animal experimental unit um

• Research Office - Faculty of Medicine - Universiti Malaya
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Lab Services

Animal Experimental Unit

The Animal Experimental Unit (AEU) was established in 2012 and is a 5-storey laboratory animal research facility of the Faculty of Medicine (FOM), University of Malaya. FOM also operates four satellite laboratories at the department of Biomedical Sciences, Parasitology, Pharmacology and Physiology. AEU and its satellite laboratories are AAALAC International accredited.

      +603-7967 4770 /4768 /7577 /7564

     [email protected]

      https://aeu.um.edu.my/

Assoc Prof Dr Wong Pooi Fong  (Head)

Dr ajantha a/p sinniah (deputy head), dr haryanti azura mohd wali (veterinary officer), dr nur azmina mohd zailan  (veterinary officer),              .

Central Research Laboratories

The Central Research Laboratories (CRL) is designated as shared core labs that provide state-of-the-art technologies and conducive laboratory environment to help improve the productivity of research. The equipment are available for the use of research community.

       +603-7967 7535/7579

     [email protected]

      https://crlfom.um.edu.my/

Assoc Prof Dr Puteri Shafinaz Akmar Abdul Rahman  (Head)

  assoc prof dr shatrah othman (deputy head), mrs  wan melissa diyana wan normazlan (science officer), mrs thibashini nair sathasivan   (science officer),                 .

Central Unit for Advance Research Imaging (CENTUARI)

The main goal of our Central Unit is to explore the beauty of science. We provide multiple imaging facilities that covers cell imaging to molecular to in vivo animal or human imaging. 

       +603-7949 2610

     [email protected]

      https://centuari.um.edu.my/

Dr Anwar Norazit  (Head)

               .

Biobank and Tumour Repository

Biobank Unit is a central tissue repository unit that can safely and efficiently handle and track a wide variety of biospecimens.The Biobank initiative was undertaken by UMCRI in 2010.It has now evolved to a facility that has banked more than 10000 good quality specimens and is bona fide unit under Faculty Of Medicine, University of Malaya. In May 2016,Biobank Unit completed a new freezer facility which host 20 unit of ultra-low temperature freezer.

       +603-7967 7899

     [email protected]

Professor Dr Hany Ariffin  (Head)

 mr  mohd syafiq salleh (manager).

ResearchCart360 Supply Store

ResearchCart360 – an online shopping platform – is designed by researchers, and is dedicated to all Malaysian researchers. Commonly used consumables and services in many S&T fields can be found on this platform, with specification, picture and pricing. 

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Assoc Professor Dr Ivy Chung  (Head)

Immunotherapeutics Laboratory (ITL)

The Immunotherapeutics Laboratory is a research facility which operates under biosafety level 2 standards. We focus on building capacity in basic and translational immunology research through tr aining, service and collaboration. We support research from aspects of conceptualisation to assay optimisation. If your area of interest overlaps, drop by and say hi!

       +603-7949 6746

      [email protected]

Assoc Professor Dr Reena A/P Rajasuriar  (Head)

 ms nurul syuhada binti zulhaimi   (science officer ).

Last Update: 25/08/2022

Animal Care and Use

UNIVERSITY OF MALAYA ANIMAL CARE AND USE POLICY (UM ACUP) provides the framework within which animals may be used at the University of Malaya (UM) for teaching and research in a manner that conforms with all government laws and regulations, provides for approved research and teaching activities, and safeguards the health and welfare of staff and students involved in scholarly activities using animals or animal parts derived from animals. As evidence to the full commitment of UM to the judicious and humane use of animals in research and teaching, the UNIVERSITY OF MALAYA INSTITUTIONAL ANIMAL CARE AND USE COMMITTEE (IACUC) was established. UM IACUC is a committee appointed to oversee the UM animal programme, facilities and procedures, including the key function of reviewing and approving Animal Research Protocol Applications (ARPA) for use of animals in research. This is in line with the growing concerns for the ethical use of animals in research. In addition, extensive evidence from various studies has demonstrated that stress resulting from improper management, handling and manipulation of animalswill result in skewed and irreproducible data. Not only is this a waste in terms of resources, such as money and time, but also leads to the unnecessary sacrifice of animals. Thus, it is also the objective of IACUC to disseminate knowledge and information on management practices as well as welfare issues encountered in the use of laboratory animals. Accountability for the proper conduct of research and teaching using animal subjects is the collective responsibility of UM IACUC, UM Laboratory Animal Centre (UM LAC) and the Principle Investigator. UM IACUC, itself, is composed of a minimum of ten members qualified to evaluate the animal programmes and protocol applications under review and who represent several categories of interested people concerned with humane animal care and use including veterinarians, scientists, non-scientists and non-affiliated public members. The Chairperson is an experienced scientist using laboratory animal subjects. The secretary is the Head of UM LAC and the secretariat is the Office of LAC. The membership of IACUC is for a two (2) year non-renewable tenure except for the appointments of the veterinarians and safety officer.

• Review and approve, require modifications in or withhold approval of new ARPA in research and teaching in monthly meetings.
• Make recommendations to the Institutional Officer (The Deputy Vice-Chancellor of Research & Innovation) for any corrections or modifications needed in animal programmes or facilities.
• Review and approve, require modifications in or withhold approval of proposed significant changes of current ARPA in on-going activities.
• Suspend any animal use activities that are not in compliance with acceptable standards in Laboratory Animal Care and Use.
• Review concerns and issues involving the care and use of animals on campus.
• Keep records and maintain the confidentiality of IACUC meetings and activities.
• Quarterly inspections of animal care and use facilities and evaluation of animal care and use programmes in the animal rooms of all facilities on campus together with the relevant Safety and Health Committee.

Related Link

• Organization Chart
• Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC)
• 'The Guide' --The Eighth Edition of the Guide for the Care and Use of Laboratory Animals (NRC 2011)
• Faculty of Medicine Institutional Animal Care and Use Committee (FOM IACUC), University of Malaya
• Animal Experimental Unit

• Faculty of Medicine
• [email protected]
• 03 - 7967 6686

Biomedical Science Department

The Department of Biomedical Science (formerly known as the Biomedical Science Unit under the Department of Molecular Medicine) offers a full time Bachelor of Biomedical Science undergraduate programme and conducts Biomedical research in various disciplines, including Population Genetics, Rare Diseases, Neuroscience, Haematology, Microbiology, Developmental Biology, Cancer Therapeutics, and Natural Products.

The department consists of fifteen (15) academic staff, with two (2) Professors, two (2) Associates Professors, eleven (11) senior lecturers, who are supported by trained Medical Laboratory Technologists. Facilities within the department are designed for teaching and research, with fully equipped lecture theatres, seminar rooms and teaching labs, and research labs for Biochemistry, Haematology, Immunology, Clinical Chemistry, Tissue Culture and Molecular Biology. The department is also home to the Zebrafish Lab, which is one of the satellite facilities for the Faculty’s AAALAC-accredited Animal Experimental Unit.

At present there are nearly 200 undergraduate and postgraduate students, both local and international, making up the student population of the department.

Our Mission

• To produce competent graduates with a sound foundation in the basic medical sciences, in depth knowledge of the major Biomedical Science disciplines with good scientific research skills.
• To produce postgraduates who are competent, able and independent scientists.
• To develop techniques and tools for clinical diagnostics.
• To share the knowledge and skills with others in the country and region and this is envisaged to be achieved through workshops, conferences and staff and student exchange programmes.
• Academic Programme
• Facilities & Services
• Future Plan

Professor. Dr. Chua Kek Heng

Head of Department

+603-79676607 [email protected]

The Biomedical Science has been acknowledged as an important branch of Science and Technology, especially in providing support service in conjunction with various other medical services in health and patient care. Research in the area of Biomedical Science has also emerged as a growing, very important field of science. Thus, the realisation of the importance to fulfil this need drove the Faculty of Medicine to propose the Biomedical Science Degree Programme.

