How To Calculate Sample Size In Animal Studies? Pdf
J Pharmacol Pharmacother. 2013 October-Dec; 4(4): 303–306.
How to calculate sample size in beast studies?
Jaykaran Charan
Department of Pharmacology, GMERS Medical Higher, Patan, Regime Medical College, Surat, Gujarat, India
N. D. Kantharia
Department of Pharmacology, GMERS Medical Higher, Patan, Authorities Medical Higher, Surat, Gujarat, Bharat
Abstract
Calculation of sample size is 1 of the important component of design of whatever enquiry including beast studies. If a researcher select less number of animals information technology may lead to missing of any significant divergence even if information technology exist in population and if more number of animals selected then it may lead to unnecessary wastage of resource and may pb to ethical problems. In this article, on the basis of review of literature done past u.s. we suggested few methods of sample size calculations for animate being studies.
Keywords: Alpha error, animal studies, power, sample size
How many animals I should use for my study? This is 1 of the most confusing questions faced by a researcher. As well pocket-sized sample size can miss the real consequence in experiment and also big sample size volition lead to unnecessary wasting of the resources and animals.[1] Effect of sample size has been highlighted adequately for the clinical trials and clinical studies, merely not explored much in the case of fauna studies in published literature. Information technology is very important to teach young researchers and post-graduate students regarding importance and methods of sample size adding. To analyze this issue of sample size in animal studies, nosotros decided to search various manufactures available regarding the sample size in animal studies. We did PubMed search by using diverse MeSH terms such as "sample size," "sample size calculations," "beast studies" etc., and their combinations. Nosotros take as well searched various articles through Google and Google Scholar. We accept besides searched various websites related to animal research (http://www. 3rs-reduction.co.uk/html/6__power_and_sample_size.html, http://www.acuc.berkeley.edu/, http://www.bu.edu/orccommittees/iacuc/policies-and-guidelines/sample-size-calculations/, http://world wide web.ucd.ie/researchethics/etc.). First writer read all available literature and an understanding nearly the concept is made in consultation with the second author. Hither, we are explaining briefly near the method of sample size calculations in beast studies based on review of the literature carried out by us.
Basically, there are two methods of sample size adding in beast studies. The most favored and most scientific method is calculation of sample size by ability analysis.[two] Every effort should be carried out to calculate sample size by this method. This method is similar to the method used for calculation of sample size for clinical trials and clinical studies. Unproblematic calculation can be carried out manually with the help of some formula [Appendix i], but for circuitous calculations statistical software can be used or help from a statistician tin be sought. To calculate the sample size by power assay a researcher must have knowledge and information about these concepts:
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Effect size: This is the departure betwixt the mean of two groups (quantitative data) or proportions of events in two groups (qualitative data). A researcher should decide before the start of the study that how much minimum difference betwixt two groups can exist considered every bit clinically meaning. The idea nigh clinically significant difference between the groups should exist taken preferably from previously published studies[2,iii,four,5]
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Standard divergence: Standard deviation measures variability inside the sample. Information almost standard deviation is needed only in the instance of quantitative variables. Information well-nigh the standard deviation of a particular variable can be taken from previously published studies. If no such study is available then author should conduct a pilot study first and standard divergence can be calculated from the airplane pilot study[2,3,four,five]
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Blazon 1 error: This is measured past significance level, which is usually stock-still at the level of 5% (P = 0.05). This is an arbitrary value and tin can be decreased or increased according to the research question[2,3,4,five]
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Power: Power of a study is probability of finding an effect, which the study is aimed to detect. This may be kept betwixt 80% to even 99% depending on research question, but unremarkably, it is kept at 80%[2,3,iv,5]
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Direction of effect (one tailed or ii tailed): When a researcher wants to explore the effect of some intervention, the actual issue observed in sample may exist in same management as researcher thought or it may exist just opposite to that. If researcher feels that effect may be in both directions then he should utilise two tailed test and if he has stiff reason to believe for the event to lie in one direction then he can employ 1 tailed test. In brute research, 2 tailed tests are usually used[2]
