Statistical Methods for Censored Environmental Data Using Minitab and R, Second Edition introduces and explains methods for analyzing and interpreting censored data in the environmental sciences. Adapting survival analysis techniques from other fields, the book translates well-established methods from other disciplines into new solutions for environmental studies.
This new edition applies methods of survival analysis, including methods for interval-censored data to the interpretation of low-level contaminants in environmental sciences and occupational health. Now incorporating the freely available R software as well as Minitab into the discussed analyses, the book features newly developed and updated material including:
- A new chapter on multivariate methods for censored data
- Use of interval-censored methods for treating true nondetects as lower than and separate from values between the detection and quantitation limits ("remarked data")
- A section on summing data with nondetects
- A newly written introduction that discusses invasive data, showing why substitution methods fail
- Expanded coverage of graphical methods for censored data
The author writes in a style that focuses on applications rather than derivations, with chapters organized by key objectives such as computing intervals, comparing groups, and correlation. Examples accompany each procedure, utilizing real-world data that can be analyzed using the Minitabr and R software macros available on the book's related website, and extensive references direct readers to authoritative literature from the environmental sciences.
"Statistics for Censored Environmental Data Using Minitab and R", Second Edition is an excellent book for courses on environmental statistics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for environmental professionals, biologists, and ecologists who focus on the water sciences, air quality, and soil science.
Review of NADA Book (2012) by Helsel
by ADI-NV Inc in the United States (18-10-2013)
The book does not provide any thing new which has not been widely available and published. A major part of the book describes methods and equations which are available in several free downloadable government guidance documents. The book has been written to address statistical issues of environmental projects, especially for data sets with non-detects. The book (Chapters 2, 6, 7) promotes Kaplan-Meier (KM) estimation method; however the book describes the use of KM method in terms of survival functions requiring flipping of data and re-flipping of estimates, which is unnecessary and confusing. It is tedious to use this method when multiple variables need to be processed. Surprisingly, the book also suggests that standard deviation is not of interest (page 76). In many environmental studies, decisions are made based upon upper limits such as upper prediction limits and upper tolerance limits which need a GOOD estimate of standard deviation. Instead of computing KM standard deviation directly, the book suggests the use of an ad hoc "back door" estimate. The differences between upper limits based upon direct and back door estimates of standard deviation can be quite significant especially when they are computed using log-transformed data. These issues have been discussed in detail in ProUCL guidance documents.
The book (Chapter 7) suggests the use of percentile bootstrap method on KM estimates to compute upper confidence limit of mean. However, it is well known that percentile bootstrap method does not perform well (in terms of providing coverage to the mean) for skewed data set. There are several better performing methods available in the environmental literature which account for skewness. The book acknowledges the presence of skewness (e.g., page 76) but provides no guidance on how to address skewness in the computation of statistics used to make environmental decisions.
Most of the conclusions and suggestions described in the book are made based upon a handful of TOY data sets. Specifically, conclusions and suggestions have been made without taking sample size, data distribution, and data skewness into consideration. For example, the main decision statistics (e.g., UCLs, UPLs, UTLs) computed using the methods (e.g., percentile bootstrap, statistics using KM estimates and t-critical values) described and suggested in the book will fail to provide the desired coverage to the environmental parameters of interest (e.g., mean, upper percentile) of moderately skewed to highly skewed populations (common occurrence in environmental applications); and conclusions derived based upon those statistics may lead to incorrect conclusions which may not be cost-effective or protective of human health and the environment. The author fails to acknowledge the existence and use of the better performing methods for skewed data sets to compute UPLs, UTLs, and UCLs.
Praise for the First Edition
" [...] an excellent addition to an upper-level undergraduate course on environmental statistics, and [...] a 'must-have' desk reference for environmental practitioners dealing with censored datasets."
– Vadose Zone Journal
1 Things People Do with Censored Data that Are Just Wrong 1
2 Three Approaches for Censored Data 12
3 Reporting Limits 22
4 Reporting, Storing, and Using Censored Data 37
5 Plotting Censored Data 44
6 Computing Summary Statistics and Totals 62
7 Computing Interval Estimates 99
8 What Can be Done When All Data Are Below the Reporting Limit? 142
9 Comparing Two Groups 153
10 Comparing Three or More Groups 194
11 Correlation 218
12 Regression and Trends 236
13 Multivariate Methods for Censored Data 268
14 The NADA for R Software 297
Appendix: Datasets 303
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Dennis R. Helsel, PhD, is owner and Principal Scientist of Practical Stats, where he designs and conducts training courses in environmental statistics for scientists. He has over thirty years of experience working with the U.S. Geological Survey and is the author of numerous published articles on nondetect data and statistical methods in the environmental sciences. Dr. Helsel is the recipient of the Distinguished Service Award from the U.S. Department of the Interior (2007) as well as the Distinguished Achievement Award from the American Statistical Association (2003).