A Board of Studies was formed by the Senate to study the proposal for the implementation of this programme in the Universiti Malaya. After several meetings with the University Malaya Council and Board of Studies, the senate agreed to the proposal and was eventually approved by the Ministry of Education on 15 October 1992. The Department of Allied Health Sciences which comprised units of Biomedical Science, Nursing and Rehabilitation Medicine was formed in early 1993. The first enrolment (13 students) into the Biomedical Science programme was in 1993 and the first batch graduated in 1996. The student intake has increased over the years (not exceeding 40 per year) and there are a total of 100 undergraduates pursuing this programme at various levels. More than 500 students have graduated since the commencement of this programme.In 1997, the University of Malaya Biomedical Science degree programme was accredited by the Institute of Biomedical Sciences (IBMS), UK.

In 2003, the Biomedical Science Unit was merged with the Department of Biochemistry and the merged department was known as Department of Molecular Medicine. The Biomedical Science Programme was run under this department until 2012. The formation of Department of Biomedical Science was approved on 12 June 2012 and on 2nd January 2013, the Department of Biomedical Science was commenced.

Undergraduate Programme: The Biomedical Science Programme spans a minimum period of 4 years. Initially, students are provided with a broad based knowledge of basic medical sciences, allowing students the chance to acquire basic medical laboratory skills. Subsequently students proceed to the specific study of medical laboratory disciplines of their own choice: Anatomic Pathology, Haematology, Clinical Chemistry, Medical Microbiology,Parasitology, Physiology and Pharmacology. Students will learn the principles underlying the various analytical methods and investigatory procedures used in laboratory medicine, and obtain practical training to consolidate theoretical instruction. In addition, instruction is provided on research methodologies as students will be carrying out research projects of their own design during final year. Successful graduates in biomedical science should be able to assume responsible positions in the following situations: (1) as part of a healthcare team that is concerned with the care of patients and/or with basic and applied clinical research; (2) as part of a research team in allied medical disciplines, in food and pharmaceutical industries, in public health, and in biotechnology. Career opportunities are wide ranging and include employment in clinical laboratory service departments, teaching institutions, and research centres in public as well as private sectors. Post-graduate training is strongly encouraged, either within the country or abroad, all towards attaining the goal of heightening the quality of science and medicine. Postgraduate Programme: The department offers postgraduate programmes by research leading to Master of Medical Science and PhD degrees. The intake criteria includes a bachelors degree in the relevant field with a cGPA>3.0 for Master of Medical Science programme and a Masters degree or Bachelors degree with cGPA>3.7 in the relevant field for PhD programme. The areas of research in the Department of Biomedical Science include Medical Genetics, Microbiology, Virology, Pathology, Free Radical Biology and Biochemistry, Neuroscience, Immunology and Natural Products. The department is equipped with reasonably adequate amount of research instruments to support the research activities. The Department has excellent research links and collaborations within the Faculty, University, country as well as Asia- International.

Molecular Biology

• Prof. Dr. Chua Kek Heng
• Dr. Kee Boon Pin
• Dr. Chai Hwa Chia

Biochemistry

• Prof. Dr. Umah Rani Kuppusamy
• Dr. Bavani Arumugam
• Dr.Rozaida @ Poh Yuen Ying  

Neuroscience

• Assoc. Prof. Dr. Azlina Ahmad Annuar
• Dr. Anwar Norazit

Cancer biology

• Dr. Kamariah Ibrahim
• Dr. Nurain Salehan
• Dr. Looi Mee Lee
• Dr. Suzita Mohd Noor

Molecular Microbiology

• Puah Suat Moi
• Tan Soon Hao

Assc. Prof Dr Ong Kien Chai

The Department has specialised equipments that are available for use. Kindly contact the department’s Main Office (03-7967 4949) for further information.

Contact Information

Phone number, email address, facebook page.

Last Update: 17/05/2024

Report Animal Concerns

Unit for Laboratory Animal Medicine

The Unit for Laboratory Animal Medicine (ULAM) partners with the University of Michigan research community to achieve the highest animal welfare standards in the pursuit of impactful science to benefit both human and animal health.

The Unit for Laboratory Animal Medicine (ULAM), part of the  U-M Medical School Office of Research , is one of the nation’s oldest and most recognized programs training laboratory animal veterinarians. In addition to fulfilling its training mission, ULAM has also provided veterinary care to all animals used at the University of Michigan for over 50 years.

The proper care of laboratory animals involves attending to a range of physical and behavioral needs. This includes providing clean, appropriately lighted and well-ventilated housing, the ability to exercise and interact with con-specifics, proper nutrition, and health care.

ULAM provides a variety of services and educational offerings to support the U-M research community and the animals under their care, including:

• Laboratory animal procurement and care
• Veterinary care
• Compliance and oversight monitoring
• Personnel training
• Specialized research support services and activities, and
• Academic teaching and research programs

How ULAM supports research:

Personnel Training & Education

Provides training in the proper care and use of research animals, including the development of novel training strategies and techniques for all animal users through both online and interactive classes that focus on topics such as federal regulations, species-specific characteristics, and appropriate experimental techniques for working with animals.

Animal Husbandry & Housing

ULAM’s certified animal technicians provide a wide array of animal husbandry and housing services to U-M investigators, including cage changing; daily food, water, and health checks; cleaning and maintenance of animal rooms; monitoring of cage/room environmental conditions; and transporting animals to and from campus.

Veterinary Care & Consultation

ULAM veterinary faculty and staff conduct daily observation of all animals in University facilities to ensure and protect proper health and well-being; clinical diagnosis, physical examinations, and laboratory tests to initiate proper treatment on an as-needed basis; provides consultations on a variety of topics, including but not limited to: animal models of human disease, animal-related research techniques, and the policies of granting and regulatory agencies on the care and use of animals.

Animal Ordering & Research Support Services

Offers assistance to investigators and support staff in budget planning for research proposal preparation; provides assistance with animal ordering from approved vendors; and specialized fee-for-service research support services for investigators.

Veterinary Education Programs

Offers a variety of educational opportunities for veterinarians and veterinary students, including: veterinary clinical externships, student summer fellowships, and a postdoctoral clinical training program.

ULAM’s Commitment to Diversity, Equity & Inclusion

The Unit for Laboratory Animal Medicine (ULAM) has defined  Integrity   as one of its Core Values — “a guiding principle to underscore all that we do and our vision for an inclusive culture and shared community that encourages excellence by embracing professionalism, diversity, equity, inclusiveness, respectfulness, and ethical behavior.”

ULAM is firmly committed to anti-racism; fostering an environment where every team member—regardless of their myriad identities—feels like they belong, they are valued, and they are safe; and the continued pursuit of deliberate initiatives to ensure that differences are welcomed, and mutual respect is given at all times and in all circumstances.

Requesting Veterinary Care

Monday – Friday 6:00 AM – 5:00 PM Call ULAM at (734) 936-1037

Monday – Friday 5:00 PM – 6:00 AM Call DPSS at (734) 763-1131

Weekends & Holidays Contact the on-site Veterinary Technician at (734) 936-1037

For more information, please contact ULAM at [email protected] or (734) 764-0277. A list of ULAM contacts is also available in the Personnel Directory .

Last updated:

May 10 2024

• University of Malaya

F N Hamzah University of Malaya | UM  ·  Animal Experimental Unit

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• Asian-Australas J Anim Sci
• v.31(9); 2018 Sep

Guidelines for experimental design and statistical analyses in animal studies submitted for publication in the Asian-Australasian Journal of Animal Sciences

Seongwon seo.

1 Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea

Seoyoung Jeon

2 Asian-Australasian Journal of Animal Sciences, Seoul 08776, Korea

3 Department of Agricultural Biotechnology, College of Agriculture and Life Science, Seoul National University, Seoul 08826, Korea

Animal experiments are essential to the study of animal nutrition. Because of the large variations among individual animals and ethical and economic constraints, experimental designs and statistical analyses are particularly important in animal experiments. To increase the scientific validity of the results and maximize the knowledge gained from animal experiments, each experiment should be appropriately designed, and the observations need to be correctly analyzed and transparently reported. There are many experimental designs and statistical methods. This editorial does not aim to review and present particular experimental designs and statistical methods. Instead, we discuss some essential elements when designing an animal experiment and conducting statistical analyses in animal nutritional studies and provide guidelines for submitting a manuscript to the Asian-Australasian Journal of Animal Sciences for consideration for publication.

