Innovative Data Analysis of Adverse Event in Predicting Clinical Outcomes in Cancer

Music City Center 

While significant effort has been made in collecting adverse event (AE) in cancer clinical trial, utilization of AE data is limited and suboptimal. The key challenge is in complexity of AE data. When a patient experiences AEs, the event is not just Yes or No. Severity of the event does matter, as well as event duration. Moreover, attribution of the event to treatment relatedness is another factor for consideration. Furthermore, each unique AE likely occur only to few subjects, resulting in spareness issue. Because of high degree of difficulty, most AE reports in medical publication are descriptive simply based on proportion and frequency. We develop a novel data analysis strategy to decompose the multi-faceted AE for downstream analysis. The approach utilizes event severity and treatment relatedness to form multiple subgroups. Each subgroup assesses different metrics, such as occurrence, frequency, and duration to capture a diverse range of AE contents and to unlock the potential for clinical application. We demonstrate in a colorectal cancer (CRC) study the AE-derived metrics could identify subset of patients for treatment benefit and highlight the potential clinical utility.

adverse event

cancer

data analysis 

Biopharmaceutical Section