, Ailing, Cheng, Xinqi, Qiu, Ling
Formato: research tables/charts Journal Article
Publicado: BioMed Central 10/20/2022
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 10/20/2022
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      pub: BioMed Central
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        10.1186/s12874-022-01751-1
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        159792147
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        atl: 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.
      aug:
        au:
          Ma, Chaochao
          Hou, Li'an
          Zou, Yutong
          Ma, Xiaoli
          Wang, Danchen
          Hu, Yingying
          Song, Ailing
          Cheng, Xinqi
          Qiu, Ling
        affil: Department of Laboratory Medicine, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Science, 100730, Beijing, PR China
      sug:
        subj:
          Thyroxine
          Triiodothyronine
          Thyrotropin
          Data Mining
          Reference Values
          Sample Size
          Funding Source
          Human
      ab: 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.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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