Estimation of delay time in survival data with delayed treatment effect

Li, Wei; Chen, Yu Pu; Rong, Alan

doi:10.1080/10543406.2018.1534857

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Second, the statistical power is only slightly influenced if the true fixed delay time is smaller than the specified delay time under the assumption of the fixed time-lag effect. It is recommended to refer to the work of He et al 21 and Li et al 22 on the methods to estimate the delay time.…”

Section: Discussionmentioning

confidence: 99%

A group sequential design and sample size estimation for an immunotherapy trial with a delayed treatment effect

Gao

et al. 2020

Stat Methods Med Res

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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.

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Section: Discussionmentioning

confidence: 99%

A group sequential design and sample size estimation for an immunotherapy trial with a delayed treatment effect

Gao

et al. 2020

Stat Methods Med Res

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“…Various researchers have proposed methodologies to address trials with a DTE 26–36 . However, most of these discussions focus on regaining statistical power lost due to the delay by using alternative analysis methods, such as weighted log‐rank tests or the difference in restricted mean survival times (RMST) 37 .…”

Section: Methodsmentioning

confidence: 99%

“…Various researchers have proposed methodologies to address trials with a DTE. [26][27][28][29][30][31][32][33][34][35][36] However, most of these discussions focus on regaining statistical power lost due to the delay by using alternative analysis methods, such as weighted log-rank tests or the difference in restricted mean survival times (RMST). 37 These methods aim to account for the time-dependent hazard ratio without assuming PH or the specific shape of the underlying survival curves.…”

Section: Delayed Treatment Effectsmentioning

confidence: 99%

Assurance methods for designing a clinical trial with a delayed treatment effect

Salsbury,

Oakley,

Julious

et al. 2024

Statistics in Medicine

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An assurance calculation is a Bayesian alternative to a power calculation. One may be performed to aid the planning of a clinical trial, specifically setting the sample size or to support decisions about whether or not to perform a study. Immuno‐oncology is a rapidly evolving area in the development of anticancer drugs. A common phenomenon that arises in trials of such drugs is one of delayed treatment effects, that is, there is a delay in the separation of the survival curves. To calculate assurance for a trial in which a delayed treatment effect is likely to be present, uncertainty about key parameters needs to be considered. If uncertainty is not considered, the number of patients recruited may not be enough to ensure we have adequate statistical power to detect a clinically relevant treatment effect and the risk of an unsuccessful trial is increased. We present a new elicitation technique for when a delayed treatment effect is likely and show how to compute assurance using these elicited prior distributions. We provide an example to illustrate how this can be used in practice and develop open‐source software to implement our methods. Our methodology has the potential to improve the success rate and efficiency of Phase III trials in immuno‐oncology and for other treatments where a delayed treatment effect is expected to occur.

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Designing cancer immunotherapy trials with delayed treatment effect using maximin efficiency robust statistics

Ding

2020

Pharmaceutical Statistics

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SummaryThe 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 V0 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 V0 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 V0 test is derived. Simulations are conducted to compare the performance of the V0 test to the existing tests. A real trial is used to illustrate cancer immunotherapy trial design with delayed treatment effect.

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Estimation of delay time in survival data with delayed treatment effect

Cited by 8 publications

References 15 publications

A group sequential design and sample size estimation for an immunotherapy trial with a delayed treatment effect

A group sequential design and sample size estimation for an immunotherapy trial with a delayed treatment effect

Assurance methods for designing a clinical trial with a delayed treatment effect

Designing cancer immunotherapy trials with delayed treatment effect using maximin efficiency robust statistics

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