Observational Study Design in Veterinary Pathology, Part 1: Study Design

Caswell, J L and Bassel, L L and Rothenburger, J L and Gröne, A and Sargeant, J M and Beck, A P and Ekman, S and Gibson-Corley, K N and Kuiken, T and LaDouceur, E E B and Meyerholz, D K and Origgi, F C and Posthaus, H and Priestnall, S L and Ressel, L and Sharkey, L and Teixeira, L B C and Uchida, K and Ward, J M and Webster, J D and Yamate, J (2018) Observational Study Design in Veterinary Pathology, Part 1: Study Design. VETERINARY PATHOLOGY, 55 (6).

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Observational studies are the basis for much of our knowledge of veterinary pathology and are highly relevant to the daily practice of pathology. However, recommendations for conducting pathology-based observational studies are not readily available. In part 1 of this series, we offer advice on planning and conducting an observational study with examples from the veterinary pathology literature. Investigators should recognize the importance of creativity, insight, and innovation in devising studies that solve problems and fill important gaps in knowledge. Studies should focus on specific and testable hypotheses, questions, or objectives. The methodology is developed to support these goals. We consider the merits and limitations of different types of analytic and descriptive studies, as well as of prospective vs retrospective enrollment. Investigators should define clear inclusion and exclusion criteria and select adequate numbers of study subjects, including careful selection of the most appropriate controls. Studies of causality must consider the temporal relationships between variables and the advantages of measuring incident cases rather than prevalent cases. Investigators must consider unique aspects of studies based on archived laboratory case material and take particular care to consider and mitigate the potential for selection bias and information bias. We close by discussing approaches to adding value and impact to observational studies. Part 2 of the series focuses on methodology and validation of methods.

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