On the use of nonparametric tests for comparing immunological Reverse Cumulative distribution curves (RCDCs)

Abstract

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 (CIs) 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 paper, we will suggest several widely used rank-based tests to compare the curves in the context of vaccine studies. These rank-based tests allow for comparisons between treatments, for stratified analyses, weighted analyses, and other modifications that make them the alternative of parametric analyses without the normality assumptions. Clinical trial identification: NCT01712984 and NCT01230957.

Keywords: Distribution; Normality assumption; Rank-based test; Survival; Vaccine.