Statistical Methods to Analyze Adverse Events Data of Randomized Clinical Trials

Abstract

The adverse events data of randomized clinical trials are often analyzed based on either crude incidence rates or exposure-adjusted incidence rates. These rates do not adequately account for an individual patient's profile of adverse events over the study period when an individual may remain in the trial after experiencing one or more events (i.e., occurrence of multiple events of the same kind or different kinds). Moreover, the required statistical assumptions (e.g., constant hazard rate over time) for valid estimates of incidence rates are not likely to be met in practice by adverse events data of clinical trials. A nonparametric approach called the mean cumulative function (MCF) provides a valid statistical inference on recurrent adverse event profiles of drugs in randomized clinical trials. The estimate involves no assumptions about the form of MCF. To demonstrate the applicability and utility of the MCF approach in clinical trial datasets, an adverse event dataset obtained from a clinical trial is analyzed in this article. As compared to the crude or exposure-adjusted incidence rates of adverse events, the MCF estimates facilitate more understanding of safety profiles of a drug in a randomized clinical trial.

Key Words:

• Adverse events
• AEs
• Gender
• ISS
• MCF

ACKNOWLEDGMENT

The author expresses his gratitude to two referees for their valuable comments and suggestions on the earlier version of this article.

Notes

*Males: 141 (drug group), 119 (placebo group); females: 485 (drug group), 481 (placebo group)

Crude rate =n/N, EAIR =n/T (expected number of patients with event per 1 patient—12 weeks). The 95% confidence interval for the difference Δ between study drug vs. placebo was based on the normal approximation (Wald's method).

^¥In person 12 weeks.