ReviewRadiation research2023
Feasibility of Reducing Animal Numbers in Radiation Countermeasure Experiments from Historic Levels when using Sample Size Calculations.
Review in Radiation research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Authors and funding
5 authors.
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Abstract
Historically, animal numbers have most often been in the hundreds for experiments designed to estimate the dose reduction factor (DRF) of a radiation countermeasure treatment compared to a control treatment. Before 2010, researchers had to rely on previous experience, both from others and their own, to determine the number of animals needed for a DRF experiment. In 2010, a formal sample size formula was developed by Kodell et al. This theoretical work showed that sample sizes for realistic, yet hypothetical, DRF experiments could be less than a hundred animals and still have sufficient power to detect clinically meaningful DRF values. However, researchers have been slow to use the formula for their DRF experiments, whether from ignorance to its existence or hesitancy to depart from "tried and true" sample sizes. Here, we adapt the sample size formula to better fit usual DRF experiments, and, importantly, we provide real experimental evidence from two independent DRF experiments that sample sizes smaller than what have typically been used can still statistically detect clinically meaningful DRF values. In addition, we update a literature review of DRF experiments which can be used to inform future DRF experiments, provide answers to questions that researchers have asked when considering sample size calculations rather than solely relying on previous experience, whether their own or others', and, in the supplementary material, provide R code implementing the formula, along with several exercises to familiarize the user with the adapted formula.
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Registered trials
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