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PubMed2000

Importance of inoculum size and sampling effect in rapid antigen detection for diagnosis of Streptococcus pyogenes pharyngitis.

Current recommendations suggest that negative rapid Streptococcus pyogenes antigen tests be backed up with a culture, reflecting evidence that culture may have a higher sensitivity and also that testing of a second swab may yield a different (i.e., a positive) result because of variation in sample size or distribution. If the latter is common, the sensitivities of current antigen detection tests might be improved by simply increasing the amount of sample tested. The present study assessed the effect of antigen testing of two swabs extracted together compared to independent testing of each swab extracted separately for children with clinical pharyngitis. S. pyogenes grew from one or both swabs for 198 (37%) of 537 children. The combined culture was significantly (P < 0.05) more sensitive than culture of either swab alone. Compared to combined culture, antigen testing of two swabs extracted and tested together was significantly more sensitive than two single swab extractions (94.1 versus 80%; P = 0.03); however, the specificity was decreased (81.5 versus 89.8 to 92.7%; P < 0.05). This study suggests that sample size and/or uneven sample distribution may have influenced the apparent sensitivities of prior studies that compared antigen tests to a single plate culture. A strategy, such as the one used in the present study, that increases the sample size available for antigen testing (i.e., extraction of samples from both swabs) may improve detection rates to a level that will better approximate true disease status and obviate the need for backup cultures if specificity can be improved.

Adolescent↗
PubMed1994

Dispersion statistics and sample size estimates for house fly (Diptera: Muscidae) larvae and Macrocheles muscaedomesticae (Acari: Macrochelidae) in poultry manure.

The precision of the density estimate for a given population depends upon the population's density and degree of clumping and upon sampling characteristics, such as the number and surface area of the quadrats. The parameters k of the negative binomial distribution and b of Taylor's power law (s2 = a mean b) were determined for third instars of the muscaedomesticae (Scopoli), which were sampled by random quadrats at two shallow-pit, caged-layer poultry houses. Most calculated values of k were < 1, but agreement with the negative binomial distribution was found only in 4, 18, and 14 of the 27 weekly samples for house fly larvae, female mites, and male mites, respectively. Taylor's power law provided the best fit for the distribution (P < 0.01 for all regression coefficients) with s2 = 9.08 mean 1.83 (r2 = 0.97) for third-instar house flies and s2 = 4.65 mean 1.76 (r2 = 0.97) for both sexes of M. muscaedomesticae. Two ecological processes, social clustering and environmental heterogeneity, were hypothesized as the mechanisms determining aggregation of house fly larvae and the macrochelid mites. Regardless of the biological model influencing their distributions, Taylor's power law provided a reliable index for measuring their degree of aggregation. Reliability defined by the coefficient of variability and Taylor's regression coefficients was used to calculate optimum sample size-density estimates for each species with coefficients of variability of 10, 15, 20, and 25%.

Animals↗
PubMed2004

Two-stage designs for gene-disease association studies with sample size constraints.

Gene-disease association studies based on case-control designs may often be used to identify candidate polymorphisms (markers) conferring disease risk. If a large number of markers are studied, genotyping all markers on all samples is inefficient in resource utilization. Here, we propose an alternative two-stage method to identify disease-susceptibility markers. In the first stage all markers are evaluated on a fraction of the available subjects. The most promising markers are then evaluated on the remaining individuals in Stage 2. This approach can be cost effective since markers unlikely to be associated with the disease can be eliminated in the first stage. Using simulations we show that, when the markers are independent and when they are correlated, the two-stage approach provides a substantial reduction in the total number of marker evaluations for a minimal loss of power. The power of the two-stage approach is evaluated when a single marker is associated with the disease, and in the presence of multiple disease-susceptibility markers. As a general guideline, the simulations over a wide range of parametric configurations indicate that evaluating all the markers on 50% of the individuals in Stage 1 and evaluating the most promising 10% of the markers on the remaining individuals in Stage 2 provides near-optimal power while resulting in a 45% decrease in the total number of marker evaluations.

Biometry↗