Cancer immunotherapy trial design with random delayed treatment effect and cure rate

2021 ◽  
Author(s):  
Jianrong Wu ◽  
Jing Wei
Keyword(s):  
Cancer Immunotherapy ◽  
Treatment Effect ◽  
Trial Design ◽  
Cure Rate ◽  
Delayed Treatment ◽  
Immunotherapy Trial

Cancer immunotherapy trial design with cure rate and delayed treatment effect

2019 ◽  
Vol 39 (6) ◽  
pp. 698-708 ◽  
Author(s):  
Jing Wei ◽  
Jianrong Wu
Keyword(s):  
Cancer Immunotherapy ◽  
Treatment Effect ◽  
Trial Design ◽  
Cure Rate ◽  
Delayed Treatment ◽  
Immunotherapy Trial

Cancer immunotherapy trial design with delayed treatment effect

2019 ◽  
Vol 19 (3) ◽  
pp. 202-213
Author(s):  
Jianrong Wu ◽  
Jing Wei
Keyword(s):  
Cancer Immunotherapy ◽  
Treatment Effect ◽  
Trial Design ◽  
Delayed Treatment ◽  
Immunotherapy Trial

A group sequential design and sample size estimation for an immunotherapy trial with a delayed treatment effect

2020 ◽  
pp. 096228022098078
Author(s):  
Bosheng Li ◽  
Liwen Su ◽  
Jun Gao ◽  
Liyun Jiang ◽  
Fangrong Yan
Keyword(s):  
Sample Size ◽  
Treatment Effect ◽  
Search Algorithm ◽  
Rank Test ◽  
Group Sequential Design ◽  
Group Sequential ◽  
Delayed Treatment ◽  
Maximum Sample Size ◽  
Analytical Estimation ◽  
Immunotherapy Trial

A delayed treatment effect is often observed in the confirmatory trials for immunotherapies and is reflected by a delayed separation of the survival curves of the immunotherapy groups versus the control groups. This phenomenon makes the design based on the log-rank test not applicable because this design would violate the proportional hazard assumption and cause loss of power. Thus, we propose a group sequential design allowing early termination on the basis of efficacy based on a more powerful piecewise weighted log-rank test for an immunotherapy trial with a delayed treatment effect. We present an approach on the group sequential monitoring, in which the information time is defined based on the number of events occurring after the delay time. Furthermore, we developed a one-dimensional search algorithm to determine the required maximum sample size for the proposed design, which uses an analytical estimation obtained by the inflation factor as an initial value and an empirical power function calculated by a simulation-based procedure as an objective function. In the simulation, we tested the unstable accuracy of the analytical estimation, the consistent accuracy of the maximum sample size determined by the search algorithm and the advantages of the proposed design on saving sample size.

Designing cancer immunotherapy trials with delayed treatment effect using maximin efficiency robust statistics

2020 ◽  
Vol 19 (4) ◽  
pp. 424-435
Author(s):  
Xue Ding ◽  
Jianrong Wu
Keyword(s):  
Cancer Immunotherapy ◽  
Treatment Effect ◽  
Robust Statistics ◽  
Delayed Treatment

scholarly journals Assessing Long-Term Survival Benefits of Immune Checkpoint Inhibitors Using the Net Survival Benefit

2019 ◽  
Vol 111 (11) ◽  
pp. 1186-1191 ◽  
Author(s):  
Julien Péron ◽  
Alexandre Lambert ◽  
Stephane Munier ◽  
Brice Ozenne ◽  
Joris Giai ◽  
...  
Keyword(s):  
Treatment Effect ◽  
Survival Benefit ◽  
Rank Test ◽  
Cure Rate ◽  
Term Survival ◽  
Long Term Survival ◽  
Delayed Treatment ◽  
Net Survival ◽  
Log Rank Test

Abstract Background The treatment effect in survival analysis is commonly quantified as the hazard ratio, and tested statistically using the standard log-rank test. Modern anticancer immunotherapies are successful in a proportion of patients who remain alive even after a long-term follow-up. This new phenomenon induces a nonproportionality of the underlying hazards of death. Methods The properties of the net survival benefit were illustrated using the dataset from a trial evaluating ipilimumab in metastatic melanoma. The net survival benefit was then investigated through simulated datasets under typical scenarios of proportional hazards, delayed treatment effect, and cure rate. The net survival benefit test was computed according to the value of the minimal survival difference considered clinically relevant. As comparators, the standard and the weighted log-rank tests were also performed. Results In the illustrative dataset, the net survival benefit favored ipilimumab [Δ(0) = 15.8%, 95% confidence interval = 4.6% to 27.3%, P = .006]. This favorable effect was maintained when the analysis was focused on long-term survival differences (eg, >12 months, Δ(12) = 12.5% (95% confidence interval = 4.4% to 20.6%, P = .002). Under the scenarios of a delayed treatment effect and cure rate, the power of the net survival benefit test compared favorably to the standard log-rank test power and was comparable to the power of the weighted log-rank test for large values of the threshold of clinical relevance. Conclusion The net long-term survival benefit is a measure of treatment effect that is meaningful whether or not hazards are proportional. The associated statistical test is more powerful than the standard log-rank test when a delayed treatment effect is anticipated.

A robust approach to sample size calculation in cancer immunotherapy trials with delayed treatment effect

Biometrics ◽  
2018 ◽  
Vol 74 (4) ◽  
pp. 1292-1300 ◽  
Author(s):  
Ting Ye ◽  
Menggang Yu
Keyword(s):  
Sample Size ◽  
Cancer Immunotherapy ◽  
Treatment Effect ◽  
Sample Size Calculation ◽  
Robust Approach ◽  
Delayed Treatment

scholarly journals Treating non-responders: pitfalls and implications for cancer immunotherapy trial design

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Zhenzhen Xu ◽  
Yongsoek Park ◽  
Ke Liu ◽  
Bin Zhu
Keyword(s):  
Cancer Immunotherapy ◽  
Trial Design ◽  
Immunotherapy Trial

Cancer immunotherapy trial design with long‐term survivors

2020 ◽  
Author(s):  
Xue Ding ◽  
Jianrong Wu
Keyword(s):  
Cancer Immunotherapy ◽  
Trial Design ◽  
Immunotherapy Trial ◽  
Long Term Survivors

Estimation of delay time in survival data with delayed treatment effect

2018 ◽  
Vol 29 (2) ◽  
pp. 229-243 ◽  
Author(s):  
Wei Li ◽  
Sophie Yu-Pu Chen ◽  
Alan Rong
Keyword(s):  
Treatment Effect ◽  
Survival Data ◽  
Delay Time ◽  
Delayed Treatment
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