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Statistical tests: For sample size adding, information technology is important to have an thought about statistical test, which is to exist practical on data. For simple statistical tests such as Students t-test or Chi-foursquare exam, manual calculation based on formula can exist carried out [Appendix], only for complex tests like ANOVA or not-parametric tests assistance of statistician or employ of software is needed[2,iv]
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Expected attrition or death of animals: Final sample size should be adjusted for expected compunction. Suppose a researcher is expecting 10% attrition then the sample size calculated by formula or software should be divided past 0.9 to go actual sample size. Suppose sample size calculated by software is 10 animals per grouping and researcher is expecting 10% attrition then his final sample size volition be eleven animals per group (10/0.9 = 11.11). Similarly, for xx% attrition sample size should be divided by 0.8.[5] This can be explained in the class of structured formula i.due east.,
Corrected sample size = Sample size/ (ane− [% attrition/100])
Nosotros suggest employ of freely downloadable software G Ability (Faul, Erdfelder, Lang and Buchner, 2007) for sample size calculation. This software is equally proficient for sample size adding for clinical trials also. This software can be used for simple likewise every bit complex sample size calculations.[6] G Power can calculate sample size based on pre-designed effect size at small, medium, and big divergence betwixt the groups based on Cohen'south principles.[7] Information about other freely available software and calculators for sample size adding is given in Appendix 2. More than circuitous sample size will need more sophisticated software such as "nQuery counselor" or "MINITAB."
Second method of adding is a crude method based on law of diminishing return. This method is called "resource equation" method.[2,viii,9] This method is used when it is not possible to assume about consequence size, to get an idea about standard deviation as no previous findings are available or when multiple endpoints are measured or complex statistical procedure is used for assay. This method can likewise be used in some exploratory studies where testing of hypothesis is non the primary aim, merely researcher is interested only in finding any level of difference between groups.
According to this method a value "Due east" is measured, which is nothing only the degree of freedom of analysis of variance (ANOVA). The value of E should lie between 10 and 20. If Eastward is less than 10 then adding more animals will increment the gamble of getting more significant result, but if information technology is more twenty and then adding more animals volition not increase the take a chance of getting pregnant results. Though, this method is based on ANOVA, it is applicable to all animal experiments. Whatsoever sample size, which keeps Eastward between ten and 20 should exist considered as an acceptable. E tin be measured by following formula:
E = Total number of animals − Total number of groups
Suppose a researcher wants to see the issue of a drug and he fabricated five groups (i grouping as control and iv groups of different doses of that drug) with 10 rats each. In this case E will exist
E = (10 × 5) − 5
Due east = fifty − v = 45, which is more than 20 hence sample size in this experiment is more than than necessary. However, if sample size is five per grouping so E will be 20, which is the adequate limit and hence tin be considered as adequate sample size.
This method is easy, merely information technology cannot be considered as robust every bit power analysis method.
We want to suggest researchers to include a argument most method of calculation of sample size and justification of sample size in the manuscript they desire to publish. Animals in research: Reporting in vivo experiments guideline recommends inclusion of a argument mentioning justification of the sample size used in research and detail of method of calculation of sample size.[ten] All components of sample size adding such as effect size, type 1 and type 2 fault, one tailed/2 tailed test, standard deviation etc., should be reported in manuscript sent for publication the manner it is suggested for the clinical research.[11] Shortage of resources (budget, manpower), fourth dimension constrain etc., cannot be considered as valid justification regarding decision of sample size. Many researchers consider six animals per grouping as adequate sample size, merely later reviewing available literature on this issue nosotros came to a determination that this notion of vi animals per grouping has picayune scientific and statistical ground. This is a brief description and readers are requested to read more resources bachelor for better understanding of diverse concepts related to the sample size calculation in animal studies.
ACKNOWLEDGMENT
We desire to acknowledge unknown reviewer for constructive comments and guidance for improvisation of this manuscript.
Appendix i
Appendix 2
Footnotes
Source of Support: Nil
Conflict of Interest: None declared.
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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3826013/
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