INTRODUCTION

For scientific, ethical, and economic reasons, experiments involving animals should be appropriately designed, correctly analyzed, and transparently reported. This increases the scientific validity of the results and maximizes the knowledge gained from each experiment. Nonetheless, biologists, on average, feel uncomfortable with mathematics and statistics, and they often design experiments and analyze data in inappropriate ways [ 1 ]. Therefore, in some fields of research where animal experiments are essential, the editorial board regularly reviews the statistical methodologies reported in the papers and presents their suitability [ 2 – 5 ]. Some fields of research have set up consortia and provide guidelines for animal experiments [ 6 , 7 ], and some scientific journals have guidelines for their authors to follow for publication [ 8 , 9 ]. For example, in the animal science field, the Journal of Dairy Science provides detailed guidance on statistical methodology in the instructions to authors [ 10 ]. Animal Feed Science and Technology has published two editorials that discuss proper experimental design and statistical analyses to guide authors who are submitting manuscripts to the journal [ 11 , 12 ].

The Asian-Australasian Journal of Animal Sciences (AJAS) published the first issue in January 1988, and its contribution and influence to the animal science fields have continuously expanded over the past three decades. In particular, a total of 102 nutritional studies were published in AJAS in 2017, which included 84 in vivo trials. In these studies, statistical methods are essential, and authors should strive to employ an appropriate experimental design and statistical analyses to provide the reader with scientifically relevant and valid knowledge.

This editorial will discuss some of the principles of experimental design and statistical analysis and provide guidelines when submitting nutritional studies to AJAS for consideration for publication.

EXPERIMENTAL DESIGN

Authors must provide details regarding the experimental design in a manuscript such that reviewers and readers have sufficient information about how the study was conducted and can evaluate the quality of the experimental design. Details include animal characteristics (e.g., species, breed, gender, weight), number of treatments, number of experimental and sampling units, arrangement of treatments (e.g., factorial, change-over), and consideration for known variation (e.g., blocking, covariates). Only properly designed experiments can yield valid and reliable results that lead to correct and appropriate interpretations and conclusions in the study.

The experimental unit and the number of replicates

Treatments, the set of circumstances created for an experiment based on research hypotheses, are the effects to be measured and compared in the experiment [ 13 ]. The treatment is applied to the experimental unit, and one experimental unit corresponds to a single replication; Kuehl [ 14 ] defines the experimental unit as “the physical entity” or subject exposed to the treatment independently of other units. The number of replicates (i.e., sample size) is the number of experimental units per each treatment. Defining the experimental unit correctly is crucial for proper experimental design and statistical analysis. However, correctly defining the experiment unit is sometimes not easy. This is especially true in the cases where a group of animals are fed together in a pen, there is debate as to the most appropriate experimental unit between statisticians and biologists [ 11 ].

Like most other biostatisticians [ 14 , 15 ], editors of AJAS have a more conservative view regarding the determination of the experimental unit. For many nutritional studies, the purpose of the experiment is to infer population means. For example, in a feeding trial in which different dietary treatments are applied to different groups of animals, the ultimate goal of the experiment is not to observe the treatment effect within the experimental animals but to investigate its effect on independent animals in the real world. The role of replication is to provide measures of how much the results are reliable and reproducible, and thus replicates are to be independent observations and experimental units must be independent of each other. If a treatment is applied to a group of animals in a single pen, the individual animals are not independent; thus, the pen is considered the experimental unit even when measurements are made individually. The treatment effect is confounded by the effect of the pen in this case, and it is obvious that the pen should be the experimental unit because it is unknown whether the results of the experiment were caused by the treatment of the pen. On the other hand, if treatments are randomly assigned to individual animals within a group of animals in a pen, the individual animal can be considered the experimental unit even though they are in the same pen.

A sufficient number of replicates are needed to obtain a reliable outcome from an experiment. Because the number of replicates is related with the power of a test, more experimental replicates can provide greater statistical power to detect a desired difference among treatments. The cost of replicates, however, is high in animal experiments, and the smallest number of replicates is preferred, as long as it is sufficient to detect a difference. For this purpose, power tests are performed prior to initiating an experiment to determine the required sample size based on expected variation in means and the size of the difference between means that needs to be detected.

Power tests are also useful for supporting the validity of an experiment when no significant difference is observed between the treatment means. It is not uncommon to fail to detect a significant difference between treatments, and in this case, one can argue that significance was not observed simply because the sample size was small. The result from the power test can provide supportive evidence that the reason for the failure to detect a difference between treatments was not because the sample size was small, rather the difference between the treatment means was not great enough to be considered significant.

Therefore, AJAS encourages authors to provide the results of power tests. The results of power tests can be used to justify that the experiment was appropriately designed.

Consideration for known variations

To properly test for treatment effects, factors other than the main treatment that may affect the response of the animals should be minimized or at least accounted for. In this regard, the use of a block or covariate is recommended.

Blocking is a practice wherein the experimental units are placed into groups of similar units based on one or more factors that are known or expected to affect the responses to be measured and tested. Physical and physiological characteristics, such as sex, litter, and initial body weight, are commonly used for blocking in the animal science field. Blocking controls the variability of experimental units and reduces experimental error.

Covariates are variables that are known or expected to be related to the response variables of interest. The primary difference between blocks and covariates is that covariates are continuous variables, whereas blocks are categorical variables. For example, animals can be grouped or blocked as high, medium, and low groups according to their body weight. Conversely, individual body weight can be used as a covariate to reduce the estimates of experimental error in the statistical model. Blocking is applied at the experimental design stage, whereas the use of covariates is applied when conducting statistical analysis.

The use of a block and covariate is a sound and logical way to account for known errors and reduce unexplained errors. The AJAS editorial board thus encourages authors to use blocks and covariates if there are known or expected variables that could have a significant effect on the response to be tested for in the experimental treatments.

When a limited number of animals are available or when individual animal variation is to be removed, crossover (i.e., changeover) designs are often used in animal nutritional studies. In this case, it can be an issue if a carryover effect from a treatment given in a previous period influences the response in the following treatment. It should be noted that crossover designs should be avoided when significant carryover effects are expected [ 16 ]. Even if a significant carryover effect is not expected, the potential for a carryover effect should not be ignored in crossover designs. A sufficient rest or wash-out period between two treatment periods is one of the practical ways to minimize carryover effects. More importantly, the order of treatments for each animal should be balanced to avoid confounding of treatment and period effects and to minimize the influence of carryover effects. In a balanced crossover design, each treatment follows each of the other treatments an equal number of times, and each treatment is assigned once for each animal and the same number of times in each period. When a carryover effect is suspected, its significance also needs to be tested by statistical analysis. The AJAS editorial board recommends authors describe the procedure used to minimize possible carryover effects and show that carryover effects are not significant in their study when using a crossover design.

Randomization

Randomization is an essential procedure to ensure the reliability of the experiment and the validity of the statistical analysis. The purpose of an experiment is to make inferences about the population mean and variance, and the statistical analysis assumes the observations are from a random sample from a normally distributed population. This assumption can be valid only through randomization. In animal nutritional studies, two randomization processes are required: random sampling of experimental units and random allocation of treatments to experimental animals.

Theoretically, experimental animals represent the animal population of interest; thus, they need to be randomly selected from the population. However, this is usually not feasible, if not impossible, in the real world and whether experimental animals can be considered a random sample is questionable. Nevertheless, whenever possible, randomization must be practiced in selecting experimental animals to eliminate biases and to obtain valid estimates of experimental error variance. For example, when a deep analysis is performed on selected animals (e.g., blood analysis for selected animals from a group of animals in each treatment), random selection should be conducted.

Random allocation of treatments to experimental units is the most important and critical step to justify and establish the validity of statistical inferences for the parameters of the population and tests of hypothesis. The experimental errors are assumed to be independently and normally distributed. Estimation of parameters and statistical inferences can be possible if and only if this assumption is valid. Random assignment of treatments to experimental animals is the only method that guarantees the independence of observations and permits us to proceed with the analysis as if the observations are independent and normally distributed. The authors are required to describe the randomization procedure used for their animal trials.

