Statistical Methodology for Cancer Immunology Trial Design

Organizers: Jianrong Wu (UKY)
Chair: Herbert Pang (Genentech)
Vice Chair: Jianrong Wu (UKY)

Speakers:
Jianrong Wu (UKY)
Xue Ding (Seagen)
Jing Wei (UKY)

Abstracts:

Title: Cancer Immunotherapy Trial Design with Long-Term Survivors
Speaker: Jianrong Wu (UKY)

Cancer immunotherapy often reflects the mixture of improvement in short-term risk reduction and long-term survival. In this scenario, the hazard functions between two groups will ultimately cross over. Thus, conventional assumption of proportional hazards is violated and study design using standard log-rank test is inefficient. In this talk, we propose a change sign weighted log-rank test for the trial design. We derived a sample size formula for the weighted log-rank test, which can be used for designing cancer immunotherapy trials to detect both short-term risk reduction and long-term survival. Simulation studies are conducted to compare the efficiency between the standard log-rank test and the weighted long-rank test.

Title: Designing Cancer Immunotherapy Trials with Delayed Treatment Effect Using Maximin Efficiency Robust Statistics
Speaker: Xue Ding (Seagen)

The indirect mechanism of action of immunotherapy causes a delayed treatment effect, producing delayed separation of survival curves between the treatment groups, and violates the proportional hazards assumption. Therefore using the log-rank test in immunotherapy trial design could result in a severe loss efficiency. Recently, Ye and Yu proposed use of a maximin efficiency robust test (MERT) for the trial design. For simplicity, we propose use of an approximated maximin test which is the sum of the log-rank test for the full data set and the log-rank test for the data beyond the lag time point.  The proposed test fully uses the trial data and is more efficient than the log-rank test when lag exits with relatively little efficiency loss when no lag exists. The sample size formula for the proposed test is derived.  Simulations are conducted to compare the performance of the proposed test to the existing tests.

Title: Cancer Immunotherapy Trial Design with Cure Rate and Delayed Treatment Effect
Speaker: Jing Wei (UKY)

Cancer immunotherapy trials have two special features: a delayed treatment effect and a cure rate. Both features violate the proportional hazards model assumption and ignoring either one of the two features in an immunotherapy trial design will result in substantial loss of statistical power.  To properly design immunotherapy trials, we proposed a piecewise proportional hazards cure rate model  to incorporate both delayed treatment effect and cure rate into the trial design consideration. A sample size formula is derived for a weighted log-rank test under a fixed alternative hypothesis. The accuracy of sample size calculation using the new formula is assessed and compared with the existing methods via simulation studies.