Hi there;
I want to investigate anti tumor effects of an antagonist in murine models and I need to know how to determine number of animals in each group such that the results would be statistically acceptable. I've read about different methods like "power analysis" and "resource equation" but I need some expertised suggestions! :)
Could anyone suggest any methods?
There are two methods for calculating sample size
1. one robust power analysis that depends on a host of variables such as type one error (Alpha), effect size, SD, power effect and direction of effect (one-tailed VS two-tailed analysis)
2. The second method which is more easy but less accurate; the calculation of the E Value; E = Total number of animals − Total number of groups- E between 10 and 20 is acceptable.
You can see the below link you can get some Idea..
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3826013/
https://www.ncbi.nlm.nih.gov/books/NBK43321/
Hi,
Sample size calculation in animals is hard. We usually use a calculation based on outcome (mucus velocity, for exemple). that's to say, "to demonstrate a difference of 1mm/second we calculate..." Sometimes a pilot study could help in this issue. Finally, I reccommend the ARRIVE gudelines for animal studies.
I hope I can help you.
Best wishes,
TalithaComaru
Dear Raheleh, I would suggest the power analysis in order to decide the sample size of your groups. You can find online calculators for the power analysis. For example, here you can insert the sample sizes and the standard deviation of your outcome in the population and you will obtain which effect size your study will be able to detect with a power of 80%:
http://www.sample-size.net/means-effect-size/
On the other hand, here you can insert the effect size that you desire and you will obtain the sample sizes required:
http://www.sample-size.net/sample-size-means/
I agree with Murugesan: we generally use 6 mice per group. This can give you a basci information. The size of each group to obtain a "significant" value depends on the difference you find, but if your ealise the difference is too small, I think you should ask if the study is worth conducting.
I am convinced that if we design animal studies properly we may fiand an agreement with anti-animal-experiments groups!
I agree with colleguaes who said 6 mice/rats per cage (group). I did my experiments with such groups and obtained good results for statistical analyses. This is the commonest number of experimental animals in pharmadodinamic screening and pharmacokinetic investigation.
I agree with All above mentioned answers.
Number of animals also depends on death rate during and after disease induction up to the last day of study.
Like evaluation of Anti cancer and find out treatment for toxicity of chemotherapy.
I also agree with colleagues that 6 animals/per group are the most suitable number, besides pharmacological or toxicological studies with possible high mortality of animals.
In order to answer this question/problem, several remarks have to be studied.
1. General remarks:
Research studies are usually carried out on sample of subjects rather than whole populations. The most challenging aspect of fieldwork is drawing a random sample from the target population to which the results of the study would be generalized. The key to a good sample is that it has to be typical of the population from which it is drawn. When the information from a sample is not typical of that in the population in a systematic way, we say that error has occurred. In actual practice, the task is so difficult that several types of errors, i.e. sampling error, non-sampling error, Response error, Processing error,…
In addition, the most important error is the Sampling error, which is statistically defined as the error caused by observing a sample instead of the whole population. The underlying principle that must be followed if we are to have any hope of making inferences from a sample to a population is that the sample be representative of that population. A key way of achieving this is through the use of “randomization”. There several types of random samples, Some of which are: Simple Random Sampling, Stratified Random Sampling, Double-stage Random Sampling... Moreover, the most important sample is the simple random sample which is a sample selected in such a way that every possible sample of the same size is equally likely to be chosen. In order to reduce the sampling error, the simple random sample technique and a large sample size have to be developed.
2. Specific remarks:
The following factors are highly affected the sample size and need to be identified:
- Population Size,
- Margin of Error,
· Confidence Level (level of significance) and
- Standard of Deviation.
Then, the sample size can be estimated by,
Necessary Sample Size = (z-score or t-value)2 * StdDev*(1-StdDev) / (margin of error)2 .
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