STATISTICAL ANALYSIS

Statistical analysis is conducted to test the hypotheses and significance of tests in a study. There are many methods for conducting statistical analysis and various methods yield different results and conclusions. Proper statistical methods should be applied when conducting an experiment, and details of statistical methods should be provided in the statistical methods section of a manuscript to allow reviewers and readers to assess the quality of statistical methods used in the study.

Statistical models

When submitting a manuscript for publication in AJAS, authors should clearly define their statistical models used for the statistical analysis. Statistical models are usually expressed as linear models with the overall mean of the response variable, fixed or random variables that are known to influence the response variable, and unexplained experimental random error. The statistical model should be consistent with the experimental design and be appropriate to analyze the observations from the experiment. A clear description of the statistical model as an equation, as well as in words, is useful to understand the analytical procedure and the meaning of statistical implications and to evaluate the correctness and relevance of the statistical methods used in the study. Thus, the statistical model is often used as a criterion for the recommendation of manuscript rejection by reviewers and editors [ 11 ].

Statistical methods

Various statistical methods are available, and the choice of method depends on the data type of observations, research questions to answer, and the statistical model.

If observations of the response variables are binary (i.e., yes or no) or categorical, the logistic model or other categorical analysis needs to be used. Sometimes research questions are not about means but seek to understand the quantitative relationship between response variables or between the response variable and treatment (e.g., dose-response analysis). The linear or non-linear regression analysis is the method to be used in this case.

When the response variable of interest is a continuous variable and the research question is about means or an interval of the value, either parametric or non-parametric statistical methods can be applied. The most famous parametric statistical methods are the t-test and analysis of variance (ANOVA). A t-test is used for comparing two samples or treatments, whereas the ANOVA is used when there are more than two treatments. Different methods can be used within a t-test and an ANOVA. For example, if two samples are paired (e.g., blood samples collected before and after treatment in the same animal), a paired t-test is most appropriate. Additionally, because different levels of complexity can exist in statistical models (e.g., the existence of both fixed and random effects and their interactions, repeated measures over time), the most appropriate method may vary by the statistical models when conducting an ANOVA. Parametric methods assume that the observations are independent and normally distributed around their mean. This assumption is generally true in animal nutritional studies as long as randomization is practiced. However, it is always a good practice to test this assumption, especially if variables are expected not to follow it. For example, particle size normally has a log-normal distribution [ 17 ], and thus statistical tests need to be performed on transformed values.

If the observations are not normally distributed or the sample size is not large enough, non-parametric analyses (e.g., Mann-Whitney U test instead of a t-test and Kruskal-Wallis H test instead of a one-way ANOVA) would be the methods of choice. Non-parametric methods do not assume a normal distribution of experimental errors and more powerful to detect differences among treatments than parametric methods (e.g., t-test and ANOVA). Because non-parametric methods have more statistical power, they can exaggerate the significance of the difference between treatments. A parametric method is thus preferred when it is applicable.

Comparing the means of interest

When an ANOVA reveals that the probability that treatment means are all equal is sufficiently small enough to conclude that at least one of the treatment means is different from the others, we may ask further questions, such as which ones are different from each other? Before conducting further analyses, two things are to be considered.

First, we need to determine how small is sufficiently small. This is called the level of significance, and it is normally assumed that the probability of less than 5% (i.e., p<0.05) is statistically significant in animal nutritional studies. The level of significance is also called type I error or α, which is the probability of rejecting a null hypothesis when it is true. If α = 0.05, the test can mistakenly find treatment effects in a maximum of one out of 20 trials. When the p-value obtained using an ANOVA test is less than the level of significance, the results may be meaningful and need to be discussed; thus, comparing the means becomes interesting. If the obtained p-value is larger than the predetermined level of significance, we need to conclude that the null hypothesis is plausible, and we do not have enough evidence to reject the null hypothesis and accept the alternative hypothesis. It should be pointed out that we must not accept the null hypothesis. It is logically impossible to test whether the null hypothesis is true and to prove all the means are the same. We cannot ensure that the null hypothesis would remain plausible if the number of replicates was larger. The authors are thus required to state the level of statistical significance in the statistical analysis section.

Next, we need to determine which techniques are most appropriate for the post hoc analysis on the basis that there is a significant difference among the treatments using an ANOVA. One of the most intuitive and simplest methods to compare the means of interest is linear contrasts. If the number of treatments is t, then a set of t – 1 orthogonal contrasts can be tested. Sets of orthogonal contrasts are not unique for a given experiment; there may be many such sets. Finding an appropriate set of orthogonal contrasts lies in the structure of the treatments. For example, suppose there is an experiment of testing two feed additives as alternatives to antibiotics, and it has four treatments: without feed additives (CONT), antibiotics (ANTI), feed additive A (ADTA), and feed additive B (ADTB). A set of 3 (4 – 1) orthogonal contrasts that can be made, and logical and obvious contrasts are i) CONT vs the others, ii) ANTI vs (ADTA and ADTB), and iii) ADTA vs ADTB.

In addition to linear contrasts, there are many methods available for multiple comparisons of means; the most widely used methods include Dunnett’s test [ 18 ], Tukey’s test [ 19 ], Scheffe’s test [ 20 ], the least significant difference (LSD) [ 21 ], and Duncan’s multiple range test [ 22 ]. Among these, Duncan’s test is the most popular method in the animal nutritional studies. Approximately 37% of animal nutrition papers that conducted pair-wise comparisons in 2017 in AJAS used Duncan’s test. The second most used tests were the LSD and Tukey’s test; each accounted for 14% of multiple comparison tests.

The AJAS editorial board does not take a position on which test is more desirable under certain circumstances and leaves the decision to authors as long as the test can properly test logical questions according to the experimental design. For example, for a dose-response experiment with increasing inclusion levels, testing the significance of differences between particular means is inappropriate. Instead, linear and curve-linear regression for testing the dose-response relationship would be a better choice. A pairwise comparison procedure is appropriate to use when there is no structure among a series of treatments.

Statistical software packages and statistical procedures

There are several software packages available for statistical analysis. Even using the data analysis add-in of Microsoft Excel allows for the t-test, analysis for correlation, linear regression analysis, and one-way ANOVA to be performed. More complicated statistical models, however, require software with statistical packages, which include the statistical analysis system (SAS), general statistics (GENSTAT), statistical program for the social sciences (SPSS), Minitab, and R. The most commonly used statistical software in animal nutritional studies is SAS. Fifty-five percent of animal nutrition papers published in 2017 in AJAS used SAS. The second most popular statistical software was SPSS (27.5%), and more than 83% of the papers used one of them. Like other journals, AJAS takes no position on which of these statistical software packages is more desirable in any particular circumstances and leaves that decision to authors. However, it is required for authors to report which software is used for the statistical analysis.

Even within each statistical software package, there are different procedures that can be used for analyzing data. For example, when conducting an ANOVA in SAS, any procedures that can solve a general linear model, such as the ANOVA, GLM, and MIXED procedures, can be used. However, each procedure may have different features and work better for a specific circumstance. For example, in SAS, compared with the GLM procedure (PROC GLM) which is designed to analyze a general linear model with fixed effects, the MIXED procedure can better handle statistical models having random effects. For the analysis of binary or categorical variables with fixed effects, the GENMOD procedure that uses a generalized linear model should be used instead of PROC GLM. A more recent procedure, PROC GLIMMIX, can analyze statistical models with fixed and random effects for both categorical and continuous variables. AJAS does not take a position on which procedures are more desirable under certain circumstances and leaves the decision to authors as long as the procedure can properly handle the data type. However, when the observations are repeatedly measured or random effects are included in the statistical model, PROC MIXED or PROC GLIMMIX in SAS or similar procedures in other statistical packages are preferred.

Reporting all relevant information is important in scientific papers to increase the transparency and validity of the results and provide information for confidence and limitations of scientific knowledge gained from experiments. Not only the probability value (p-value) but also error measures (e.g., standard error of means [SEM]) should be reported in tables. Likewise, error measures should be present as error bars in figures. Error measures can be expressed in several ways: standard deviation (SD), SEM, and the standard error of the difference (SED). AJAS recommends the presentation of the pooled SEM because the objective of animal nutritional studies is usually to provide inferences about the population. If the sample sizes are different among treatments, the sample sizes are to be reported, as well as pooled SEM. However, the use of SD is also allowed when it is used for descriptive statistics.

