Fig 1.
World Health Organization process to establish new target product profiles [8].
PP = product profile.
Table 1.
Selected features of the target product profile of a diagnostic to map onchocerciasis in low prevalence areas.
Criteria that are generally expected of a rapid diagnostic test for a NTD are not included in this summary. For all 41 criteria, please see the original publication [5]. The word “biomarker” includes an antibody.
Table 2.
Selected features of the target product profile of a diagnostic to support an MDA stopping decision.
Criteria that are generally expected of a rapid diagnostic test for a NTD are not included in this summary. For all 41 criteria, please see the original publication [5]. The word “biomarker” includes an antibody.
Table 3.
Effect of specificity on the Type II error of a cluster survey involving 20 villages, and 50 people/village.
No sensitivity assumptions were made. The critical cutoff was ≥ 2 positive tests in at least 1 village. The calculations were made for a true prevalence of 0%. At least 99.8% specificity is required to reach a Type II error of < 10%.
Table 4.
Effect of sensitivity on the Type I error of a cluster survey involving 20 villages, and 50 people/participant.
A specificity of 99.8% was assumed. The critical cutoff was ≥ 2 positive tests in at least 1 village. The calculations were made for a true prevalence of 2%. At least 60% sensitivity is required to reach a Type I error of < 5%.
Table 5.
Effect of specificity on the Type II error of a 30-cluster survey, where treatment decisions are based on the average prevalence of the entire survey area.
No sensitivity assumptions were made. The critical cutoff was ≥ 19 positive tests out of a sample size of 3000. The calculations were made for a true prevalence of 0%. At least 99.6% specificity is required to have a Type II error of < 10%.
Table 6.
Effect of sensitivity on the Type I error of a 30-cluster survey involving 3000 children, where treatment decisions are based on the average prevalence of the entire survey area.
A specificity of 99.8% was assumed. The critical cutoff was ≥ 19 positive tests. The calculations were made for a true prevalence of 1%. At least 50% sensitivity is required to have a Type I error of < 5%.
Table 7.
Effect of specificity on the Type II error when treatment decisions are based on the cluster-specific results, assuming 30 clusters are surveyed and 100 children/cluster.
No sensitivity assumptions were made. The critical cutoff was ≥ 3 positive tests in at least 1 village. The calculations were made for a true prevalence of 0%. At least 99.7% specificity is required to have a Type II error of < 10%.
Table 8.
Effect of sensitivity on the Type I error when treatment decisions are based on the cluster-specific results, assuming 30 clusters are surveyed and 100 children/cluster.
A specificity of 99.8% was assumed. The critical cutoff was ≥ 2 positive tests in at least 1 village. The calculations were made for a true prevalence of 1%. At least 89% sensitivity is required to have a Type I error of < 5%.