1887
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.

Statistical Considerations in Environmental Microbial Forensics

MyBook is a cheap paperback edition of the original book and will be sold at uniform, low price.
Buy this Microbiology Spectrum Article
Price Non-Member $15.00
  • Authors: Graham McBride1, Brent Gilpin2
  • Editors: Raúl J. Cano3, Gary A. Toranzos4
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: National Institute of Water & Atmospheric Research (NIWA), Hamilton, Hamilton, 3216 New Zealand; 2: Environmental Science Research (ESR), Christchurch 8540, New Zealand; 3: California Polytechnic State University, San Luis Obispo, CA; 4: University of Puerto Rico-Río Piedras, San Juan, Puerto Rico
  • Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.EMF-0005-2015
  • Received 01 October 2015 Accepted 10 October 2015 Published 12 August 2016
  • Graham McBride, Graham.McBride@niwa.co.nz
image of Statistical Considerations in Environmental Microbial Forensics
    Preview this microbiology spectrum article:
    Zoom in
    Zoomout

    Statistical Considerations in Environmental Microbial Forensics, Page 1 of 2

    | /docserver/preview/fulltext/microbiolspec/4/4/EMF-0005-2015-1.gif /docserver/preview/fulltext/microbiolspec/4/4/EMF-0005-2015-2.gif
  • Abstract:

    In environmental microbial forensics, as in other pursuits, statistical calculations are sometimes inappropriately applied, giving rise to the appearance of support for a particular conclusion or failing to support an innately obvious conclusion. This is a reflection of issues related to dealing with sample sizes, the methodologies involved, and the difficulty of communicating uncertainties. In this brief review, we attempt to illustrate ways to minimize such problems. In doing so, we consider one of the most common applications of environmental microbial forensics—the use of genotyping in food and water and disease investigations. We explore three important questions. (i) Do hypothesis tests’ values serve as adequate metrics of evidence? (ii) How can we quantify the value of the evidence? (iii) Can we turn a value-of-evidence metric into attribution probabilities? Our general conclusions are as follows. (i) values have the unfortunate property of regularly detecting trivial effects when sample sizes are large. (ii) Likelihood ratios, rather than any kind of probability, are the better strength-of-evidence metric, addressing the question “what do these data say?” (iii) Attribution probabilities, addressing the question “what should I believe?,” can be calculated using Bayesian methods, relying in part on likelihood ratios but also invoking prior beliefs which therefore can be quite subjective. In legal settings a Bayesian analysis may be required, but the choice and sensitivity of prior assumptions should be made clear.

  • Citation: McBride G, Gilpin B. 2016. Statistical Considerations in Environmental Microbial Forensics. Microbiol Spectrum 4(4):EMF-0005-2015. doi:10.1128/microbiolspec.EMF-0005-2015.

Key Concept Ranking

Campylobacter jejuni
0.61344165
Food and Water
0.61344165
Campylobacter jejuni
0.61344165
Escherichia coli
0.57936156
0.61344165

