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Mathematical interpretation of targeted volatilome data
Citation:
Pleil, J. AND P. Egeghy. Mathematical interpretation of targeted volatilome data. Chapter 37, Breathborne Biomarkers and the Human Volatilome. Elsevier Science Inc., Burlington, MA, , 615-631, (2020).
Impact/Purpose:
Chapter describes modern statistical methods for interpreting longitudinal and case-control studies for environmental and medical targeted data from human breath measurements
Description:
Regardless of the purpose, design, sampling methods, and analysis procedure implemented for a breath study, the ultimate goal is to answer questions about human exposures, health state, or biochemistry. This requires a thorough understanding of the collected data and meta-data including the distributions, confidence in identification, analytical errors, detection limits, human variability, and the ultimate context within which data are considered. This chapter discusses how to prepare a statistically defensible interpretation of breath measurement data. Basically, we discuss mathematical description of distributions, lognormality, comparisons, repeat measures, outliers and data sorting. We do not discuss more esoteric data issues involving random & fixed effects, multi-variate modeling and heatmapping; these were covered in chapter 1 of the previous edition of this book (Amann and Smith, 2013).