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

The most frequently asked question to a statistician: The sample size

2017 ◽  
Vol 23 (5) ◽  
pp. 644-646 ◽  
Author(s):  
Maria Pia Sormani
Keyword(s):  
Clinical Study ◽  
Sample Size ◽  
Study Design ◽  
Treatment Effect ◽  
Sample Size Calculation ◽  
Ethical Implications ◽  
Number Of Patients ◽  
Reducing Costs ◽  
Correct Size

The calculation of the sample size needed for a clinical study is the challenge most frequently put to statisticians, and it is one of the most relevant issues in the study design. The correct size of the study sample optimizes the number of patients needed to get the result, that is, to detect the minimum treatment effect that is clinically relevant. Minimizing the sample size of a study has the advantage of reducing costs, enhancing feasibility, and also has ethical implications. In this brief report, I will explore the main concepts on which the sample size calculation is based.

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 Evaluation of Performance of Adaptive Designs Based on Treatment Effect Intervals

2018 ◽  
Vol 7 (6) ◽  
pp. 81
Author(s):  
Fang Fang ◽  
Yong Lin ◽  
Weichung Joe Shih ◽  
Shou-En Lu ◽  
Guangrui Zhu
Keyword(s):  
Sample Size ◽  
Treatment Effect ◽  
Sample Size Calculation ◽  
Adaptive Designs ◽  
Point Estimate ◽  
Phase 3 ◽  
Treatment Effect Estimation ◽  
Effect Estimation ◽  
Size Adjustment ◽  
Sample Size Adjustment

The accuracy of the treatment effect estimation is crucial to the success of Phase 3 studies. The calculation of sample size relies on the treatment effect estimation and cannot be changed during the trial in a fixed sample size design. Oftentimes, with limited efficacy data available from early phase studies and relevant historical studies, the sample size calculation may not accurately reflect the true treatment effect. Several adaptive designs have been proposed to address this uncertainty in the sample size calculation. These adaptive designs provide flexibility of sample size adjustment during the trial by allowing early trial stopping or sample size adjustment at interim look(s). The use of adaptive designs can optimize the trial performance when the treatment effect is an assumed constant value. However in practice, it may be more reasonable to consider the treatment effect within an interval rather than as a point estimate. Because proper selection of adaptive designs may decrease the failure rate of Phase 3 clinical trials and increase the chance for new drug approval, this paper proposes measures and evaluates the performance of different adaptive designs based on treatment effect intervals, and identifies factors that may affect the performance of adaptive designs.

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.

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