Comparisons of titer estimation methods for multiplexed pneumococcal opsonophagocytic killing assay

D. Wang and S.-J. Soong

Computational Statistics & Data Analysis, 2008, vol. 52, issue 11, 5022-5032

Abstract: Titer estimation is one of the major components of immunoassay and vaccine development. A multiplexed in vitro opsonization assay (MOPA) is widely accepted to quantitate Streptococcus pneumococcal antibodies to serotype-specific pneumococcal capsular polysaccharide. Titer estimation of vaccine based on OPA is one important component of standardization of OPA, and the selected statistical method is a factor influencing the accuracy and precision of titer estimation. We evaluated five titer estimation methods for pneumococcal OPA in terms of precision and accuracy using three data sets generated by specifically designed experiments with both an eight-dilution and an eleven-dilution design. The bootstrap resampling technique was also used to determine the performance of the estimation. We concluded that the traditional direct method did not perform as well as the other four methods in terms of precision and accuracy of titer estimation. The Spearman-Kärber estimator might be biased upward for OPA titer estimation. The four-parameter logistic model (4PL) method is an alternative choice for OPA titer estimation. The eleven-dilution design provided more information than the eight-dilution design for titer estimation and enhanced precision of estimators. UAB opsotiter, computer software using the statistical language R and Microsoft Excel®, was developed to implement OPA titer estimation.

Date: 2008
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00228-4
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:52:y:2008:i:11:p:5022-5032

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().