Statistical Methods for Standard Membrane-Feeding Assays to Measure Transmission Blocking or Reducing Activity in Malaria

Bruce J. Swihart, Michael P. Fay and Kazutoyo Miura

Journal of the American Statistical Association, 2018, vol. 113, issue 522, 534-545

Abstract: Transmission blocking vaccines for malaria are not designed to directly protect vaccinated people from malaria disease, but to reduce the probability of infecting other people by interfering with the growth of the malaria parasite in mosquitoes. Standard membrane-feeding assays compare the growth of parasites in mosquitoes from a test sample (using antibodies from a vaccinated person) compared to a control sample. There is debate about whether to estimate the transmission reducing activity (TRA) which compares the mean number of parasites between test and control samples, or transmission blocking activity (TBA) which compares the proportion of infected mosquitoes. TBA appears biologically more important since each mosquito with any parasites is potentially infective; however, TBA is less reproducible and may be an overly strict criterion for screening vaccine candidates. Through a statistical model, we show that the TBA estimand depends on μc, the mean number of parasites in the control mosquitoes, a parameter not easily experimentally controlled. We develop a standardized TBA estimator based on the model and a given target value for μc which has better mean squared error than alternative methods. We discuss types of statistical inference needed for using these assays for vaccine development. Supplementary materials for this article are available online.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2017.1356313 (text/html)
Access to full text is restricted to subscribers.

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:taf:jnlasa:v:113:y:2018:i:522:p:534-545

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2017.1356313

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().