Large sample inference for an assay quality measure used in high-throughput screening
Article first published online: 8 OCT 2010
Copyright © 2010 John Wiley & Sons, Ltd.
Volume 10, Issue 3, pages 227–231, May/June 2011
How to Cite
Majumdar, A. and Stock, D. (2011), Large sample inference for an assay quality measure used in high-throughput screening. Pharmaceut. Statist., 10: 227–231. doi: 10.1002/pst.452
- Issue published online: 16 MAY 2011
- Article first published online: 8 OCT 2010
- high throughput screening;
- confidence interval;
- large sample inference
The Z′ factor, introduced by Zhang et al. (J. Biomol. Screening 1999; 4(10):67–73), is used extensively in drug discovery for evaluating the performance of high-throughput screening (HTS) assays. These assays are time consuming and expensive. Important decisions regarding HTS assay development, validation and quality are often based solely on point estimates of Z′. Although it would be beneficial to have a confidence interval for Z′, it appears that a formal inferential procedure has not yet been proposed. We fill this gap in the literature by deriving a large sample interval estimator for Z′. Simulation studies found that the proposed confidence interval performed well with both independent and moderately correlated data. Our confidence interval is algebraically simple and amenable to spreadsheet programming. The new interval allows researchers to explicitly consider the variability of estimation when making decisions based on Z′. Copyright © 2010 John Wiley & Sons, Ltd.