References

1. Aitken CGG, Stoney DA. 1991. The Use of Statistics in Forensic Science. Ellis Horwood, Chichester, England.
2. Balding DJ, Nichols RA. 1994. DNA profile match probability calculation: how to allow for population stratification, relatedness, database selection and single bands. Forensic Sci Int 64:125–140. [PubMed][CrossRef]
3. Curran JM, Buckleton JS, Triggs CM, Weir BS. 2002. Assessing uncertainty in DNA evidence caused by sampling effects. Sci Justice 42:29–37. [PubMed][CrossRef]
4. Aitken C, Taroni F. 2005. Statistics and the Evaluation of Evidence for Forensic Scientists, 2nd ed. Wiley, New York, NY.
5. Butler JM. 2005. Forensic DNA Typing: Biology, Technology, and Genetics of DNA Markers, 2nd ed. Elsevier Academic Press, Burlington, MA.
6. Wilson DJ, Gabriel E, Leatherbarrow AJH, Cheesbrough J, Gee S, Bolton E, Fox A, Fearnhead P, Hart CA, Diggle PJ. 2008. Tracing the source of campylobacteriosis. PLoS Genet 4:e1000203. [PubMed][CrossRef]
7. Taroni F, Bozza S, Biedermann A, Garbolino P, Aitken C. 2010. Data Analysis in Forensic Science: A Bayesian Decision Perspective. Wiley, New York, NY. [CrossRef]
8. Berkson J. 1942. Tests of significance considered as evidence. J Am Stat Assoc 37:325–335. [CrossRef]
9. Cohen J. 1994. The earth is round (p < .05). Am Psych 49(12):997–1003. [Full paper also contained in the Harlow et al. text (reference 10)] [CrossRef]
10. Harlow LL, Muliak SA, Steiger JH. 1997. What If There Were No Significance Tests? Lawrence Erlbaum, Mahwah, NJ.
11. Altman DG. 1991. Practical Statistics for Medical Research. Chapman and Hall, London, England.
12. Altman DG, Deeks JJ, Sackett DL. 1998. Odds ratios should be avoided when events are common. BMJ 317:1318. [PubMed][CrossRef]
13. Deeks JJ, Higgins JPT. 2010. Statistical Algorithms. Review Manager 5. http://ims.cochrane.org/revman/documentation/Statistical-methods-in-RevMan-5.pdf.
14. Pagano M, Gauvreau K. 2000. Principles of Biostatistics, 2nd ed. Brooks/Cole, Belmont, CA.
15. Parshall MB. 2013. Unpacking the 2 × 2 table. Heart Lung 42:221–226. [PubMed][CrossRef]
16. Woolf B. 1955. On estimating the relation between blood group and disease. Ann Hum Genet 19:251–253. [PubMed][CrossRef]
17. Nuzzo R. 2014. Statistical errors: P-values, the ‘gold standard’ of statistical validity, are not as reliable as many scientists assume. Nature 506:150–152. [PubMed][CrossRef]
18. McBride G, Cole RG, Westbrooke I, Jowett I. 2014. Assessing environmentally significant effects: a better strength-of-evidence than a single P value? Environ Monit Assess 186:2729–2740. [PubMed][CrossRef]
19. Leek JT, Peng RD. 2015. Statistics: P values are just the tip of the iceberg. Nature 520:612. [PubMed][CrossRef]
20. Nuzzo R. 2015. Scientists perturbed by loss of stat tools to sift research fudge from fact. Sci Am (April):16.
21. Fisher RA. 1925. Statistical Methods for Research Workers. Oliver and Boyd, Edinburgh, Scotland. [PubMed]
22. Lee PM. 1997. Bayesian Statistics: An Introduction. Arnold, London, England.
23. Royall R. 1997. Statistical Evidence: A Likelihood Paradigm. Chapman & Hall/CRC, Boca Raton, FL.
24. Kass RE, Raftery AE. 1995. Bayes factors. J Am Stat Assoc 90:773–795. [CrossRef]
25. Cho SH, Kim J, Oh KH, Hu JK, Seo J, Oh SS, Hur MJ, Choi YH, Youn SK, Chung GT, Choe YJ. 2014. Outbreak of enterotoxigenic Escherichia coli O169 enteritis in schoolchildren associated with consumption of kimchi, Republic of Korea, 2012. Epidemiol Infect 142:616–623. [PubMed][CrossRef]
26. Vogt RL, Dippold L. 2005. Escherichia coli O157:H7 outbreak associated with consumption of ground beef, June-July 2002. Public Health Rep 120:174–178. [PubMed]
27. Besser RE, Lett SM, Weber JT, Doyle MP, Barrett TJ, Wells JG, Griffin PM. 1993. An outbreak of diarrhea and hemolytic uremic syndrome from Escherichia coli O157:H7 in fresh-pressed apple cider. JAMA 269:2217–2220. [PubMed][CrossRef]
28. Campbell JV, Mohle-Boetani J, Reporter R, Abbott S, Farrar J, Brandl M, Mandrell R, Werner SB. 2001. An outbreak of Salmonella serotype Thompson associated with fresh cilantro. J Infect Dis 183:984–987. [PubMed][CrossRef]
29. Centers for Disease Control and Prevention (CDC). 2013. Multistate outbreak of salmonella chester infections associated with frozen meals—18 states, 2010. MMWR Morb Mortal Wkly Rep 62:979–982. [PubMed]
30. Gardner TJ, Fitzgerald C, Xavier C, Klein R, Pruckler J, Stroika S, McLaughlin JB. 2011. Outbreak of campylobacteriosis associated with consumption of raw peas. Clin Infect Dis 53:26–32. [PubMed][CrossRef]
31. Zeigler M, Claar C, Rice D, Davis J, Frazier T, Turner A, Kelley C, Capps J, Kent A, Hubbard V, Ritenour C, Tuscano C, Qiu-Shultz Z, Leaumont CF, Centers for Disease Control and Prevention. 2014. Outbreak of campylobacteriosis associated with a long-distance obstacle adventure race—Nevada, October 2012. MMWR Morb Mortal Wkly Rep 63:375–378. [PubMed]
32. McGrayne SB. 2011. The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy. Yale University Press, New Haven, CT.
33. Gelman A. 2015. Working through some issues. Significance 12:33–35. [CrossRef]
34. Abramowitz M, Stegun IA. 1972. Handbook of Mathematical Functions. Dover, Mineola, NY.
microbiolspec.EMF-0005-2015.citations
cm/4/4
content/journal/microbiolspec/10.1128/microbiolspec.EMF-0005-2015
Loading

