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Economic analysis of randomized controlled trial data: a framework and feedlot cattle case study
Lucas M Horton1, Dustin L Pendell2, David G Renter1
1Center for Outcomes Research and Epidemiology, and the Department of Diagnostic Medicine and Pathobiology, Kansas State University, Manhattan, KS 66506, USA.
None:
Livestock industry stakeholders rely on research, often randomized controlled trials, to make evidence-based decisions. Economic implications of interventions are often a major deciding factor for adoption by producers. However, economic analyses in beef feedlot trials are infrequently conducted and often suffer from inconsistent methodologies. Gaps in planning, execution, and reporting of economic assessments underscore the need for guidance and standardized approaches in conducting economic evaluations on RCT data. Our objective was to compare and contrast methodologies for assessing costs and benefits associated with livestock health and production trials and to provide scientific guidance, rationale, and recommendations for future conduct of economic analyses on RCT data. Several types of economic analyses are frequently used by agricultural economists, including cash flow budgets, enterprise budgets, gross margin analyses, cost-benefit and -effectiveness analyses, and partial budgets. Partial budgeting emerges as the most pragmatic strategy for RCT data, focusing on the marginal impact of alternative interventions or management strategies, aligning well with RCT objectives. We provided an example of applying a partial budget to an RCT conducted at a commercial beef feedlot using published data. All observed data for relevant animal performance, health, and carcass variables were included, regardless of their original statistical significance. The budget was applied to each experimental unit (pen), with net return as the final outcome, and analyzed statistically using linear mixed models. While simple partial budgets use fixed prices that may not represent economic risk, incorporating statistical analyses at the pen-level accounts for biological variability and error in the estimates. When warranted, other strategies to account for economic risk (e.g., sensitivity analysis, stochastic simulation) can be incorporated within a partial budget framework. To encourage robust and transparent reporting, future research should explicitly state the type of economic assessment, the values and sources of all prices and the timeframe they represent, the methodology used, and how analyses were conducted. By adopting more consistent and transparent economic evaluation methods, researchers can enhance the applicability of RCT findings, ultimately supporting stakeholders in making economically sound decisions.
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