When there are outliers or missing data, they need to be clearly reported in the Materials and Methods section or the Results section of the manuscript where it is more appropriate. In particular, the methods and their rationale for identifying outliers should be provided, and the results from the statistical analysis of the data with and without outliers should be compared and discussed in the manuscript.

SUMMARY OF RECOMMENDATIONS

The AJAS editorial board takes no position on which experimental designs and statistical methods are more desirable in certain circumstances and leaves that decision to the authors. Nevertheless, a summary of the recommendations of the AJAS editorial board is as follows:

• Provide details of experimental design and statistical methods in the Materials and Methods section.
• Define the experimental unit and report the number of replicates. Replicates are to be independent observations and experimental units must be independent of each other.
• Conduct power tests and provide their results to justify the experiment was appropriately designed.
• Use blocks or covariates whenever applicable to reduce unexplained experimental errors.
• Describe the procedure used to minimize possible carryover effects and to show carryover effects are not significant when using a crossover design.
• Ensure the implementation of randomization when sampling experimental units and allocating treatments to experimental units.
• Describe the statistical models used for the statistical analysis as equations, as well as in words.
• Use appropriate statistical methods depending on the data type of observations, research questions to be answered, and the statistical model.
• Test if the observations are normally distributed around their mean. If not or the sample size is not large enough, use non-parametric analyses instead; otherwise, use parametric methods.
• State the level of statistical significance in the statistical analysis section.
• Conduct post hoc analysis on the basis that there is a significant difference among the treatments and use appropriate methods according to the structure of the treatments.
• Perform pair-wise comparisons (e.g., Duncan’s multiple range test) only when there is no structure among a series of treatments.
• Report which software and procedures are used for the statistical analysis.
• Use appropriate statistical methods and procedures if observations are repeatedly measured or random effects are expected.
• Present both probability value (p-value) and pooled SEM as error measures. The standard deviation can only be used for descriptive statistics.
• Report outliers or missing data in the Materials and Methods section or the Results section where it is more appropriate.

CONFLICT OF INTEREST

We certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript.

Experimental unit

Failure to identify correctly the experimental unit  is a common mistake which can result in incorrect conclusions. The experimental unit is the physical entity which can be assigned, at random, to a treatment. Commonly it is an individual animal. The experimental unit is also the unit of statistical analysis. However, any two experimental units must be capable of receiving different treatments. Thus, if mice in a cage are given a treatment in the diet, the cage of animals rather than the individual animal is the experimental unit as mice in the cage can not have different treatments, and they may be more similar than mice in different cages. This means that the p-values in the statistical analysis may be incorrect if it is assumed that the mouse is the experimental unit. In this case the statistical analysis should normally be done using the mean of all the animals in the cage.

Experimental units may be:

The individual animal: the breeding female and litter: the cage of animals a part of an animal: an animal for a period of time: more than one type of experimental unit in an experiment., a fancy tri-coloured guinea-pig., the ind ividual animal:.

This is the most common case. Individual animals are assigned to the treatments, but it must be possible for any two animals to receive different treatments. If the treatment is given to several animals in the same cage in the diet or water then individuals within a cage can not receive different treatments, so the individual animal is not the experimental unit.

The breeding fe male and litter:

In teratogenesis experiments the pregnant female is treated with the test substance (or vehicle), but measurements are made on individual pups after birth. Because individual pups within a litter can not have received different treatments the experimental unit is the whole litter and the unit of statistical analysis is the litter mean or proportion affected, possibly weighted by litter size. If there are, say, 10 treated and 10 control females, then the statistical tests will be based on a group size of 10, not on the numbers of individual pups

The c age of animals:

If the treatment is incorporated into the diet or water then any two animals within the same cage can not be assigned to different diets so the experimental unit will be the cage of animals. Cage effects (differences between cages on the same treatment) can occur as a result of fighting, sub-clinical infection or differences in environment due to cage position. The statistical analysis will then be based on the mean of all animals within a cage, and “N” will be the number of cages, not the number of animals. However, if the treatment can be given to individual animals within a cage, say by injection or gavage, then the individual animal may be the experimental unit.

A p art of an animal:

If the experimental treatment is the topical application of a substance to the shaved skin of an animal, then it may be possible to divide an area of skin into a number of patches which can receive different treatments. In this case it is the patch on the back of an animal that is the experimental unit. Similarly, some paired organs may be considered as the experimental unit if they can be assigned to different treatments. If individual cells, say in the brain, can be given different stimuli and recordings of response are made, then individual cells are the experimental unit because different cells can receive different treatments. In this case it may be possible to get a lot of experimental units out of a single individual. However, usually it is wise to use three or four animals (this amounts to a randomised block or repeated measures experimental design) in case individuals behave differently.

An animal for a pe riod of time:

In a crossover experiment  where an animal may be assigned to a treatment for a period of time, then rested and assigned to another treatment, the experimental unit is the animal for a period of time.

M ore than one type of experimental unit in an experiment.

Occasionally “split plot” or “nested” experiments are designed where there are two classes of experimental unit. For example, a researcher may want to compare two diets each with or without a vitamin supplement on growth rate in mice. Suppose 20 cages each containing two mice are assigned to the project, with 10 cages being assigned at random to each diet. Suppose also that the vitamin supplement can be given individually by injection or gavage to one of the two mice, with the other one receiving the vehicle as a control. In this case the cage is the experimental unit for comparing the two diets using the average growth rate of the two mice in the cage, but the mouse is the experimental unit for comparing the effect of the vitamin supplement. With this design it will also be possible to find out whether the response to the vitamin depends on the diet. Split plot designs can be economical and powerful, but the statistical analysis can become complicated. Expert advice should be obtained before starting such an experiment.

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Article Contents

Introduction, experimental design: initial steps, design of the animal experiment, experimental design: final considerations.

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Practical Aspects of Experimental Design in Animal Research

Paula D. Johnson, D.V.M., M.S., is Executive Director, Southwest Association for Education in Biomedical Research, University of Arizona, Tucson; David G. Besselsen, D.V.M., Ph.D., is Veterinary Specialist and Chief, Pathology Services, University Animal Care, University of Arizona, Tucson.

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Paula D. Johnson, David G. Besselsen, Practical Aspects of Experimental Design in Animal Research, ILAR Journal , Volume 43, Issue 4, 2002, Pages 202–206, https://doi.org/10.1093/ilar.43.4.202

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A brief overview is presented of the key steps involved in designing a research animal experiment, with reference to resources that specifically address each topic of discussion in more detail. After an idea for a research project is conceived, a thorough review of the literature and consultation with experts in that field are pursued to refine the problem statement and to assimilate background information that is necessary for the experimental design phase. A null and an alternate hypothesis that address the problem statement are then formulated, and only then is the specific design of the experiment developed. Likely the most critical step in designing animal experiments is the identification of the most appropriate animal model to address the experimental question being asked. Other practical considerations include defining the necessary control groups, randomly assigning animals to control/treatment groups, determining the number of animals needed per group, evaluating the logistics of the actual performance of the animal experiments, and identifying the most appropriate statistical analyses and potential collaborators experienced in the area of study. All of these factors are critical to designing an experiment that will generate scientifically valid and reproducible data, which should be considered the ultimate goal of any scientific investigation.

Experimental design is obviously a critical component of the success of any research project. If all aspects of experimental design are not thoroughly addressed, scientists may reach false conclusions and pursue avenues of research that waste considerable time and resources. It is therefore critical to design scientifically sound experiments and to follow standard laboratory practices while performing these experiments to generate valid reproducible data ( Bennett et al. 1990 ; Diamond 2001 ; Holmberg 1996 ; Larsson 2001 ; Sproull 1995 ; Weber and Skillings 2000 ; Webster 1985 ; Whitcom 2000 ). Data generated by this approach should be of sufficient quality for publication in well-respected peer-reviewed journals, the major form of widespread communication and archiving experimental data in research. This article provides a brief overview of the steps involved in the design of animal experiments and some practical information that should also be considered during this process.

