mortality, power calculation, sample size, swine


Mortality within livestock production has a substantial impact on economic sustain­ability of enterprises. The livestock industry has a unique opportunity to maximize animal welfare and production efficiency by minimizing morbidity and mortality. To do so, investigators must appropriately balance the utilization of limited resources with the quantity necessary for robust research. The objective of the study was to illustrate the use of readily implementable statistical models to determine sample size neces­sary to detect statistically significant differences between groups with varying levels of mortality. To this end, a series of examples were created where the unit to which a treatment is independently applied (experimental unit, EU) is either half of a 1,200 pig barn (group-level) or an individual pen (pen-level, 25 pigs per pen, 48 total pens) within a 1,200 pig barn. These examples and corresponding models can be readily adapted and implemented using SAS software to meet individual needs. Model inputs include the number of pigs, barns, and pens when appropriate, and model output is the calculated statistical power. When the EU is half-barn and mortality is measured on a group-level, 7 barns would be necessary to detect a 1 percentage unit difference in mortality (2% vs. 1% for two groups, respectively). As the observed difference in mortality between groups increases, the number of barns needed to detect the respec­tive difference decreases and vice versa. When the EU is the individual pen containing 25 pigs, and mortality is measured on the pen level, a total of 240 pens or 5 barns would be necessary to detect the same 1 percentage unit reduction in mortality from 2% to 1%. Comparing the results derived from group or pen-level mortality, a relatively close number of pigs is necessary to detect differences. For example, if comparing group mortalities of 3% vs. 4%, the number of barns necessary for group-level mortality is 11 and for pen-level mortality is 9. The models currently proposed incorporate appropriate design structure features for a series of different study designs and assume that observed mortality follows a binomial distribution. Performing proper sample size calculations prior to initiation of research trials is critical to determine the necessary sample size to reasonably expect to see statistical differences. It is very important to recognize that proper use of this information requires background knowledge regarding statistical analysis including understanding of EU, observational unit, and design features such as blocking, subsampling, and other design features. When these assumptions change based on the experiment being planned, appropriate modifications must be made to the models. It is always recommended to engage a statistician in the early stages of experimental design to ensure all facets of the proposed experiment are appropriately accounted for and to reduce the risk of making inaccurate production decisions. Lastly, it is not recommended that these models be used in a manner that assumes one size fits all. Rather, we provide the information as a foundation from which the reader can use appropriate content expertise and statistical principles to assist with designing experi­ments.

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Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.