Guidelines for the design and statistical analysis of experiments using laboratory animals. Export

@article{citeulike:1443979, abstract = {For ethical and economic reasons, it is important to design animal experiments well, to analyze the data correctly, and to use the minimum number of animals necessary to achieve the scientific objectives---but not so few as to miss biologically important effects or require unnecessary repetition of experiments. Investigators are urged to consult a statistician at the design stage and are reminded that no experiment should ever be started without a clear idea of how the resulting data are to be analyzed. These guidelines are provided to help biomedical research workers perform their experiments efficiently and analyze their results so that they can extract all useful information from the resulting data. Among the topics discussed are the varying purposes of experiments (e.g., exploratory vs. confirmatory); the experimental unit; the necessity of recording full experimental details (e.g., species, sex, age, microbiological status, strain and source of animals, and husbandry conditions); assigning experimental units to treatments using randomization; other aspects of the experiment (e.g., timing of measurements); using formal experimental designs (e.g., completely randomized and randomized block); estimating the size of the experiment using power and sample size calculations; screening raw data for obvious errors; using the t-test or analysis of variance for parametric analysis; and effective design of graphical data.}, address = {MRC Toxicology Unit, University of Leicester, UK.}, author = {Festing, M. F. and Altman, D. G.}, citeulike-article-id = {1443979}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/12391400}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=12391400}, issn = {1084-2020}, journal = {ILAR J}, keywords = {animal\_studies, experiment, experimental\_design, research\_ethics, scientific\_method, scientific\_practice, statistics}, number = {4}, pages = {244--258}, posted-at = {2007-07-09 12:35:58}, priority = {2}, title = {Guidelines for the design and statistical analysis of experiments using laboratory animals.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/12391400}, volume = {43}, year = {2002} }

TY - JOUR ID - citeulike:1443979 L3 - citeulike-article-id:1443979 N2 - For ethical and economic reasons, it is important to design animal experiments well, to analyze the data correctly, and to use the minimum number of animals necessary to achieve the scientific objectives---but not so few as to miss biologically important effects or require unnecessary repetition of experiments. Investigators are urged to consult a statistician at the design stage and are reminded that no experiment should ever be started without a clear idea of how the resulting data are to be analyzed. These guidelines are provided to help biomedical research workers perform their experiments efficiently and analyze their results so that they can extract all useful information from the resulting data. Among the topics discussed are the varying purposes of experiments (e.g., exploratory vs. confirmatory); the experimental unit; the necessity of recording full experimental details (e.g., species, sex, age, microbiological status, strain and source of animals, and husbandry conditions); assigning experimental units to treatments using randomization; other aspects of the experiment (e.g., timing of measurements); using formal experimental designs (e.g., completely randomized and randomized block); estimating the size of the experiment using power and sample size calculations; screening raw data for obvious errors; using the t-test or analysis of variance for parametric analysis; and effective design of graphical data. IS - 4 JF - ILAR J SN - 1084-2020 AD - MRC Toxicology Unit, University of Leicester, UK. EP - 258 TI - Guidelines for the design and statistical analysis of experiments using laboratory animals. VL - 43 SP - 244 KW - animal_studies KW - experiment KW - experimental_design KW - research_ethics KW - scientific_method KW - scientific_practice KW - statistics AU - Festing, MF AU - Altman, DG PY - 2002/// UR - http://view.ncbi.nlm.nih.gov/pubmed/12391400 ER -