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.

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.

scholarly journals A Combined Test for a Generalized Treatment Effect in Clinical Trials with a Time-to-event Outcome

2017 ◽  
Vol 17 (2) ◽  
pp. 405-421 ◽  
Author(s):  
Patrick Royston
Keyword(s):  
Sample Size ◽  
Treatment Effect ◽  
Sample Size Calculation ◽  
Rank Test ◽  
P Value ◽  
Time To Event ◽  
Test Statistic ◽  
Potential Threat ◽  
Log Rank Test ◽  
Combined Test

Most randomized controlled trials with a time-to-event outcome are designed and analyzed assuming proportional hazards of the treatment effect. The sample-size calculation is based on a log-rank test or the equivalent Cox test. Nonproportional hazards are seen increasingly in trials and are recognized as a potential threat to the power of the log-rank test. To address the issue, Royston and Parmar (2016, BMC Medical Research Methodology 16: 16) devised a new “combined test” of the global null hypothesis of identical survival curves in each trial arm. The test, which combines the conventional Cox test with a new formulation, is based on the maximal standardized difference in restricted mean survival time (RMST) between the arms. The test statistic is based on evaluations of RMST over several preselected time points. The combined test involves the minimum p-value across the Cox and RMST-based tests, appropriately standardized to have the correct null distribution. In this article, I outline the combined test and introduce a command, stctest, that implements the combined test. I point the way to additional tools currently under development for power and sample-size calculation for the combined test.

scholarly journals Clinical Design for Phase II/III Clinical Trials for Testing Therapeutic Interventions in COVID-19 Patients

2020 ◽  
Author(s):  
Shesh Rai ◽  
Chen Qian ◽  
Jianmin Pan ◽  
Anand Seth ◽  
Deo Kumar Srivast ◽  
...  
Keyword(s):  
Clinical Trials ◽  
High Risk ◽  
Sample Size ◽  
Risk Group ◽  
High Risk Group ◽  
Intermediate Risk ◽  
Group Sequential Design ◽  
Group Sequential ◽  
Interim Analyses ◽  
Toxicity Monitoring

Abstract Background Researchers around the world are urgently conducting clinical trials to develop new treatments for reducing mortality and morbidity related to COVID-19. However, due to unknown features of the disease and complexity of the patient population, traditional trial designs may not be optimal in such patients. We propose two independent clinical trials designs based on careful grouping of the expected characteristics of patient population. This could serve as a useful guide for researchers designing COVID-19 related Phase II/III trials. Methods Using the commonly utilized World Health Organization ordinal scale on patient status, we classify patients into three risk groups. In this approach, patients in Stages 3, 4 and 5 are categorized as the intermediate-risk group while patients in Stages 6 and 7 are categorized as the high-risk group. To ensure that an intervention, if deemed efficacious, is promptly made available to vulnerable patients, we propose a group sequential design with two interim analyses along with a final analysis and a toxicity monitoring rule for the intermediate-risk group. For the high-risk group, we propose a group sequential design with two interim analyses without toxicity monitoring. Results Based on different response rates, effect sizes, and power, required sample size and toxicity boundaries are calculated for each scenario. Sample size requirements for the designs with interim analyses are only marginally greater than the ones without. In addition, for both the intermediate-risk group and the high-risk group, conducting two interim analyses have identical required sample size compared with just one interim analysis. Additional issues that could potentially impact the trial are discussed. Conclusions We recommend using composite endpoints, with binary outcome for those in Stages 3, 4 and 5 with a power of 90% to detect an improvement of 20% in response rate, and 30 days mortality rate outcome for those in Stages 6 and 7 with a power of 90% to detect 15% (effect size) reduced mortality rate, in the COVID-19 trial design. For the intermediate-risk group, two interim analyses for efficacy evaluation along with toxicity monitoring are encouraged. For the high-risk group, two interim analyses without toxicity monitoring is advised.

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