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Chapter 6 : Statistical Issues in Microbial Source Identification

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Abstract:

The focus of this chapter is almost exclusively on statistical issues and procedures commonly employed in cultivation-dependent approaches to microbial source tracking (MST), and the material in the chapter is also relevant more broadly. A section presents a critical review of statistical techniques for discriminant analysis in microbial source tracking. In the context of microbial source tracking, multidimensional scaling (MDS) plots are based on a matrix of numerical interisolate dissimilarity measures. Statistical methods for predictive discriminant analysis are commonly referred to as classification rules. This section describes the general process of constructing and evaluating classification rules in the context of microbial source tracking. The three most common types of similarity-based rules in the source-tracking literature are the maximum-similarity, average-similarity, and k-nearest-neighbor rules. Most of the techniques and tools for discriminant analysis discussed in the chapter require software for implementation. It is likely that SAS is used more often than other packages because of its wide availability and flexibility. The chapter reviews current statistical practice in microbial source tracking with particular attention to evaluation of method performance, sampling considerations, and discriminant analysis. Lastly, methods for addressing exploratory, confirmatory, and predictive discriminant analysis are presented. Recent ad hoc procedures to evaluate and improve the reliability of source identifications are also discussed. However, the development of valid measures of reliability and confidence in source identifications is an important problem requiring further statistical research.

Citation: Wilbur J, Whitlock J. 2007. Statistical Issues in Microbial Source Identification, p 181-210. In Santo Domingo J, Sadowsky M, Doyle M (ed), Microbial Source Tracking. ASM Press, Washington, DC. doi: 10.1128/9781555815769.ch6

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Citation: Wilbur J, Whitlock J. 2007. Statistical Issues in Microbial Source Identification, p 181-210. In Santo Domingo J, Sadowsky M, Doyle M (ed), Microbial Source Tracking. ASM Press, Washington, DC. doi: 10.1128/9781555815769.ch6
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Citation: Wilbur J, Whitlock J. 2007. Statistical Issues in Microbial Source Identification, p 181-210. In Santo Domingo J, Sadowsky M, Doyle M (ed), Microbial Source Tracking. ASM Press, Washington, DC. doi: 10.1128/9781555815769.ch6
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Citation: Wilbur J, Whitlock J. 2007. Statistical Issues in Microbial Source Identification, p 181-210. In Santo Domingo J, Sadowsky M, Doyle M (ed), Microbial Source Tracking. ASM Press, Washington, DC. doi: 10.1128/9781555815769.ch6
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Citation: Wilbur J, Whitlock J. 2007. Statistical Issues in Microbial Source Identification, p 181-210. In Santo Domingo J, Sadowsky M, Doyle M (ed), Microbial Source Tracking. ASM Press, Washington, DC. doi: 10.1128/9781555815769.ch6
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Citation: Wilbur J, Whitlock J. 2007. Statistical Issues in Microbial Source Identification, p 181-210. In Santo Domingo J, Sadowsky M, Doyle M (ed), Microbial Source Tracking. ASM Press, Washington, DC. doi: 10.1128/9781555815769.ch6
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Citation: Wilbur J, Whitlock J. 2007. Statistical Issues in Microbial Source Identification, p 181-210. In Santo Domingo J, Sadowsky M, Doyle M (ed), Microbial Source Tracking. ASM Press, Washington, DC. doi: 10.1128/9781555815769.ch6
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Citation: Wilbur J, Whitlock J. 2007. Statistical Issues in Microbial Source Identification, p 181-210. In Santo Domingo J, Sadowsky M, Doyle M (ed), Microbial Source Tracking. ASM Press, Washington, DC. doi: 10.1128/9781555815769.ch6
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Citation: Wilbur J, Whitlock J. 2007. Statistical Issues in Microbial Source Identification, p 181-210. In Santo Domingo J, Sadowsky M, Doyle M (ed), Microbial Source Tracking. ASM Press, Washington, DC. doi: 10.1128/9781555815769.ch6
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FLDA and MDS plots of the data from the three source categories for comparison.

Citation: Wilbur J, Whitlock J. 2007. Statistical Issues in Microbial Source Identification, p 181-210. In Santo Domingo J, Sadowsky M, Doyle M (ed), Microbial Source Tracking. ASM Press, Washington, DC. doi: 10.1128/9781555815769.ch6
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Tables

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Table 1

Four possible outcomes of source identification

Citation: Wilbur J, Whitlock J. 2007. Statistical Issues in Microbial Source Identification, p 181-210. In Santo Domingo J, Sadowsky M, Doyle M (ed), Microbial Source Tracking. ASM Press, Washington, DC. doi: 10.1128/9781555815769.ch6
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Table 2

Notation for cross-classification for four source categories

Citation: Wilbur J, Whitlock J. 2007. Statistical Issues in Microbial Source Identification, p 181-210. In Santo Domingo J, Sadowsky M, Doyle M (ed), Microbial Source Tracking. ASM Press, Washington, DC. doi: 10.1128/9781555815769.ch6
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Table 3

Results of cross-classification for Whitlock data

Citation: Wilbur J, Whitlock J. 2007. Statistical Issues in Microbial Source Identification, p 181-210. In Santo Domingo J, Sadowsky M, Doyle M (ed), Microbial Source Tracking. ASM Press, Washington, DC. doi: 10.1128/9781555815769.ch6
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Table 4

Estimation of specificity and sensitivity based on data in Whitlock et al. ( )

Citation: Wilbur J, Whitlock J. 2007. Statistical Issues in Microbial Source Identification, p 181-210. In Santo Domingo J, Sadowsky M, Doyle M (ed), Microbial Source Tracking. ASM Press, Washington, DC. doi: 10.1128/9781555815769.ch6
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Table 5

Common notation for similarity coefficients between binary profiles

Citation: Wilbur J, Whitlock J. 2007. Statistical Issues in Microbial Source Identification, p 181-210. In Santo Domingo J, Sadowsky M, Doyle M (ed), Microbial Source Tracking. ASM Press, Washington, DC. doi: 10.1128/9781555815769.ch6

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