Safety analysis of new medications in clinical trials: a simulation study to assess the differences between cause-specific and subdistribution frameworks in the presence of competing events

BMC Med Res Methodol. 2023 Jul 13;23(1):168. doi: 10.1186/s12874-023-01985-7.

Abstract

Safety is an essential part of the evaluation of new medications and competing risks that occur in most clinical trials are a well identified challenge in the analysis of adverse events. Two statistical frameworks exist to consider competing risks: the cause-specific and the subdistribution framework. To date, the application of the cause-specific framework is the standard practice in safety analyses. Here we analyze how the safety analysis results of new medications would be affected if instead of the cause-specific the subdistribution framework was chosen. We conducted a simulation study with 600 participants, equally allocated to verum and control groups and a 30 months follow-up period. Simulated trials were analyzed for safety in a competing risk (death) setting using both the cause-specific and subdistribution frameworks. Results show that comparing safety profiles in a subdistribution setting is always more pessimistic than in a cause-specific setting. For the group with the longest survival and a safety advantage in a cause-specific setting, the advantage either disappeared or a disadvantage was found in the subdistribution analysis setting. These observations are not contradictory but show different perspectives. To evaluate the safety of a new medication over its comparator, one needs to understand the origin of both the risks and the benefits associated with each therapy. These requirements are best met with a cause-specific framework. The subdistribution framework seems better suited for clinical prediction, and therefore more relevant for providers or payers, for example.

Keywords: Cause-specific versus subdistribution hazard ratio; Competing risks; Drug development; Safety analysis; Simulation study.

MeSH terms

  • Clinical Trials as Topic
  • Computer Simulation*
  • Humans
  • Proportional Hazards Models