In this free webinar, we explore some common challenges faced when designing clinical trials with survival endpoints, and the solutions for these problems.
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We explore common challenges faced when designing clinical trials with survival endpoints.
Survival analysis is a very important and common statistical area of interest for oncology trials. Due to the complex nature of these trials, some unique challenges are faced.
Many of these issues are encountered when the proportional hazards assumption is not met and methods like the Log-Rank test and Cox Proportional Hazards model can not be directly applied.
Treatments that exhibit delayed effects or other non-proportional effect patterns can be analysed using weighted rank tests and concepts such as relative time. Interaction effects can also be identified and accounted for in stratified designs.
Clinical prediction models are another helpful tool to enable healthcare providers to make better risk evaluations for diagnostic or prognostic purposes. However, the quality of these models is often under scrutiny and it is important to ensure that these models meet certain metrics for quality and robustness.
In this webinar, we will examine methods to analyse survival trials that encounter non-proportional hazards, as well assessing the challenges in constructing a prediction model with survival endpoints.