Comparison of three alternative methods for analysis of equine Faecal Egg Count Reduction Test data

TY - JOUR

T1 - Comparison of three alternative methods for analysis of equine Faecal Egg Count Reduction Test data

AU - Denwood, Matt

AU - Reid, S W J

AU - Love, S

AU - Nielsen, M K

AU - Matthews, L

AU - McKendrick, I J

AU - Innocent, G T

PY - 2010/3/1

Y1 - 2010/3/1

N2 - The Faecal Egg Count Reduction Test (FECRT) is the most widely used method of assessing the efficacy of anthelmintics, and is the only in vivo technique currently approved for use with horses. Equine Faecal Egg Count (FEC) data are frequently characterised by a low mean, high variability, small sample size and frequent zero count observations. Accurate analysis of the data therefore depends on the use of an appropriate statistical technique. Analyses of simulated FECRT data by methods based on calculation of the empirical mean and variance, non-parametric bootstrapping, and Markov chain Monte Carlo (MCMC) are compared. The MCMC method consistently outperformed the other methods, independently of the distribution from which the data were generated. Bootstrapping produced notional 95% confidence intervals containing the true parameter as little as 40% of the time with sample sizes of less than 50. Analysis of equine FECRT data yielded inconclusive results in 53 of 63 (84%) datasets, suggesting that the routine use of prior sample size calculations should be adopted to ensure sufficient data are collected. The authors conclude that computationally intensive parametric methods such as MCMC be used for analysis of FECRT data with sample sizes of less than 50, in order to avoid erroneous inference about the true efficacy of anthelmintics in the field.

AB - The Faecal Egg Count Reduction Test (FECRT) is the most widely used method of assessing the efficacy of anthelmintics, and is the only in vivo technique currently approved for use with horses. Equine Faecal Egg Count (FEC) data are frequently characterised by a low mean, high variability, small sample size and frequent zero count observations. Accurate analysis of the data therefore depends on the use of an appropriate statistical technique. Analyses of simulated FECRT data by methods based on calculation of the empirical mean and variance, non-parametric bootstrapping, and Markov chain Monte Carlo (MCMC) are compared. The MCMC method consistently outperformed the other methods, independently of the distribution from which the data were generated. Bootstrapping produced notional 95% confidence intervals containing the true parameter as little as 40% of the time with sample sizes of less than 50. Analysis of equine FECRT data yielded inconclusive results in 53 of 63 (84%) datasets, suggesting that the routine use of prior sample size calculations should be adopted to ensure sufficient data are collected. The authors conclude that computationally intensive parametric methods such as MCMC be used for analysis of FECRT data with sample sizes of less than 50, in order to avoid erroneous inference about the true efficacy of anthelmintics in the field.

KW - Animals

KW - Anthelmintics

KW - Feces

KW - Helminthiasis, Animal

KW - Horse Diseases

KW - Horses

KW - Netherlands

KW - Parasite Egg Count

U2 - 10.1016/j.prevetmed.2009.11.009

DO - 10.1016/j.prevetmed.2009.11.009

M3 - Journal article

C2 - 19962203

SN - 0167-5877

VL - 93

SP - 316

EP - 323

JO - Preventive Veterinary Medicine

JF - Preventive Veterinary Medicine

IS - 4

ER -