 |
 |
Jesse et al: There are many different ways of calculating sample size. A simple (and interactive way) of determining the required sample size for a study is to model this as a Binomial Distribution, since animals can only be positive or negative. By using one of the many web-based Binomial calculators available (I recommend http://vassarstats.net/binomialX.html), you can easily model the probability of getting zero positive results in a sample size of N animals, assuming a specific level of prevalence. Here is how this works for the calculator noted above: You need to specify three parameters: N (number of individuals you want to use for the total sample), K (the number of positive animals, which you set as zero), and P (the "true" prevalence rate, from 0-1.0). Using the calculator noted above, if you fill in these values for Jesse's example (30 amphibians sampled, prevalence = 0.1, zero found with infections), you will get the output shown on the attached page. The critical part of this is the part listed under "hypothesis testing" towards the bottom the output: P: 0 or fewer out of 30 One-Tail Two-Tail Method 1. exact binomial calculation 0.0423 -this tells you that the probability of sampling 30 animals at random where the actual prevalence is 0.1 and finding zero "hits" is 0.0423. The inverse of this is the confidence level (1-0.0423 = 95.7%). If the actual prevalence rate was 5%, you would need a sample of 60 animals. The nice thing about this approach is that it is (a) free and (b) can be used for any combination of values of estimated sample size, levels of prevalence, and desired confidence levels. However, keep in mind that, like any inference-based statistic, you need to be sure that you are sampling at random and that there are no false negatives. Based on Jesse's last email, I wonder how confident we can be of that? Rich Seigel Richard A. Seigel Professor Department of Biological Sciences Towson University Towson, MD 21252 410-704-3123 http://towson.academia.edu/RichardSeigel From: Global Ranavirus Consortium [mailto:[log in to unmask]] On Behalf Of Jesse Brunner Sent: Monday, June 18, 2012 1:12 PM To: [log in to unmask] Subject: Re: question from the netherlands Dear All, I was just looking through the OIE manual again, based on Greg's suggestion, and was struck by the lack of validated PCR, especially qPCR assays in it. From my experience (and I believe that of others, though I won't speak for them), quantitative real time PCR is much more (analytically) sensitive than standard PCR (e.g., with the MCP4/5 primers in Mao et al. 1997) and more sensitive even than virus isolation in cell culture. We are consistently detecting the equivalent of <1 plaque forming unit in asymptomatic animals from experiments and from the wild. Jaramillo et al. have a validated assay for ranavirus in fish that seems to support the assertion that qPCR is more sensitive: "The assay had 100-fold greater analytical sensitivity compared to virus isolation in Bluegill fry (BF-2) cells, detected all viruses in a panel of 20 ranaviruses from Australia, America, Europe and the United Kingdom, did not detect two disputed members of the genus Ranavirus (doctorfish virus and guppy virus 6) and did not detect a megalocytivirus or two cyprinid herpesviruses." (Note: I do understand that analytic sensitivity is not the same as diagnostic sensitivity, but if virus isolation is our gold standard then qPCR cannot, by definition, have greater diagnostic sensitivity!) So I guess my question is, if someone is interested in ensuring that their animals are ranavirus free, shouldn't we be pointing them to these more sensitive assays? What are your thoughts? Would it be worth "stressing out" some subset of the animals to see if animals carrying inapparent infections might recrudesce? The question of whether they can develop full blown, transmissible infections seems very relevant in these cases. I would also like to point out that the protocols in the OIE manual state, in section 6: "Statistically valid sampling practices need to be used but these cannot presently be defined for amphibians." I am not sure why amphibians would present a special case in epidemiology, but I was not at these meetings, so perhaps someone could clear up my confusion. Barring that, I might suggest using the standard formulas for calculating the sample sizes required to be, say, 95% confidence that the prevalence of infection is at or below some level (e.g., 10%) given a population of N (e.g., 10^6)... here n = 30. I'm sure there are other references for the formula, but this is the one I have: Cannon, R.M., and R.T. Roe. 1982. Livestock Disease Surveys: A Field Manual For Veterinarieans. Canberra: Australian Bureau of Animal Health I've attached a spreadsheet I created a while back to help me see the relationships between confidence-level, population sizes, the number of disease animals, and the sample size. It is rather busy, but some playing with the numbers in different colors should clear up which graph is associated with which equation. Or I'm happy to answer questions about it. In any case, if there is a better way of establishing statistically valid sample sizes, I would love to hear them. It would be great for the members of this list to be on the same page, more or less, about how best to sample. Best wishes, Jesse Jesse Brunner School of Biological Sciences Washington State University PO Box 644236 Pullman, WA 99164 USA [log in to unmask]<mailto:[log in to unmask]> 509-335-3702 http://www.oie.int/international-standard-setting/aquatic-manual/access-online/ Jaramillo D, Tweedie A, Becker JA, Hyatt A, Crameri S et al. (2012) A validated quantitative polymerase chain reaction assay for the detection of ranaviruses (Family Iridoviridae) in fish tissue and cell cultures, using EHNV as a model. Aquaculture