Literature Search

A thorough search of the scientific literature must be performed to determine what is known about the focus of the study. The search should include current and past journal articles and textbooks, as well as information available via the internet. Journal searches can be performed in any number of appropriate journal databases or indexes (e.g., MEDLINE, TOXLINE, PUBMED, NCBI, AGRICOLA). The goals of the literature search are to learn of pertinent studies and methods, identify appropriate animal models, and eliminate unnecessary duplication of research. The “3Rs” of animal research ( Russell and Burch 1959 ) should also be considered at this stage: reduction of animal numbers, refinement of methods, and replacement of animals by viable nonanimal alternatives when these exist. The literature search is also an important component of an institutional animal care and use committee (IACUC 1 ) protocol submission to provide evidence that the project is not duplicative, that alternatives to the use of animals are not available, and that potentially painful procedures are justified.

Scientific Method

The core aspect of experimental design is the scientific method ( Barrow 1991 ; Kuhn 1962 ; Lawson 2002 ; Wilson 1952 ). The scientific method consists of four basic steps: (1) observation and description of a scientific phenomena, (2) formulation of the problem statement and hypothesis, (3) use of the hypothesis to predict the results of new observations, and (4) the performance of methods or procedures to test the hypothesis.

Problem Statement, Objectives, and Hypotheses

It is critical to define the problem statement, objectives, and hypotheses clearly. The problem statement should include the issue that will be addressed experimentally and its significance (e.g., potential application to human or animal health, improved understanding of biological processes). Objectives should be stated in a general description of the overall goals for the proposed experiments and the specific questions being addressed. Hypotheses should include two distinct and clearly defined outcomes for each proposed experiment (e.g., a null and an alternate hypothesis). These outcomes may be thought of as the two experimental answers to the specific question being investigated: The null hypothesis is defined as no difference between experimental groups, and the alternate hypothesis is defined as a real difference between experimental groups. Development of a clearly stated problem statement and the hypotheses are necessary to proceed to the next stage of the experimental design process, although they obviously can (and likely will) be modified as the process continues. Examples of a problem statement and various types of hypotheses follow:

Problem statement: Which diet causes more weight gain in rats: diet A or diet B?

Null hypothesis: Groups are expected to show the same results (e.g., rats on diet A will gain the same amount of weight as rats on diet B).

Alternate hypothesis: Experimental groups are expected to show different results (e.g., rats will gain more weight on diet A than diet B, or vice versa).

Nontestable hypothesis: A result cannot be easily defined or interpreted (e.g., rats on diet A will look better than rats on diet B). What does “better” mean? Its definition must be clearly stated to create a testable hypothesis.

Identification of Animal Model

In choosing the most appropriate animal models for proposed experiments, we offer the following recommendations: (1) Use the lowest animal on the phylogenic scale (in accordance with replacement, one of the 3Rs). (2) Use animals that have the species- and/or strain-specific characteristics desirable or required for the specific study proposed. (3) Consider the costs associated with acquiring and maintaining the animal model during the period of experimentation. (4) Perform a thorough literature search, network with colleagues within the selected field of study, and/or contact commercial vendors or government-supported repositories of animal models to identify a potential source of the animal model. (5) Consult with laboratory animal veterinarians before final determination of the animal model.

Identification of Potential Collaborators

The procedures required to carry out the experiments will determine what, if any, additional expertise is needed. It is important to identify and consult with potential collaborators at the beginning of project development to determine who will be working on the project and in what capacity (e.g., as coinvestigators, consultants, or technical support staff). Collaborator input into the logistics and design of the experiments and proper sample acquisition are critical to ensure the validity of the data generated. Core facilities at larger research institutions provide many services that involve highly technical procedures or require expensive equipment. Identification of existing core facilities can often lead to the development of a list of potential intramural collaborators.

Research Plan

A description of the experimental manipulations required to address the problem statement, objectives, and hypotheses should be carefully devised and documented ( Keppel 1991 ). This description should specify the experimental variables that are to be manipulated, suitable test parameters that accurately assess the effects of experimental variable manipulation, and the most appropriate methods for sample acquisition and generation of the test data. The overall practicality of the project as well as the time frame for data collection and evaluation are determined at this stage in the development process.

Practical issues that may need to be addressed include the lifespan of the animal model (for chronic studies), the anticipated progression of disease in that model (to determine appropriate time points for evaluation), the amount of personnel time available for the project, and the costs associated with performing the experiments ( De Boer et al. 1975 ). If the animals are to receive chemical or biological treatments, an appropriate method for administration must be identified (e.g., per os via the diet or in drinking water [soluble substances only], by osmotic pump, or by injection). Known or potential hazards must also be identified, and appropriate precautions to minimize risk from these hazards must be incorporated into the plan. All experimental procedures should be detailed through standard operating procedures, a requirement of good laboratory practice standards ( EPA 1989 ; FDA 1987 ).

Finally, the methods to be used for data analysis should be determined. If statistical analysis is required to document a difference between experimental groups, the appropriate statistical tests should be identified during the design stage. A conclusion will be drawn subsequently from the analysis of the data with the initial question answered and/or the hypotheses accepted or rejected. This process will ultimately lead to new questions and hypotheses being formulated, or ideas as to how to improve the experimental design.

Experimental Unit

The entity under study is the experimental unit, which could be an individual animal or a group. For example, an individual rat is considered the experimental unit when a drug therapy or surgical procedure is being tested, but an entire litter of rats is the experimental unit when an environmental teratogen is being tested. For purposes of estimating error of variance, or standard error for statistical analysis, it is necessary to consider the experimental unit ( Weber and Skillings 2000 ). Many excellent sources provide discussions of the types of experimental units and their appropriateness ( Dean and Voss 1999 ; Festing and Altman 2002 ; Keppel 1991 ; Wu and Hamada 2000 ).

N Factor: Experimental Group Size

The assignment of an appropriate number of animals to each group is critical. Although formulas to determine the proper number of animals can be found in standard statistical texts, we recommend consulting a statistician to ensure appropriate experimental design for the generation of statistically significant results ( Zolman 1993 ). Indeed, the number of animals assigned to each experimental group is often determined by the particular statistical test on the basis of the anticipated magnitude of difference between the expected outcomes for each group. The number of animals that can be grouped in standard cages is a practical consideration for determining experimental group size. For example, standard 71 sq in (460 sq cm) polycarbonate shoebox cages can house up to four adult mice, so group sizes that are divisible by four will maximize group size and minimize per diem costs.

A plethora of variables (e.g., genetic, environmental, infectious agents) can potentially affect the outcome of studies performed with animals. It is therefore critical to use control animals to minimize the impact of these extraneous variables or to recognize the possible presence of unwanted variables. In general, each individual experiment should use control groups of animals that are contrasted directly to the experimental groups of animals. Multiple types of controls include positive, negative, sham, vehicle, and comparative.

Positive Controls

In positive control groups, changes are expected. The positive control acts as a standard against which to measure difference in severity among experimental groups. An example of a positive control is a toxin administered to an animal, which results in reproducible physiological alterations or lesions. New treatments can then be used in experimental groups to determine whether these alterations may be prevented or cured. Positive controls are also used to demonstrate that a response can be detected, thereby providing some quality control on the experimental methods.

Negative Controls

Negative controls are expected to produce no change from the normal state. In the example above, the negative control would consist of animals not treated with the toxin. The purpose of the negative control is to ensure that an unknown variable is not adversely affecting the animals in the experiment, which might result in a false-positive conclusion.

Sham Controls

A sham control is used to mimic a procedure or treatment without the actual use of the procedure or test substance. A placebo is an example of a sham control used in pharmaceutical studies ( Spector 2002 ). Another example is the surgical implantation of “X” into the abdominal cavity. The treated animals would have X implanted, whereas the sham control animals would have the same surgical procedure with the abdominal cavity opened, as with the treated animals, but without having the X implanted.

