STATISTICAL METHODS TO ANALYZE ADVERSEEVENTS DATA OF RANDOMIZED CLINICAL TRIALS

摘要

The adverse events data of randomized clinical trials are often analyzed based oneither crude incidence rates or exposure-adjusted incidence rates. These rates do notadequately account for an individual patient's profile of adverse events over the studyperiod 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 validestimates of incidence rates are not likely to be met in practice by adverse eventsdata of clinical trials. A nonparametric approach called the mean cumulative function(MCF) provides a valid statistical inference on recurrent adverse event profiles of drugsin randomized clinical trials. The estimate involves no assumptions about the form ofMCF. To demonstrate the applicability and utility of the MCF approach in clinicaltrial datasets, an adverse event dataset obtained from a clinical trial is analyzed inthis article. As compared to the crude or exposure-adjusted incidence rates of adverseevents, the MCF estimates facilitate more understanding of safety profiles of a drugin a randomized clinical trial.