In this webinar, you will learn about:
- Challenges you encounter in survival analysis trial design
- How non-proportional hazards impacts your power and analysis
- Logistical and statistical issues for survival adaptive designs
- nQuery Practical Demonstrations
- Log-Rank Test
- Linear-Rank Tests/MaxCombo
- Group Sequential Log-Rank Test
Duration - 60 minutes
Speaker: Ronan Fitzpatrick, Lead Statistician, Statsols
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Complex Survival Patterns and Impact of Adaptive Design
Survival analysis is one of the most common areas of interest in clinical trials, especially for oncology trials. However, survival analysis is also one of the more complex areas for statistical analysis.
For clinical trial design, this can require complex assumptions regarding accrual, survival and censoring patterns, especially as newer therapeutics such as immunotherapies challenge traditional statistical approaches. In addition, the wider adoption of adaptive designs poses unique challenges in the survival analysis context that require particular attention.
The emergence of newer therapeutic approaches (such as immunotherapies) with more complex survival effects has led to a re-evaluation of the traditional approaches to statistical analysis of survival endpoints. Non-proportional hazard patterns such as delayed effects have spurred interest in methods such as linear-rank tests, MaxCombo procedures and restricted mean survival time (RMST) to allow more accurate inference for these more complex survival scenarios.
In addition, the wider adoption of adaptive designs poses unique challenges for survival analysis trials where practical aspects such as interim timing are far more challenging while issues such as potential bias due to the chosen adaptation strategy or the impact of non-proportional hazards on adaptive design assumptions require careful evaluation.
In this webinar, we provide an overview of the challenges encountered when designing survival analysis clinical trials, look at the recent interest in the effect of non-proportional hazards and explore the specific issues faced when applying adaptive designs in the survival analysis context.
About nQuery
nQuery helps make your clinical trials faster, less costly and more successful. It is an end-to-end platform covering Frequentist, Bayesian, and Adaptive designs with 1000+ sample size procedures.
nQuery Solutions
Sample Size & Power Calculations
Calculate for a variety of frequentist and Bayesian designs
Adaptive Design
Design and analyze a wide range of adaptive designs
Milestone Prediction
Predict interim analysis timing or study length
Randomization Lists
Generate and save lists for your trial design