Vehicle Controls

A vehicle control is used in studies in which a substance (e.g., saline or mineral oil) is used as a vehicle for a solution of the experimental compound. In a vehicle control, the supposedly innocuous substance is used alone, administered in the same manner in which it will be used with the experimental compound. When compared with the untreated control, the vehicle control will determine whether the vehicle alone causes any effects.

Comparative Controls

A comparative control is often a positive control with a known treatment that is used for a direct comparison to a different treatment. For example, when evaluating a new chemopreventive drug regime in an animal model of cancer, one would want to compare this regime to the chemopreventive drug regime currently considered “accepted practice” to determine whether the new regime improves cancer prevention in that model.

Randomization

Randomization of the animals assigned to different experimental groups must be achieved to ensure that underlying variables do not result in skewed data for each experimental group. To achieve randomization, it is necessary to begin by defining the population. A homogeneous population consists of animals that are considered to share some characteristics (e.g., age, sex, weight, breed, strain). A heterogeneous population consists of animals that may not be the same but may have some common feature. Generally, the better the definition of the group, the less variable the experimental data, although the results may be less pertinent to large broad populations. Methods commonly used to achieve randomization include the following ( Zolman 1993 ):

Identifying each animal with a unique identification number, then drawing numbers “out of a hat” and randomly assigning them in a logical fashion to different groups. For example, the first drawn number is assigned to group 1, the second to group 2, the third to group 1, the fourth to group 2, and so forth. Dice or cards may also be used to randomly assign animals to experimental groups.

Using random number tables or computer-generated numbers/sampling to achieve randomization.

Experimental Protocol Approval

Animal experimentation requires IACUC approval of an animal care and use protocol if the species used are covered under the Animal Welfare Act (regardless of funding source), the research is supported by the National Institutes of Health and involves the use of vertebrate species, or the animal care program is accredited by the Association for the Assessment and Accreditation of Laboratory Animal Care International ( Silverman et al. 2000 ). In practice, virtually all animal experiments require IACUC approval, which entails full and accurate completion of appropriate protocol forms for submission to the IACUC, followed by clarification or necessary modification of any procedures the IACUC requires. Approval must be obtained before the animal purchase or experimentation and is required before submission of a grant proposal by some funding agencies. If the research involves hazardous materials, then protocol approval from other intramural oversight committees or departments may also be required (e.g., a Biosafety Committee if infectious agents or recombinant DNA are to be used, or a Radiation Safety Committee if radioisotopes or irradiation are to be used).

Animal welfare regulations and Public Health Service policy mandate that individuals caring for or using research animals must be appropriately trained. Specifically, all personnel involved in a research project must be appropriately qualified and/or trained in the methods they will be performing for that project. The institution where the research is being performed is responsible for ensuring this training, although the actual training may occur elsewhere.

Pilot Studies

Pilot studies use a small number of animals to generate preliminary data and/or allow the procedures and techniques to be solidified and “perfected” before large-scale experimentation. These studies are commonly used with new procedures or when new compounds are tested. Preliminary data are essential to show evidence supporting the rationale of a proposal to a funding agency, thereby increasing the probability of funding for the proposal. All pilot projects must have IACUC approval, as for any animal experiment. As soon as the pilot study is completed, the IACUC representative will either give the indication to proceed to a full study or will indicate that the experimental manipulations and/or hypotheses need to be modified and evaluated by additional pilot studies.

Data Entry and Analysis

The researcher has the ultimate responsibility for collecting, entering, and analyzing the data correctly. When dealing with large volumes of data, it is especially easy for data entry errors to occur (e.g., group identifications switched, animal identifications transposed). Quality assurance procedures to identify data entry errors should be developed and incorporated into the experimental design before data analysis. This process can be accomplished by directly comparing raw (original) data for individual animals with the data entered into the computer or with compiled data for the group as a whole (to identify potential “outliers,” or data that deviates significantly from the rest of the members of a group). The analysis of the data varies depending on the type of project and the statistics required to evaluate it. Because this topic is beyond the scope of this article, we refer the reader to the many outstanding books and articles on statistical analysis ( Cobb 1998 ; Cox and Reid 2000 ; Dean and Voss 1999 ; Festing and Altman 2002 ; Lemons et al. 1997 ; Pickvance 2001 ; Wasserman and Kutner 1985 ; Wilson and Natale 2001 ; Wu and Hamada 2000 ).

Detection of flaws, in the developing or final experimental design is often achieved by several levels of review that are applicable to animal experimentation. For example, grant funding agencies and the IACUC provide input into the content and design of animal experiments during their review processes and may also serve as advisory consultants before submission of the grant proposal or animal care and use protocol. Scientific peers and the scientific literature also provide invaluable information applicable to experimental design, and these resources should be consulted throughout the experimental design process. Finally, scientific peer-reviewed journals provide a final critical evaluation of the soundness of the experimental design. The overall quality of the experimental data is evaluated and a determination is made as to whether it is worthy of publication. Obviously, discovering major experimental design deficiencies during manuscript peer review is not desirable. Therefore, pursuit of scientific peer review throughout the experimental design process should be exercised routinely to ensure the generation of valid, reproducible, and publishable data.

The steps listed below comprise a practical sequence for designing and conducting scientific studies. We recommend that investigators

Conduct a complete literature review and consult experts who have experience with the techniques proposed in an effort to become thoroughly familiar with the topic before beginning the experimental design process.

Ask a specific question and/or formulate an appropriate hypothesis. Then design the experiments to specifically address that problem/question.

Consult a biostatistician during the design phase of the project, not after performing the experiments.

Choose proper controls to ensure that only the variable of interest is evaluated. More than one control is frequently required.

Start with a small pilot project to generate preliminary data and work out procedures and techniques. Then proceed to larger scale experiments to generate statistical significance.

Modify original question and procedures, ask new questions, and begin again.

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Abbreviation used in this article: IACUC, institutional animal care and use committee.

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4. Randomisation State whether randomisation was used to allocate experimental units to control and treatment groups. If done, provide the method used to generate the randomisation sequence. explanation

Using appropriate randomisation methods during the allocation to groups ensures that each experimental unit has an equal probability of receiving a particular treatment and provides balanced numbers in each treatment group. Selecting an animal ‘at random’ (i.e. haphazardly or arbitrarily) from a cage is not statistically random as the process involves human judgement. It can introduce bias that influences the results, as a researcher may (consciously or subconsciously) make judgements in allocating an animal to a particular group, or because of unknown and uncontrolled differences in the experimental conditions or animals in different groups. Using a validated method of randomisation helps minimise selection bias and reduce systematic differences in the characteristics of animals allocated to different groups  [1-3] . Inferential statistics based on non-randomised group allocation are not valid  [4,5] . Thus, the use of randomisation is a prerequisite for any experiment designed to test a hypothesis. Examples of appropriate randomisation methods include online random number generators (e.g.  https://www.graphpad.com/quickcalcs/randomize1/ ), or a function like Rand() in spreadsheet software such as Excel, Google Sheets, or LibreOffice. The EDA has a dedicated feature for randomisation and allocation concealment  [6] .

Systematic reviews have shown that animal experiments that do not report randomisation or other bias-reducing measures such as blinding, are more likely to report exaggerated effects that meet conventional measures of statistical significance  [7-9] . It is especially important to use randomisation in situations where it is not possible to blind all or parts of the experiment but even with randomisation, researcher bias can pervert the allocation. This can be avoided by using allocation concealment (see   item 5 – Blinding ). In studies where sample sizes are small, simple randomisation may result in unbalanced groups; here randomisation strategies to balance groups such as randomising in matched pairs  [10-12]  and blocking are encouraged  [13] . Reporting the precise method used to allocate animals or experimental units to groups enables readers to assess the reliability of the results and identify potential limitations.

Report the type of randomisation used (simple, stratified, randomised complete blocks, etc.; see “Considerations for the randomisation strategy” below), the method used to generate the randomisation sequence (e.g. computer-generated randomisation sequence, with details of the algorithm or programme used), and what was randomised (e.g. treatment to experimental unit, order of treatment for each animal). If this varies between experiments, report this information specifically for each experiment. If randomisation was not the method used to allocate experimental units to groups state this explicitly and explain how the groups being compared were formed. 

