Statistical resolutions for large variabilities in hair mineral analysis.

Measuring biomaterials is usually subject to error. Measurement errors are classified into either random errors or biases. Random errors can be well controlled using appropriate statistical methods. But, biases due to unknown, unobserved, or temporary causes, may lead to biased conclusions. This stu...

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Main Authors: Tsuyoshi Nakamura, Tomomi Yamada, Koshi Kataoka, Koichiro Sera, Todd Saunders, Toshihiro Takatsuji, Toshio Makie, Yoshiaki Nose
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2018-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0208816
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spelling doaj-6d83607c6f994228894f5ff8b77f7e812021-03-03T21:00:41ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-011312e020881610.1371/journal.pone.0208816Statistical resolutions for large variabilities in hair mineral analysis.Tsuyoshi NakamuraTomomi YamadaKoshi KataokaKoichiro SeraTodd SaundersToshihiro TakatsujiToshio MakieYoshiaki NoseMeasuring biomaterials is usually subject to error. Measurement errors are classified into either random errors or biases. Random errors can be well controlled using appropriate statistical methods. But, biases due to unknown, unobserved, or temporary causes, may lead to biased conclusions. This study describes a verification method to examine whether measurement errors are random or not and to determine efficient statistical methods. A number of studies have dealt with associations between hair minerals and exposures such as health, dietary or environmental conditions. Most review papers, however, emphasize the necessity for validation of hair mineral measurements, since large variations can cause highly variable results. To address these issues, we answer the following questions: How can we ascertain the reliability of measurements?How can we assess and control the variability of measurements?How do we efficiently determine associations between hair minerals and exposures?How can we concisely present the reference values? Since hair minerals all have distinctive natures, it would be unproductive to examine each mineral individually to find significant and consistent answers that apply to all minerals. To surmount this difficulty, we used one simple model for all minerals to explore quantitative answers. Hair mineral measurements of six-year-old children were analyzed based on the statistical model. The analysis verified that most of the measurements were reliable, and their inter-individual variations followed two-parameter distributions. These results allow for sophisticated study designs and efficient statistical methods to examine the effects of various kinds of exposures on hair minerals.https://doi.org/10.1371/journal.pone.0208816
collection DOAJ
language English
format Article
sources DOAJ
author Tsuyoshi Nakamura
Tomomi Yamada
Koshi Kataoka
Koichiro Sera
Todd Saunders
Toshihiro Takatsuji
Toshio Makie
Yoshiaki Nose
spellingShingle Tsuyoshi Nakamura
Tomomi Yamada
Koshi Kataoka
Koichiro Sera
Todd Saunders
Toshihiro Takatsuji
Toshio Makie
Yoshiaki Nose
Statistical resolutions for large variabilities in hair mineral analysis.
PLoS ONE
author_facet Tsuyoshi Nakamura
Tomomi Yamada
Koshi Kataoka
Koichiro Sera
Todd Saunders
Toshihiro Takatsuji
Toshio Makie
Yoshiaki Nose
author_sort Tsuyoshi Nakamura
title Statistical resolutions for large variabilities in hair mineral analysis.
title_short Statistical resolutions for large variabilities in hair mineral analysis.
title_full Statistical resolutions for large variabilities in hair mineral analysis.
title_fullStr Statistical resolutions for large variabilities in hair mineral analysis.
title_full_unstemmed Statistical resolutions for large variabilities in hair mineral analysis.
title_sort statistical resolutions for large variabilities in hair mineral analysis.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2018-01-01
description Measuring biomaterials is usually subject to error. Measurement errors are classified into either random errors or biases. Random errors can be well controlled using appropriate statistical methods. But, biases due to unknown, unobserved, or temporary causes, may lead to biased conclusions. This study describes a verification method to examine whether measurement errors are random or not and to determine efficient statistical methods. A number of studies have dealt with associations between hair minerals and exposures such as health, dietary or environmental conditions. Most review papers, however, emphasize the necessity for validation of hair mineral measurements, since large variations can cause highly variable results. To address these issues, we answer the following questions: How can we ascertain the reliability of measurements?How can we assess and control the variability of measurements?How do we efficiently determine associations between hair minerals and exposures?How can we concisely present the reference values? Since hair minerals all have distinctive natures, it would be unproductive to examine each mineral individually to find significant and consistent answers that apply to all minerals. To surmount this difficulty, we used one simple model for all minerals to explore quantitative answers. Hair mineral measurements of six-year-old children were analyzed based on the statistical model. The analysis verified that most of the measurements were reliable, and their inter-individual variations followed two-parameter distributions. These results allow for sophisticated study designs and efficient statistical methods to examine the effects of various kinds of exposures on hair minerals.
url https://doi.org/10.1371/journal.pone.0208816
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