Although various studies have provided novel insights into how to best design, analyze and interpret a fecal egg count reduction test (FECRT), it is still not straightforward to provide guidance that allows improving both the standardization and the analytical performance of the FECRT across a variety of both animal and nematode species. For example, it has been suggested to recommend a minimum number of eggs to be counted under the microscope (not eggs per gram of feces), but we lack the evidence to recommend any number of eggs that would allow a reliable assessment of drug efficacy. Other aspects that need further research are the methodology of calculating uncertainty intervals (UIs; confidence intervals in case of frequentist methods and credible intervals in case of Bayesian methods) and the criteria of classifying drug efficacy into 'normal', 'suspected' and 'reduced'. The aim of this study is to provide complementary insights into the current knowledge, and to ultimately provide guidance in the development of new standardized guidelines for the FECRT. First, data were generated using a simulation in which the 'true' drug efficacy (TDE) was evaluated by the FECRT under varying scenarios of sample size, analytic sensitivity of the diagnostic technique, and level of both intensity and aggregation of egg excretion. Second, the obtained data were analyzed with the aim (i) to verify which classification criteria allow for reliable detection of reduced drug efficacy, (ii) to identify the UI methodology that yields the most reliable assessment of drug efficacy (coverage of TDE) and detection of reduced drug efficacy, and (iii) to determine the required sample size and number of eggs counted under the microscope that optimizes the detection of reduced efficacy. Our results confirm that the currently recommended criteria for classifying drug efficacy are the most appropriate. Additionally, the UI methodologies we tested varied in coverage and ability to detect reduced drug efficacy, thus a combination of UI methodologies is recommended to assess the uncertainty across all scenarios of drug efficacy estimates. Finally, based on our model estimates we were able to determine the required number of eggs to count for each sample size, enabling investigators to optimize the probability of correctly classifying a theoretical TDE while minimizing both financial and technical resources.
This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 April 2023.
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#10,352,096
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#987
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Outputs of similar age from Veterinary Parasitology
#7
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Altmetric has tracked 34,492,055 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 4,506 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done well, scoring higher than 77% of its peers.
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We're also able to compare this research output to 80 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.
