Abstract:
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Reverse Cumulative Distribution Curves (RCDCs) have proven to be a useful tool in summarizing immune response profiles in vaccine studies since their introduction by Reed, Meade, and Steinhoff (RMS) (1995). They are able to display virtually all of the treatment data and characterize summary statistics such as means or even their confidence intervals that might be obscure. RMS mentioned their similarity to survival curves often used to summarize time to event data which are usually not normally distributed. The RCDCs, while intuitively pleasing and useful, contain important properties which allow for more powerful statistical applications. In this presentation, we will suggest several rank-based tests to compare the curves in the context of vaccine studies. These rank-based tests allow for comparisons of treatments, for stratified analyses, weighted analyses, and other modifications that make them the alternative of parametric analyses without the normality assumptions. Some of the methods are relatively assumption free and still very powerful. In fact, in the presence of deviations from assumptions, they are more powerful than the normal based methods.
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