Citations loading...

Loading

Article metrics loading...

/content/journal/microbiolspec/10.1128/microbiolspec.EMF-0005-2015
2016-08-12
2017-09-21

Abstract:

In environmental microbial forensics, as in other pursuits, statistical calculations are sometimes inappropriately applied, giving rise to the appearance of support for a particular conclusion or failing to support an innately obvious conclusion. This is a reflection of issues related to dealing with sample sizes, the methodologies involved, and the difficulty of communicating uncertainties. In this brief review, we attempt to illustrate ways to minimize such problems. In doing so, we consider one of the most common applications of environmental microbial forensics—the use of genotyping in food and water and disease investigations. We explore three important questions. (i) Do hypothesis tests’ values serve as adequate metrics of evidence? (ii) How can we quantify the value of the evidence? (iii) Can we turn a value-of-evidence metric into attribution probabilities? Our general conclusions are as follows. (i) values have the unfortunate property of regularly detecting trivial effects when sample sizes are large. (ii) Likelihood ratios, rather than any kind of probability, are the better strength-of-evidence metric, addressing the question “what do these data say?” (iii) Attribution probabilities, addressing the question “what should I believe?,” can be calculated using Bayesian methods, relying in part on likelihood ratios but also invoking prior beliefs which therefore can be quite subjective. In legal settings a Bayesian analysis may be required, but the choice and sensitivity of prior assumptions should be made clear.

Highlighted Text: Show | Hide
Loading full text...

Full text loading...

Figures

Image of FIGURE 1
FIGURE 1

Power curves for a one-sample two-sided student’s test at the 5% significance level, where the population variance (σ) is unknown. Rotated numbers are the sample size ().

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.EMF-0005-2015
Permissions and Reprints Request Permissions
Download as Powerpoint
Image of FIGURE 2
FIGURE 2

The value for a test positing that the true odds ratio, 1, is the sum of the areas in the left and right tails of the unit normal probability density function that are cut off by and by − (nil tests are “two-tailed”). For of 1.8 we read standard distribution tables to obtain = 2 × 0.0359 = 0.0718. (The total area under this density function is 1.) Note that larger values of give rise to smaller values of .

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.EMF-0005-2015
Permissions and Reprints Request Permissions
Download as Powerpoint

Tables

Generic image for table
TABLE 1

Example 2 × 2 contingency table

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.EMF-0005-2015
Generic image for table
TABLE 2

Results of nil hypothesis tests and likelihood ratio for varying sample size

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.EMF-0005-2015
Generic image for table
TABLE 3

O169 illnesses among Korean schoolchildren consuming kimchi (K)

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.EMF-0005-2015
Generic image for table
TABLE 4

Contraction of bacterial illnesses from various exposures

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.EMF-0005-2015
Generic image for table
TABLE 5

Campylobacteriosis cases among participants in a long-distance race

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.EMF-0005-2015

Supplemental Material

No supplementary material available for this content.

This is a required field
Please enter a valid email address
Please check the format of the address you have entered.
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error