Mini‐pool screening by nucleic acid testing for hepatitis B virus, hepatitis C virus, and HIV: preliminary results

Cardoso, M.; Koerner, K.; Kubanek, B

doi:10.1046/j.1537-2995.1998.381098440853.x

Cited by 66 publications

(44 citation statements)

References 5 publications

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“…Group testing has a rich history dating back to the works of Watson [1] and Dorfman [2]. Today, group testing is exploited in a variety of areas including epidemiology, genetics, blood-bank screening, drug discovery, biology, and plant pathology [3-9]. Depending on the application, group testing may be used simply to estimate the proportion of positives or to classify each individual item as positive or negative.…”

Section: Introductionmentioning

confidence: 99%

Bias, efficiency, and agreement for group-testing regression models

Bilder

Tebbs

2009

Journal of Statistical Computation and Simulation

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Group testing involves pooling individual items together and testing them simultaneously for a rare binary trait. Whether the goal is to estimate the prevalence of the trait or to identify those individuals that possess it, group testing can provide substantial benefits when compared to testing subjects individually. Recently, group-testing regression models have been proposed as a way to incorporate covariates when estimating trait prevalence. In this paper, we examine these models by comparing fits obtained from individual and group testing samples. Relative bias and efficiency measures are used to assess the accuracy and precision of the resulting estimates using different grouping strategies. We also investigate the agreement of individual and group-testing regression estimates for various grouping strategies and the effects of group size selection. Depending on how groups are formed, our results show that group-testing regression models can perform very well when compared to the analogous models based on individual observations. However, different grouping strategies can provide very different results in finite samples.

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Section: Introductionmentioning

confidence: 99%

Bias, efficiency, and agreement for group-testing regression models

Bilder

Tebbs

2009

Journal of Statistical Computation and Simulation

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“…The FDA requires a 95‐percent detection limit of at least 100 copies per mL for both HCV and HIV NAT blood screening assays and a sensitivity of at least 5000 copies per mL per donation when MP‐NAT is performed. Anticipating these developments, the blood transfusion organizations have introduced or are in the process of implementing MP‐NAT of blood donations for the presence of HCV RNA, HIV RNA, and HBV DNA 1‐5 . With the introduction of NAT, there was an urgent need for standardization to allow interlaboratory comparison of NAT assays.…”

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confidence: 99%

Sensitivity of HCV RNA and HIV RNA blood screening assays

Lelie¹,

Drimmelen²,

Cuypers³

et al. 2002

Transfusion

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“…They claim that this low rate is due to single‐donation testing and that pool PCR testing, in contrast, results in a contamination rate between 3.5 and 5.0 percent. For reference, they cite studies by our group 2 and by Cardoso et al 3 In fact, however, the contamination rate of our HCV pool PCR as described in Roth et al 2 was only 0.4 percent. Although not explicitly demonstrated, the HCV‐specific false‐positive data may be easily deduced from the figure, which shows the numbers of PCR‐confirmed and ‐false‐positive values for each virus over time (Fig.…”

mentioning

confidence: 77%