Single‐arm phase II trial design under parametric cure models

Wu, Jianrong

doi:10.1002/pst.1678

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…This idea comparing the survival distributions allows to conduct for hypothesis testing for the median survivals only when the survivals are assumed to follow exponential distribution. Wu [ 22 ] proposes a single-arm Phase II clinical trial design under a class of parametric cure models. Chu et al [ 23 ] proposes the design for immunotherapy trials with random delayed treatment effect based on survival time.…”

Section: Introductionmentioning

confidence: 99%

Optimal two-stage design of single arm Phase II clinical trials based on median event time test

Park

2021

PLoS ONE

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The Phase II clinical trials aim to assess the therapeutic efficacy of a new drug. The therapeutic efficacy has been often quantified by response rate such as overall response rate or survival probability in the Phase II setting. However, there is a strong desire to use survival time, which is the gold standard endpoint for the confirmatory Phase III study, when investigators set the primary objective of the Phase II study and test hypotheses based on the median survivals. We propose a method for median event time test to provide the sample size calculation and decision rule of testing. The decision rule is simple and straightforward in that it compares the observed median event time to the identified threshold. Moreover, it is extended to optimal two-stage design for practice, which extends the idea of Simon’s optimal two-stage design for survival endpoint. We investigate the performance of the proposed methods through simulation studies. The proposed methods are applied to redesign a trial based on median event time for trial illustration, and practical strategies are given for application of proposed methods.

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Section: Introductionmentioning

confidence: 99%

Optimal two-stage design of single arm Phase II clinical trials based on median event time test

Park

2021

PLoS ONE

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“…When only the cure rate was present, Boag 4 proposed a cure rate model to appropriately model the data. Significant generalizations, 5-10 computation programs, 11,12 and applications to clinical study designs [13][14][15][16][17] of mixture cure rate models have been extensively studied. In recent years, another type of cure rate model, the proportional hazards cure rate model (PHCRM), has been FIGURE 1 Ipilimumab with delay time and cure rate proposed 18 and applied.…”

Section: Introductionmentioning

confidence: 99%

Weighted log‐rank test for time‐to‐event data in immunotherapy trials with random delayed treatment effect and cure rate

Liu

Chu

Rong

2018

Pharmaceutical Statistics

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A cancer clinical trial with an immunotherapy often has 2 special features, which are patients being potentially cured from the cancer and the immunotherapy starting to take clinical effect after a certain delay time. Existing testing methods may be inadequate for immunotherapy clinical trials, because they do not appropriately take the 2 features into consideration at the same time, hence have low power to detect the true treatment effect. In this paper, we proposed a piece-wise proportional hazards cure rate model with a random delay time to fit data, and a new weighted log-rank test to detect the treatment effect of an immunotherapy over a chemotherapy control. We showed that the proposed weight was nearly optimal under mild conditions. Our simulation study showed a substantial gain of power in the proposed test over the existing tests and robustness of the test with misspecified weight. We also introduced a sample size calculation formula to design the immunotherapy clinical trials using the proposed weighted log-rank test.

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Study design of single‐arm phase II immunotherapy trials with long‐term survivors and random delayed treatment effect

Chu

Liu

Rong

2020

Pharmaceutical Statistics

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In the traditional study design of a single‐arm phase II cancer clinical trial, the one‐sample log‐rank test has been frequently used. A common practice in sample size calculation is to assume that the event time in the new treatment follows exponential distribution. Such a study design may not be suitable for immunotherapy cancer trials, when both long‐term survivors (or even cured patients from the disease) and delayed treatment effect are present, because exponential distribution is not appropriate to describe such data and consequently could lead to severely underpowered trial. In this research, we proposed a piecewise proportional hazards cure rate model with random delayed treatment effect to design single‐arm phase II immunotherapy cancer trials. To improve test power, we proposed a new weighted one‐sample log‐rank test and provided a sample size calculation formula for designing trials. Our simulation study showed that the proposed log‐rank test performs well and is robust of misspecified weight and the sample size calculation formula also performs well.

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Single‐arm phase II trial design under parametric cure models

Cited by 3 publications

References 16 publications

Optimal two-stage design of single arm Phase II clinical trials based on median event time test

Optimal two-stage design of single arm Phase II clinical trials based on median event time test

Weighted log‐rank test for time‐to‐event data in immunotherapy trials with random delayed treatment effect and cure rate

Study design of single‐arm phase II immunotherapy trials with long‐term survivors and random delayed treatment effect

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