Laboratory Animals最新文献

Isoflurane anesthesia prior to carbon dioxide euthanasia is recognized as a refinement by many guidelines. Facilities lacking access to a vaporizer can use the "drop" method, whereby liquid anesthetic is introduced into an induction chamber. Knowing the least aversive concentration of isoflurane is critical. Previous work has demonstrated that isoflurane administered with the drop method at a concentration of 5% is aversive to mice. Other work has shown that lower concentrations (1.7% to 3.7%) of isoflurane can be used to anesthetize mice with the drop method, but aversion to these concentrations has not been tested. We assessed aversion to these lower isoflurane concentrations administered with the drop method, using a conditioned place aversion (CPA) paradigm. Female C57BL/6J (OT-1) mice (n = 28) were randomly allocated to one of three isoflurane concentrations: 1.7%, 2.7%, and 3.7%. Mice were acclimated to a light-dark apparatus. Prior to and following dark (+ isoflurane) and light chamber conditioning sessions, mice underwent an initial and final preference assessment; the change in the duration spent within the dark chamber between the initial and final preference tests was used to calculate a CPA score. Aversion increased with increasing isoflurane concentration: from 1.7% to 2.7% to 3.7% isoflurane, mean ± SE CPA score decreased from 19.6 ± 20.1 s to -25.6 ± 23.2 s, to -116.9 ± 30.6 s (F_1,54 = 15.4, p < 0.001). Our results suggest that, when using the drop method to administer isoflurane, concentrations between 1.7% and 2.7% can be used to minimize female mouse aversion to induction.

Cage effects: some researchers worry about them, some don't, and some aren't even aware of them. When statistical analyses do not account for cage effects, there is real reason to worry. Regardless of researchers' worries or lack thereof, all researchers should be aware of how cage effects can affect the results. The "how" depends, in part, on the experimental design. Here, I (a) define cage effects; (b) illustrate a completely randomized design (CRD) often used in animal experiments; (c) explain how statistical significance is artificially inflated when cage effects are ignored and (d) give guidance on proper analyses and on how to increase statistical power in CRDs.

Blinding and randomisation are important methods for increasing the robustness of pre-clinical studies, as incomplete or improper implementation thereof is recognised as a source of bias. Randomisation ensures that any known and unknown covariates introducing bias are randomly distributed over the experimental groups. Thereby, differences between the experimental groups that might otherwise have contributed to false positive or -negative results are diminished. Methods for randomisation range from simple randomisation (e.g. rolling a dice) to advanced randomisation strategies involving the use of specialised software. Blinding on the other hand ensures that researchers are unaware of group allocation during the preparation, execution and acquisition and/or the analysis of the data. This minimises the risk of unintentional influences resulting in bias. Methods for blinding require strong protocols and a team approach. In this review, we outline methods for randomisation and blinding and give practical tips on how to implement them, with a focus on animal studies.

Statistically based experimental designs have been available for over a century. However, many preclinical researchers are completely unaware of these methods, and the success of experiments is usually equated only with 'p < 0.05'. By contrast, a well-thought-out experimental design strategy provides data with evidentiary and scientific value. A value-based strategy requires implementation of statistical design principles coupled with basic project management techniques. This article outlines the three phases of a value-based design strategy: proper framing of the research question, statistically based operationalisation through careful selection and structuring of appropriate inputs, and incorporation of methods that minimise bias and process variation. Appropriate study design increases study validity and the evidentiary strength of the results, reduces animal numbers, and reduces waste from noninformative experiments. Statistically based experimental design is thus a key component of the 'Reduction' pillar of the 3R (Replacement, Reduction, Refinement) principles for ethical animal research.

Variability is inherent in most biological systems due to differences among members of the population. Two types of variation are commonly observed in studies: differences among samples and the "error" in estimating a population parameter (e.g. mean) from a sample. While these concepts are fundamentally very different, the associated variation is often expressed using similar notation-an interval that represents a range of values with a lower and upper bound. In this article we discuss how common intervals are used (and misused).

Absence of statistical significance (i.e., p > 0.05) in the results of a frequentist test comparing two samples is often used as evidence of absence of difference, or absence of effect of a treatment, on the measured variable. Such conclusions are often wrong because absence of significance may merely result from a sample size that is too small to reveal an effect. To conclude that there is no meaningful effect of a treatment/condition, it is necessary to use an appropriate statistical approach. For frequentist statistics, a simple tool for this goal is the 'two one-sided t-test,' a form of equivalence test that relies on the a priori definition of a minimal difference considered to be relevant. In other words, the smallest effect size of interest should be established in advance. We present the principles of this test and give examples where it allows correct interpretation of the results of a classical t-test assuming absence of difference. Equivalence tests are also very useful in probing whether certain significant results are also biologically meaningful, because when comparing large samples it is possible to find significant results in both an equivalence test and in a two-sample t-test, assuming no difference as the null hypothesis.

The normality assumption postulates that empirical data derives from a normal (Gaussian) population. It is a pillar of inferential statistics that enables the theorization of probability functions and the computation of p-values thereof. The breach of this assumption may not impose a formal mathematical constraint on the computation of inferential outputs (e.g., p-values) but may make them inoperable and possibly lead to unethical waste of laboratory animals. Various methods, including statistical tests and qualitative visual examination, can reveal incompatibility with normality and the choice of a procedure should not be trivialized. The following minireview will provide a brief overview of diagrammatical methods and statistical tests commonly employed to evaluate congruence with normality. Special attention will be given to the potential pitfalls associated with their application. Normality is an unachievable ideal that practically never accurately describes natural variables, and detrimental consequences of non-normality may be safeguarded by using large samples. Therefore, the very concept of preliminary normality testing is also, arguably provocatively, questioned.

Most classical statistical tests assume data are normally distributed. If this assumption is not met, researchers often turn to non-parametric methods. These methods have some drawbacks, and if no suitable non-parametric test exists, a normal distribution may be used inappropriately instead. A better option is to select a distribution appropriate for the data from dozens available in modern software packages. Selecting a distribution that represents the data generating process is a crucial but overlooked step in analysing data. This paper discusses several alternative distributions and the types of data that they are suitable for.

P-values combined with estimates of effect size are used to assess the importance of experimental results. However, their interpretation can be invalidated by selection bias when testing multiple hypotheses, fitting multiple models or even informally selecting results that seem interesting after observing the data. We offer an introduction to principled uses of p-values (targeted at the non-specialist) and identify questionable practices to be avoided.