Design considerations in clinical trials with cure rate survival data: A case study in oncology

SummaryCancer immunotherapy often reflects the improvement in both short‐term risk reduction and long‐term survival. In this scenario, a mixture cure model can be used for the trial design. However, the hazard functions based on the mixture cure model between two groups will ultimately crossover. Thus, the conventional assumption of proportional hazards may be violated and study design using standard log‐rank test (LRT) could lose power if the main interest is to detect the improvement of long‐term survival. In this paper, we propose a change sign weighted LRT for the trial design. We derived a sample size formula for the weighted LRT, which can be used for designing cancer immunotherapy trials to detect both short‐term risk reduction and long‐term survival. Simulation studies are conducted to compare the efficiency between the standard LRT and the change sign weighted LRT.

Section: Motivation Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cancer immunotherapy trial design with long‐term survivors

Ding

2020

“…The proposed design uses a mixture cure rate model; see also Sun et al for a recent discussion in the clinical trial context. Let

S_{i}^{*}, i = 1, 2

be the survival functions of the uncured patients for the control and experimental arm, respectively, and let p i , i =1,2 be the proportions of patients cured.…”

Section: Sample Size For a Time‐to‐event Endpoint With A Cure Proportionmentioning

confidence: 99%

“…Hence, it is assumed that the experimental drug will act through both, increasing the proportion of cured patients and delaying events for those patients not cured. The ratio of hazard functions of treatment versus control group can be written as

θ (t) = h_{2} (t) / h_{1} (t) = (\frac{1 - p_{2}}{1 - p_{1}}) \frac{f_{2}^{*} (t)}{f_{1}^{*} (t)} (\frac{p_{1} + (1 - p_{1}) S_{1}^{*} (t)}{p_{2} + (1 - p_{2}) S_{2}^{*} (t)}) .

So, even in the simple model proposed by Sun et al where both

S_{i}^{*}

are assumed to follow an exponential distribution with rates λ i , the HR function θ ( t ), t ≥ 0 depends on time as soon as at least one of the p i >0, ie, the PH ratio assumption does not hold in general in this simple cure proportion model. Absence of the PH assumption has several implications, which we discuss in Section .…”

Section: Sample Size For a Time‐to‐event Endpoint With A Cure Proportionmentioning

confidence: 99%

Integrating phase 2 into phase 3 based on an intermediate endpoint while accounting for a cure proportion—With an application to the design of a clinical trial in acute myeloid leukemia

Rufibach

Heinzmann

Monnet

2019

For a trial with primary endpoint overall survival for a molecule with curative potential, statistical methods that rely on the proportional hazards assumption may underestimate the power and the time to final analysis. We show how a cure proportion model can be used to get the necessary number of events and appropriate timing via simulation. If phase 1 results for the new drug are exceptional and/or the medical need in the target population is high, a phase 3 trial might be initiated after phase 1. Building in a futility interim analysis into such a pivotal trial may mitigate the uncertainty of moving directly to phase 3. However, if cure is possible, overall survival might not be mature enough at the interim to support a futility decision. We propose to base this decision on an intermediate endpoint that is sufficiently associated with survival. Planning for such an interim can be interpreted as making a randomized phase 2 trial a part of the pivotal trial: If stopped at the interim, the trial data would be analyzed, and a decision on a subsequent phase 3 trial would be made. If the trial continues at the interim, then the phase 3 trial is already underway. To select a futility boundary, a mechanistic simulation model that connects the intermediate endpoint and survival is proposed. We illustrate how this approach was used to design a pivotal randomized trial in acute myeloid leukemia and discuss historical data that informed the simulation model and operational challenges when implementing it.

Group sequential design with maximin efficiency robust test for immunotherapy with generalized delayed treatment effect

Li,

Zhang,

Yang

et al. 2023

The delayed treatment effect is a common feature of immunotherapy, characterized by a gradual onset of action ranging from no effect to full effect. In this study, we propose a generalized delayed treatment effect function to depict the delayed effective process precisely and flexibly. To reduce potential power loss caused by the delayed treatment effect in a group sequential trial, we employ the maximin efficiency robust test, which enhances power robustness across a range of possible delays. We present novel approaches based on the Markov chain method for determining group sequential boundaries, calculating the power function, and estimating the maximum sample size through iterative regressions between the square root of the maximum sample size and the normal quantile of power. Extensive simulation studies validate the effectiveness of our approaches, particularly in balanced trials, demonstrating the validity of group sequential boundaries and the accuracy of maximum sample size estimations. Additionally, we utilize a real trial as an example to compare our considered trial with group sequential trials using the log‐rank and generalized piecewise weighted log‐rank tests. The results show significantly reduced maximum sample sizes, highlighting the economic advantage of our approach.