Relationship between haemagglutination-inhibiting antibody titres and clinical protection against influenza: development and application of a bayesian random-effects model

<p>Abstract</p> <p>Background</p> <p>Antibodies directed against haemagglutinin, measured by the haemagglutination inhibition (HI) assay are essential to protective immunity against influenza infection. An HI titre of 1:40 is generally accepted to correspond to a 50% reduction in the risk of contracting influenza in a susceptible population, but limited attempts have been made to further quantify the association between HI titre and protective efficacy.</p> <p>Methods</p> <p>We present a model, using a meta-analytical approach, that estimates the level of clinical protection against influenza at any HI titre level. Source data were derived from a systematic literature review that identified 15 studies, representing a total of 5899 adult subjects and 1304 influenza cases with interval-censored information on HI titre. The parameters of the relationship between HI titre and clinical protection were estimated using Bayesian inference with a consideration of random effects and censorship in the available information.</p> <p>Results</p> <p>A significant and positive relationship between HI titre and clinical protection against influenza was observed in all tested models. This relationship was found to be similar irrespective of the type of viral strain (A or B) and the vaccination status of the individuals.</p> <p>Conclusion</p> <p>Although limitations in the data used should not be overlooked, the relationship derived in this analysis provides a means to predict the efficacy of inactivated influenza vaccines when only immunogenicity data are available. This relationship can also be useful for comparing the efficacy of different influenza vaccines based on their immunological profile.</p

Robust Biomarkers: Methodologically Tracking Causal Processes in Alzheimer’s Measurement

In biomedical measurement, biomarkers are used to achieve reliable prediction of, and useful causal information about patient outcomes while minimizing complexity of measurement, resources, and invasiveness. A biomarker is an assayable metric that discloses the status of a biological process of interest, be it normative, pathophysiological, or in response to intervention. The greatest utility from biomarkers comes from their ability to help clinicians (and researchers) make and evaluate clinical decisions. In this paper we discuss a specific methodological use of clinical biomarkers in pharmacological measurement: Some biomarkers, called ‘surrogate markers’, are used to substitute for a clinically meaningful endpoint corresponding to events and their penultimate risk factors. We confront the reliability of clinical biomarkers that are used to gather information about clinically meaningful endpoints. Our aim is to present a systematic methodology for assessing the reliability of multiple surrogate markers (and biomarkers in general). To do this we draw upon the robustness analysis literature in the philosophy of science and the empirical use of clinical biomarkers. After introducing robustness analysis we present two problems with biomarkers in relation to reliability. Next, we propose an intervention-based robustness methodology for organizing the reliability of biomarkers in general. We propose three relevant conditions for a robust methodology for biomarkers: (R1) Intervention-based demonstration of partial independence of modes: In biomarkers partial independence can be demonstrated through exogenous interventions that modify a process some number of “steps” removed from each of the markers. (R2) Comparison of diverging and converging results across biomarkers: By systematically comparing partially-independent biomarkers we can track under what conditions markers fail to converge in results, and under which conditions they successfully converge. (R3) Information within the context of theory: Through a systematic cross-comparison of the markers we can make causal conclusions as well as eliminate competing theories. We apply our robust methodology to currently developing Alzheimer’s research to show its usefulness for making causal conclusions

Competitive Tendering In The Netherlands: Central Planning Or Functional Specifications?

Institute of Transport and Logistics Studies. Faculty of Economics and Business. The University of Sydne