FECRT

FECRT provides statistical methods to calculate prospective sample sizes and classify efficacy results from Faecal Egg Count Reduction Tests to detect and quantify anthelmintic resistance in ruminants, horses, and swine.

Key Features:

• Standardized Experimental Design: Implements standardized, statistically rigorous design principles for FECRT experiments to support reliable resistance detection at the farm level.
• Sample Size Calculation: Calculates prospective sample sizes using statistical power and typical values for expected pre-treatment and post-treatment variability in egg counts and within-animal correlation.
• Statistical Tests: Employs two one-sided tests—a one-sided inferiority test to assess resistance and a one-sided non-inferiority test for susceptibility—and combines their results to classify outcomes as resistant, susceptible, or inconclusive.
• Confidence Interval Recommendation: Recommends using a 90% confidence interval to maintain a Type I error rate of 5%, reducing required sample size relative to a 95% interval.
• Parameter Estimates: Provides estimates of key parameters (expected variability in egg counts and within-animal correlation) tailored to specific host–parasite systems derived from re-examination of published datasets and other sources.
• Illustrative Example: Includes a worked example of sample size calculations for a hypothetical FECRT using ivermectin in cattle.

Scientific Applications:

• Drug efficacy classification: Produces objective classifications of anthelmintic efficacy (resistant, susceptible, inconclusive) based on statistical testing.
• Study design optimization: Supplies tailored sample size recommendations to improve the reliability and efficiency of FECRT-based resistance detection studies.
• Guidance for resistance management: Informs design choices in FECRTs to support monitoring and management strategies for anthelmintic resistance in livestock.

Methodology:

Computes sample sizes using statistical power with supplied typical values for pre- and post-treatment egg count variability and within-animal correlation; applies two one-sided tests (inferiority and non-inferiority) and uses 90% confidence intervals to control a 5% Type I error; derives parameter estimates from re-examination of published datasets and other sources.