Survival analysis is one of the most common statistical approaches used in clinical trials, especially in clinical areas such as oncology.
It is also one of the most complex and flexible statistical areas with this increasing in recent years due to the emergence of innovative treatments such as immunotherapies. This complexity affects every part of trial design and analysis including power analysis and sample size determination.
In this webinar we look at the key concept for power analysis for survival trials and some of the challenges that a researcher can face including accrual, dropout and more recent considerations such as non-proportional hazards.
As survival analysis is tied to the number of events that occur, power analysis will usually consist of both calculating the required number of events and the sample size expected to achieve that number of events. This means there is significant flexibility required to deal with issues such as varying accrual, follow-up, hazard rate and dropout patterns even within the “standard” two independent group log-rank scenario.
One area of significant interest in recent years has been non-proportional hazards, where the hazard ratio varies over time, especially in the context of immunotherapies which commonly have a “delayed effect”. Multiple proposals have emerged for the analysis of these complex survival datasets with the MaxCombo test being a leading tool for analyzing complex outcomes such as delayed effects.
In addition to these considerations there has also been significant work to extend to other study designs such as one group, paired and factorial analyses.
In this webinar, we explore the important concept required for power calculations for survival analysis. We will also examine how to deal with the most common concerns and issues which can arise including non-proportional hazards.
Duration: 60 minutes
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