Evaluation of vaccine-induced antibody responses: Impact of new technologies.
ABSTRACT Host response to vaccination has historically been evaluated based on a change in antibody titer that compares the post-vaccination titer to the pre-vaccination titer. A four-fold or greater increase in antigen-specific antibody has been interpreted to indicate an increase in antibody production in response to vaccination. New technologies, such as the bead-based assays, provide investigators and clinicians with precise antibody levels (reported as concentration per mL) in ranges below and above those previously available through standard assays such as ELISA. Evaluations of bead assay data to determine host response to vaccination using fold change and absolute change, with a general linear models used to calculate adjusted statistics, present very different pictures of the antibody response when pre-vaccination antibody levels are low. Absolute changes in bead assay values, although not a standard computation, appears to more accurately reflect the host response to vaccination for those individuals with extremely low pre-vaccination antibody levels. Conversely, for these same individuals, fold change may be very high while post-vaccination antibodies do not achieve seroprotective levels. Absolute change provides an alternate method to characterize host response to vaccination, especially when pre-vaccination levels are very low, and may be useful in studies designed to determine associations between host genotypes and response to vaccination.
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ABSTRACT: In many randomized clinical trials with repeated measures of a response variable one anticipates a linear divergence over time in the difference between treatments. This paper explores how to make an efficient choice of analysis based on individual patient summary statistics. With the objective of estimating the mean rate of treatment divergence the simplest choice of summary statistic is the regression coefficient of response on time for each subject (SLOPE). The gains in statistical efficiency imposed by adjusting for the observed pre-treatment levels, or even better the estimated intercepts, are clarified. In the process, we develop the optimal linear summary statistic for any repeated measures design with assumed known covariance structure and shape of true mean treatment difference over time. Statistical power considerations are explored and an example from an asthma trial is used to illustrate the main points.Statistics in Medicine 01/1998; 16(24):2855-72. · 2.04 Impact Factor
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ABSTRACT: The benefit of annually repeated influenza vaccination on antibody formation is still under debate. In this study the effect of annually repeated influenza vaccination on haemagglutination inhibiting (HI) antibody formation in the elderly is investigated. Between 1990 and 1993 healthy young and elderly, both selected by the SENIEUR protocol, were vaccinated consecutively with commercially available influenza vaccines. The elderly had a lower HI antibody response after one vaccination as compared to the young against the A/Taiwan/1/86 (HINI), B/Yamagata/16/88 and B/Panama/45/90 strains. Annually repeated vaccination did not result in a decrease of the HI antibody titres against the A and B vaccine strains in both age groups. Moreover, the elderly had a significantly higher HI titre against the B strains after the second vaccination as compared to the first, resulting in comparable HI titres for young and elderly. Thus, annually repeated vaccination has a beneficial effect on the antibody titre against influenza virus and can contribute to a better antibody-response in the elderly.Vaccine 08/1997; 15(12-13):1323-9. · 3.49 Impact Factor
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ABSTRACT: Many randomized trials involve measuring a continuous outcome - such as pain, body weight or blood pressure - at baseline and after treatment. In this paper, I compare four possibilities for how such trials can be analyzed: post-treatment; change between baseline and post-treatment; percentage change between baseline and post-treatment and analysis of covariance (ANCOVA) with baseline score as a covariate. The statistical power of each method was determined for a hypothetical randomized trial under a range of correlations between baseline and post-treatment scores. ANCOVA has the highest statistical power. Change from baseline has acceptable power when correlation between baseline and post-treatment scores is high;when correlation is low, analyzing only post-treatment scores has reasonable power. Percentage change from baseline has the lowest statistical power and was highly sensitive to changes in variance. Theoretical considerations suggest that percentage change from baseline will also fail to protect from bias in the case of baseline imbalance and will lead to an excess of trials with non-normally distributed outcome data. Percentage change from baseline should not be used in statistical analysis. Trialists wishing to report this statistic should use another method, such as ANCOVA, and convert the results to a percentage change by using mean baseline scores.BMC Medical Research Methodology 02/2001; 1:6. · 2.17 Impact Factor