Abstract
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.
Highlights
Since measurement errors and interindividual distributions are the major sources of hair mineral measurement variability, it is necessary to fully understand these variabilities in order to perform valid and efficient statistical analyses of hair mineral measurements, clarifying the nature of measurement errors and inter-individual distribution for over 30 different hair minerals is not straightforward
We described a verification method to examine the validity of measurements
The method is applicable to Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES), or any other methods
Summary
Statistical model for hair mineral analysis interpretation with hair mineral analysis has been quite minimal with respect to extent of such endeavors” They attribute this limited success to large variabilities in hair mineral measurements and are more concerned with the biochemical aspects of hair mineral analysis to reduce and regulate the variations. Nonparametric methods are not very efficient for detecting the effects of exposures such as therapeutic effects, dietary intakes or environmental changes on study subjects. This disadvantage seems to be one of the main causes that led Kempson et al [1] to remark about minimal success
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