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

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 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

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

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

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.

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.

scholarly journals Designing therapeutic cancer vaccine trials with delayed treatment effect

2016 ◽  
Vol 36 (4) ◽  
pp. 592-605 ◽  
Author(s):  
Zhenzhen Xu ◽  
Boguang Zhen ◽  
Yongsoek Park ◽  
Bin Zhu
Keyword(s):  
Cancer Vaccine ◽  
Treatment Effect ◽  
Therapeutic Cancer Vaccine ◽  
Delayed Treatment ◽  
Vaccine Trials

scholarly journals Design and analysis of clinical trials in the presence of delayed treatment effect

2016 ◽  
Vol 35 (11) ◽  
pp. 1774-1779 ◽  
Author(s):  
Tony Sit ◽  
Mengling Liu ◽  
Michael Shnaidman ◽  
Zhiliang Ying
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Delayed Treatment

Group sequential monitoring based on the maximum of weighted log-rank statistics with the Fleming–Harrington class of weights in oncology clinical trials

2020 ◽  
Vol 29 (12) ◽  
pp. 3525-3532
Author(s):  
Thomas J Prior
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Proportional Hazards ◽  
Rank Test ◽  
Event Data ◽  
Time To Event ◽  
Survival Curves ◽  
Delayed Treatment ◽  
Time To Event Data ◽  
Log Rank Test

Clinical trials in oncology often involve the statistical analysis of time-to-event data such as progression-free survival or overall survival to determine the benefit of a treatment or therapy. The log-rank test is commonly used to compare time-to-event data from two groups. The log-rank test is especially powerful when the two groups have proportional hazards. However, survival curves encountered in oncology studies that differ from one another do not always differ by having proportional hazards; in such instances, the log-rank test loses power, and the survival curves are said to have “non-proportional hazards”. This non-proportional hazards situation occurs for immunotherapies in oncology; immunotherapies often have a delayed treatment effect when compared to chemotherapy or radiation therapy. To correctly identify and deliver efficacious treatments to patients, it is important in oncology studies to have available a statistical test that can detect the difference in survival curves even in a non-proportional hazards situation such as one caused by delayed treatment effect. An attempt to address this need was the “max-combo” test, which was originally described only for a single analysis timepoint; this article generalizes that test to preserve type I error when there are one or more interim analyses, enabling efficacious treatments to be identified and made available to patients more rapidly.

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