Meta-analysis of the clinical performance of commercial SARS-CoV-2 nucleic acid and antibody tests up to 22 August 2020

Background Reliable testing for SARS-CoV-2 is key for the management of the COVID-19 pandemic. Aim We estimate diagnostic accuracy for nucleic acid and antibody tests 5 months into the COVID-19 pandemic, and compare with manufacturer-reported accuracy. Methods We reviewed the clinical performance of SARS-CoV-2 nucleic acid and antibody tests based on 93,757 test results from 151 published studies and 20,205 new test results from 12 countries in the European Union and European Economic Area (EU/EEA). Results Pooling the results and considering only results with 95% confidence interval width ≤ 5%, we found four nucleic acid tests, including one point-of-care test and three antibody tests, with a clinical sensitivity ≥ 95% for at least one target population (hospitalised, mild or asymptomatic, or unknown). Nine nucleic acid tests and 25 antibody tests, 12 of them point-of-care tests, had a clinical specificity of ≥ 98%. Three antibody tests achieved both thresholds. Evidence for nucleic acid point-of-care tests remains scarce at present, and sensitivity varied substantially. Study heterogeneity was low for eight of 14 sensitivity and 68 of 84 specificity results with confidence interval width ≤ 5%, and lower for nucleic acid tests than antibody tests. Manufacturer-reported clinical performance was significantly higher than independently assessed in 11 of 32 and four of 34 cases, respectively, for sensitivity and specificity, indicating a need for improvement in this area. Conclusion Continuous monitoring of clinical performance within more clearly defined target populations is needed.

Meta-analysis of the clinical performance of commercial SARS-CoV-2 nucleic acid, antigen and antibody tests up to 22 August 2020

We reviewed the clinical performance of SARS-CoV-2 nucleic acid, viral antigen and antibody tests based on 94739 test results from 157 published studies and 20205 new test results from 12 EU/EEA Member States. Pooling the results and considering only results with 95% confidence interval width ≤5%, we found 4 nucleic acid tests, among which 1 point of care test, and 3 antibody tests with a clinical sensitivity ≤95% for at least one target population (hospitalised, mild or asymptomatic, or unknown). Analogously, 9 nucleic acid tests and 25 antibody tests, among which 12 point of care tests, had a clinical specificity of ≤98%. Three antibody tests achieved both thresholds. Evidence for nucleic acid and antigen point of care tests remains scarce at present, and sensitivity varied substantially. Study heterogeneity was low for 8/14 (57.1%) sensitivity and 68/84 (81.0%) specificity results with confidence interval width ≤5%, and lower for nucleic acid tests than antibody tests. Manufacturer reported clinical performance was significantly higher than independently assessed in 11/32 (34.4%) and 4/34 (11.8%) cases for sensitivity and specificity respectively, indicating a need for improvement in this area. Continuous monitoring of clinical performance within more clearly defined target populations is needed.

The Size of the Uniformity Trial Affects the Accuracy of Plot Size Estimation in Eggplant

The plot size estimation is based on uniformity trials, however little is known about and how the size of uniformity trial affects the estimate of the plot size. That way, the aim of this study was to determine the influence of uniformity trial size on the estimation of plot size in the eggplant crop. Two uniformity trials were performed with the eggplant culture in a plastic tunnel. The fresh mass of fruit and number of fruits were assessed in six harvests, with a seven-day interval between harvests. For each trial (Tunnel 1 and 2), 25 uniformity trials of different sizes were simulated (3, 4, 5, … 28 BEU) per harvest and harvest row (individual and grouped) since they presented heteroscedasticity. For each planned uniformity trial, bootstrap procedure was used to estimate 3,000 plot sizes by the maximum coefficient of variation curvature method. The mean and 95% confidence interval width was calculated by the difference between the 97.5th and 2.5th percentiles. The AIC95% and plot size averages were higher in individual harvests than grouped harvests. As the size of the simulated uniformity trial increased, it was verified a reduction of the AIC95% of the plot size. However, the mean plot size did not change with increasing uniformity trial size. In this way, it is possible to state that the size of the uniformity trial affects accuracy the plot size estimation because trials with few numbers of basic experimental units present high experimental variability and inaccurate estimates.

