Goal: visualize how FECRT point estimates, interval uncertainty, and analysis-method choice affect resistance classification. Two decisive figures below use only reported values.
Figure 1 β Farm-level FECR with 95% credible intervals vs the 95% susceptibility threshold. Data: six Lithuanian stables, fenbendazole FECRT (n = 10 treated/stable, Bayesian hierarchical model) . Overlap of each CI with the 95% line instantly exposes ambiguous classifications (farms 2, 3, 5, 6).
Figure 2 β Statistical sensitivity: CI width per farm. Interval width ranges from 1.1 pp (farm 1) to 48.8 pp (farm 6), showing why small per-stable samples (n β 10) yield low discriminatory sensitivity. This is the core sensitivity graph: farms with wide intervals cannot distinguish resistance from susceptibility.
Method-comparison layer (planned, when multi-method data are loaded): a BlandβAltman-style or agreement panel comparing classifications from eggCounts vs bayescount (agreement 10/11, Cohen's k = 0.656, revised Kaplan 2023 guidelines) , plus FECRT vs in-vitro EHA/LDA concordance in Danish goats (BZ largely concordant; LEV/IVM discrepant) .
Design notes: one column; shared y-axis 0β105%; dashed reference at 95%; color by classification status; log-scale option for EC50 panels. Uncertainty: the Italian/Argentine study reports only CI bounds (lower 90% CI β22% to 83% in Argentina; 91.9β98.5% low-level resistance in Italy) , so its farms enter the comparison as CI-bound ranges rather than point estimates β a known incompleteness.
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