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A Visual and Intuitive Comparison of Experimental Designs in the Health Sciences
Author(s) -
Hummel Ruth M
Publication year - 2019
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2019.33.1_supplement.598.25
Subject(s) - design of experiments , computer science , grasp , research design , sample size determination , factorial experiment , sample (material) , clinical study design , fractional factorial design , management science , data science , statistics , machine learning , mathematics , software engineering , medicine , engineering , chemistry , chromatography , pathology , clinical trial
Many students and practitioners struggle to understand experimental designs and their implications in appropriate modeling. Full factorial designs and hierarchical designs are commonly used, but less commonly well‐understood. Then, when a full factorial design is not an option due to limited time or resources, how do you choose between potential reduced designs? What are the implications from various design choices on what you can learn from your experiment? How will the analysis change when the design is changed? If you find additional resources after the experiment is run, how do you most effectively augment the original design? In this talk, we will explore some visualizations of experimental designs that can help students to explore these questions and grasp the experimental design options much more intuitively than in the traditional treatment of these topics. We will discuss the need to have a sense of the variability you anticipate in your experiment, and how this translates to simulations of the power and sample size relationship and comparisons of power over various design options. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .