Statistical methods for the analysis of clinical trials data containing many zeros: An application in vaccine development.

ABSTRACT

In recent years, many vaccines have been developed for the prevention of a variety of diseases. Many of these vaccines, like the one for herpes zoster, are supposed to act in a multilevel way. Ideally, they completely prevent expression of the virus, but failing that they help to reduce the severity of the disease. A simple approach to analyze these data is the so-called burden-of-illness test. The method assigns a score, say W, equal to 0 for the uninfected and a post-infection outcome X > 0 for the infected individuals. One of the limitations of this test is the potential low power when the vaccine efficacy is close to 0. To overcome this limitation, we propose a Fisher adjusted test where we combine a statistic for infection with a statistic for post-infection outcome adjusted for selection bias. The advantages and disadvantages of different methods proposed in the literature are discussed. We compared the methods via simulations in herpes zoster, HIV, and malaria vaccine trial settings. In addition, we applied these methods to published data on HIV vaccine. The paper ends with some recommendations and conclusions.