Aren’t these values sensitive to the
population size of the sampled population? Since the draw can hardly be random,
I would imagine sampling effort would need to be larger in large populations.
From: Global Ranavirus Consortium
[mailto:[log in to unmask]] On Behalf Of Seigel,
Richard
Sent: 18 June 2012 19:00
To: [log in to unmask]
Subject: Re: question from the netherlands
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
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
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.
School
of Biological Sciences
Washington
State University
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
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