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Abstract:
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In the area of pharmaceutical drug safety, one of the primary goals in analysis of adverse events (AEs) is to detect any signal of a difference between the treatment and control groups. Traditionally, Crude Incidence Rate, Chi-square Test or Fisher's Exact Test, and Miettinen & Nurminen are the useful methods in analysis of single AE data depending on what level of importance it belongs to, such as Tier 1, Tier 2, or Tier 3. The occurrence of AEs is complicated. Simple measurement of AE data without enough information about duration, severity, or recurrent events, the estimation and inference could be biased. Moreover, multiple AEs within the same System Organ Class (SOC) are usually correlated with each other. So analysis of single AE over simplifies comparison among treatment arms in drug safety. In this presentation, we would like to propose a new class of distribution-free approaches to address the effects of duration, severity, and recurrence of AE data by using a new measurement within certain specified class.
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