• Schulz KF, Chalmers I, Hayes RJ and Altman DG (1995). Empirical evidence of bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. Jama . doi: 10.1001/jama.1995.03520290060030
• Schulz KF and Grimes DA (2002). Allocation concealment in randomised trials: defending against deciphering. Lancet (London, England) . doi: 10.1016/s0140-6736(02)07750-4
• Chalmers TC, Celano P, Sacks HS and Smith Jr H (1983). Bias in treatment assignment in controlled clinical trials. New England Journal of Medicine . doi: 10.1056/NEJM198312013092204
• Greenberg BG (1951). Why randomize? Biometrics . doi: 10.2307/3001653
• Altman DG and Bland JM (1999). Statistics notes. Treatment allocation in controlled trials: why randomise? BMJ . doi: 10.1136/bmj.318.7192.1209
• Percie du Sert N, Bamsey I, Bate ST, Berdoy M, Clark RA, Cuthill I, Fry D, Karp NA, Macleod M, Moon L, Stanford SC and Lings B (2017). The Experimental Design Assistant. PLoS Biol . doi: 10.1371/journal.pbio.2003779
• Hirst JA, Howick J, Aronson JK, Roberts N, Perera R, Koshiaris C and Heneghan C (2014). The need for randomization in animal trials: an overview of systematic reviews. PLoS ONE . doi: 10.1371/journal.pone.0098856
• Vesterinen HM, Sena ES, ffrench-Constant C, Williams A, Chandran S and Macleod MR (2010). Improving the translational hit of experimental treatments in multiple sclerosis. Multiple Sclerosis Journal . doi: 10.1177/1352458510379612
• Bebarta V, Luyten D and Heard K (2003). Emergency medicine animal research: does use of randomization and blinding affect the results? Acad Emerg Med . doi: 10.1111/j.1553-2712.2003.tb00056.x
• Taves DR (1974). Minimization: a new method of assigning patients to treatment and control groups. Clinical pharmacology and therapeutics . doi: 10.1002/cpt1974155443
• Saint-Mont U (2015). Randomization does not help much, comparability does. PLOS ONE . doi: 10.1371/journal.pone.0132102
• Laajala TD, Jumppanen M, Huhtaniemi R, Fey V, Kaur A, Knuuttila M, Aho E, Oksala R, Westermarck J, Mäkelä S, Poutanen M and Aittokallio T (2016). Optimized design and analysis of preclinical intervention studies in vivo. Scientific reports . doi: 10.1038/srep30723
• Bate ST and Clark RA (2014). The design and statistical analysis of animal experiments. Cambridge University Press. https://www.cambridge.org/core/books/design-and-statistical-analysis-of-animal-experiments/BDD758F3C49CF5BEB160A9C54ED48706
• Kang M, Ragan BG and Park JH (2008). Issues in outcomes research: an overview of randomization techniques for clinical trials. J Athl Train . doi: 10.4085/1062-6050-43.2.215
• Altman DG and Bland JM (2005). Treatment allocation by minimisation. BMJ . doi: 10.1136/bmj.330.7495.843

• Animal characteristics
• Independent variables
• Group and sample size

Experimental unit

• Inclusion and exclusion
• Intervention
• Measurement
• Overview and demonstration of the EDA
• Getting the most out of the EDA
• What is the experiment diagram?
• Troubleshooting

How to identify the experimental unit in an in vivo experiment.

Why is the experimental unit important, the individual animal.

• A breeding female and litter
• The cage of animals

A part of an animal

An animal for a period of time, experiments with more than one experimental unit, representing the experimental unit in the eda.

The experimental unit is the entity you want to make inferences about (in the population) based on the sample (in your experiment).

The experimental unit is the entity subjected to an intervention independently of all other units. It must be possible to assign any two experimental units to different treatment groups. 

The sample size is the number of experimental units per group. You need enough experimental units in your experiment for reliable results. But, if you do not correctly identify the experimental unit, there is a risk you overestimate your sample size which could invalidate the results of your statistical analysis and conclusions.

The British Pharmacological Society have created an animated video to introduce the concept of experimental units and how correctly identifying them is important to interpret your results.

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Know your experimental unit

In animal experiments the experimental unit is often the individual animal. In this case, each animal is allocated to a particular treatment group independently of other animals. But this is not always the case. Depending on the treatment administered, the experimental unit may be bigger than the animal (e.g. a litter or a cage) or smaller than the animal (e.g. part of the animal or an animal for a period of time). You can learn more about how to identify your experimental unit using the examples in this section. 

Note that if you take multiple measurements from the same animal it does not mean that each animal provides multiple experimental units. The experimental unit is defined as the entity which receives an intervention or treatment, regardless of how many times you take measurements from it.

This is the most common situation and individual animals are independently assigned to distinct categories of the variable(s) of interest. It must be possible for any two individual animals to receive different treatments. 

An example could be an experiment with four groups defined by two variables of interest, sex and exercise. The categories of the variables of interest are 'female with exercise', 'female no exercise', 'male with exercise', and 'male no exercise'. Animals are either male or female independently of other animals, and each animal is allocated to different activity levels independently of the other animals. Thus, the experimental unit is the individual animal.  

A breeding female and litter 

Consider a teratogenesis experiment where the pregnant female receives a treatment and measurements are made on the individual pups after birth. Animals within a litter are all exposed to the same treatment – the experimental unit is therefore the whole litter. In this case, the variable  ‘individual pups’ is nested into the experimental unit ‘litter’.

The cage of animals 

If animals are group housed in a cage and all animals within that cage receive the same treatment, for example in the drinking water or diet, then the experimental unit is the cage of animals. 

However, if animals are group housed but can each receive a different treatment, for example by injection (and the treatment will not contaminate cage mates), then the experimental unit would be the individual animal.

If animals are exposed to a treatment via topical application, it may be possible to divide an area of skin into a number of different patches which can each receive distinct treatments. In this situation, the patch of skin on the animal is the experimental unit.

If individual cells can be stimulated independently and recording of the responses is made at the individual cell level, the experimental unit for the stimulation is the individual cell. Provided the experiment does not include another treatment which the whole animal is exposed to (e.g. drug injection or genotype), the individual cell can be the experimental unit for the whole experiment and a single animal provides many experimental units. It is important to note that if just a single animal is used, then the results hold true for that animal alone and cannot be generalised to the population.

When a single animal provides multiple experimental units, to avoid the confounding effect of between-animal variability, the individual animal should be used as a blocking factor and more than one animal should be used to improve generalisability. The number of animals needed depends on the between-animal variability.

Another scenario where a single animal can provide several experiment units is in a crossover experiment. In this experimental design, each animal is used as its own control and receives distinct treatments, separated by wash out periods. As animals can be exposed to different treatments in different test periods, the experimental unit is the animal for period of time.

Occasionally, there may be multiple experimental units in a single experiment, for example in a so-called split plot experiment.

Consider a situation where the effects of two different treatments (diet and vitamin supplements) on growth rate are investigated in mice. Diet is administered at the cage level and all mice housed in the same cage receive the same diet – the experimental unit for the diet treatment is therefore the cage. However, the vitamin supplement is administered by gavage meaning animals within the same cage can receive different supplements – the experimental unit for the vitamin supplement is the individual mouse.

This type of design is powerful as it enables researchers to investigate whether the effect of the vitamin is related to the diet administered. However the statistical analysis can be complicated and expert statistical advice should be sought before conducting such an experiment.

On your EDA diagram, the experimental unit is represented by the experimental unit node . This node is connected from one of the group nodes as shown in the image below.

If the experimental unit is the same throughout your experiment you only need one experimental unit node in your diagram. If there are multiple experimental units, multiple nodes may be necessary to clarify which unit different interventions are applied to.

Festing, MFW, et al. (2002). The design of animal experiments: reducing the use of animals in research through better experimental design . Royal Society of Medicine.

Lazic, SE (2010). The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis? BMC Neurosci 11:5. doi: 10.1186/1471-2202-11-5

Lazic, SE, Clarke-Williams, CJ and Munafo, MR (2018). What exactly is 'N' in cell culture and animal experiments? PLOS Biol 16(4):e2005282. doi: 10.1371/journal.pbio.2005282