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Designing cancer immunotherapy trials with delayed treatment effect using maximin efficiency robust statistics
Author(s) -
Ding Xue,
Wu Jianrong
Publication year - 2020
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.2003
Subject(s) - statistics , rank (graph theory) , mathematics , log rank test , minimax , clinical trial , sample size determination , test (biology) , medicine , proportional hazards model , mathematical optimization , paleontology , combinatorics , biology
Summary 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. Although few statistical methods are available for immunotherapy trial design that incorporates a delayed treatment effect, recently, Ye and Yu proposed the use of a maximin efficiency robust test (MERT) for the trial design. The MERT is a weighted log‐rank test that puts less weight on early events and full weight after the delayed period. However, the weight function of the MERT involves an unknown function that has to be estimated from historical data. Here, for simplicity, we propose the use of an approximated maximin test, the V 0 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 V 0 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 V 0 test is derived. Simulations are conducted to compare the performance of the V 0 test to the existing tests. A real trial is used to illustrate cancer immunotherapy trial design with delayed treatment effect.