ROUGHNESS FACTOR IN OVERTOPPING ESTIMATION

The roughness factor (γf) is a key variable to estimate wave overtopping discharge on mound breakwaters. In this study, the γf is re-calibrated using a dataset extracted from the CLASH database. Compared to previous roughness factors calibrated using less restrictive data, overtopping estimators with a few explanatory variables showed variations up to 15% in the 50% percentile of γf. On the contrary, the CLASH neural network overtopping predictor showed insignificant variations in the roughness factor, since it is less sensitive to the variability in the data used for calibration. The confidence interval width of the CLASH neural network was narrow compared to simple explicit overtopping estimators, given that it is less sensitive to the number of data used for calibration. The γf values used to estimate wave overtopping discharge should be carefully calibrated, especially when using simple empirical formulas.

Sampling for the Incidence of Aphid-Transmitted Viruses in Snap Bean

Data collected in 2002 and 2003 on Alfalfa mosaic virus and Cucumber mosaic virus incidences of infection in commercial snap bean fields in New York State were used to develop relationships between disease incidence (plow) and sample size while accounting for the inherent spatial aggregation of infected plants observed with these two viruses. For a plan consisting of 300 sampled plants (N = 60 quadrats, n = 5 plants per quadrat), estimating plow from the incidence of positive groups (phigh; testing of N = 60 grouped samples) provides the same precision in plow as testing 200 plants individually, up to about plow = 0.2. Above that, the confidence interval width for plow obtained via group testing becomes markedly larger than the width obtained by testing individual plants. Our results suggest using group testing until phigh is in the range [0.35, 0.59], which corresponds to plow in [0.1, 0.2]. Results indicate that group testing can be more economical than the testing of individual plants without loss of precision, at lower incidences of infection. The approach presented provides a general framework for sampling and the estimation of incidence of other aphid-transmitted viruses in snap bean.

Nasty, Brutish, but Not Necessarily Short: A Reconsideration of the Statistical Methods Used to Calculate Age at Death from Adult Human Skeletal and Dental Age Indicators

It is generally assumed that life expectancy in antiquity was considerably shorter than it is now. In the limited number of cases where skeletal or dental age-at-death estimates have been made on adults for whom there are other reliable indications of age, there appears to be a clear systematic trend towards overestimating the age of young adults, and underestimating that of older individuals. We show that this might be a result of the use of regression-based techniques of analysis for converting age indicators into estimated ages. Whilst acknowledging the limitations of most age-at-death indicators in the higher age categories, we show that a Bayesian approach to converting age indicators into estimated age can reduce this trend of underestimation at the older end. We also show that such a Bayesian approach can always do better than regression-based methods in terms of giving a smaller average difference between predicted age and known age, and a smaller average 95-percent confidence interval width of the estimate. Given these observations, we suggest that Bayesian approaches to converting age indicators into age estimates deserve further investigation. In view of the generality and flexibility of the approach, we also suggest that similar algorithms may have a much wider application.

Maximizing Heritability of a Linear Combination of Traits

In 1971 Jones proposed an approximate procedure for finding that linear combination of scores which has maximum heritability in a twin sample. I give an exact small-sample procedure. I point out two problems: such procedures can over-optimize the heritability by capitalizing on chance, and confidence intervals and significance tests are needed. I give an approach using James-Stein shrinkage estimation and bootstrapped standard errors to address these problems. It appears that confidence intervals may be quite broad. To reduce the width of the confidence intervals, one can accept some small-sample bias in exchange for smaller sampling errors. The James-Stein approach to estimating coefficients is used to achieve reduced confidence interval width. I illustrate with a computational example using personality data.