Biomarker‐driven population enrichment for adaptive oncology trials with time to event endpoints

Mehta, Cyrus R.; Schäfer, Helmut; Daniel, Hanna; Irle, Sebastian

doi:10.1002/sim.7068

Cited by 18 publications

(24 citation statements)

References 2 publications

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“…Similarly, for the second stage, the analysis time can be determined event driven. (iii)While for Type I error control, any weights

w_{1}, w_{2}, w_{1}^{2} + w_{2}^{2} = 1

can be chosen, the choice has an impact on the power. Based on the group sequential analogy, a good choice is to choose

w_{1}^{2}

proportional to the expected number of events in G 1 relative to the expected overall number of events. (iv)A similar procedure based on conditional rejection probabilities has been proposed in Irle and Schäfer and Mehta et al, which has the same caveat as the Jenkins et al procedure, namely, that not all events can be utilized in case the study is extended (with T ′ > T ).…”

Section: Statistical Methodologymentioning

confidence: 99%

“…This procedure, termed patient‐wise separation , controls the Type I error rate even if all unblinded available first‐stage data are used in the adaptation decisions, as long as the follow‐up period of first‐stage patients remains independent of the interim data . Alternatively, the patient‐wise separation approach can also be applied to adaptive tests based on the conditional rejection probability . A drawback of the above patient‐wise separation approaches is that no early testing of the null hypothesis at the interim analysis is possible because the computation of the first‐stage test statistics requires information on events occurring in the second stage.…”

Section: Introductionmentioning

confidence: 99%

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Nested combination tests with a time‐to‐event endpoint using a short‐term endpoint for design adaptations

Jörgens¹,

Wassmer

König

et al. 2019

Pharmaceutical Statistics

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Adaptive trial methodology for multiarmed trials and enrichment designs has been extensively discussed in the past. A general principle to construct test procedures that control the family-wise Type I error rate in the strong sense is based on combination tests within a closed test. Using survival data, a problem arises when using information of patients for adaptive decision making, which are under risk at interim. With the currently available testing procedures, either no testing of hypotheses in interim analyses is possible or there are restrictions on the interim data that can be used in the adaptation decisions as, essentially, only the interim test statistics of the primary endpoint may be used. We propose a general adaptive testing procedure, covering multiarmed and enrichment designs, which does not have these restrictions. An important application are clinical trials, where short-term surrogate endpoints are used as basis for trial adaptations, and we illustrate how such trials can be designed. We propose statistical models to assess the impact of effect sizes, the correlation structure between the short-term and the primary endpoint, the sample size, the timing of interim analyses, and the selection rule on the operating characteristics.

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“…Similarly, for the second stage, the analysis time can be determined event driven. (iii)While for Type I error control, any weights

w_{1}, w_{2}, w_{1}^{2} + w_{2}^{2} = 1

can be chosen, the choice has an impact on the power. Based on the group sequential analogy, a good choice is to choose

w_{1}^{2}

Section: Statistical Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nested combination tests with a time‐to‐event endpoint using a short‐term endpoint for design adaptations

Jörgens¹,

Wassmer

König

et al. 2019

Pharmaceutical Statistics

View full text Add to dashboard Cite

show abstract

“…The concept of informational analysis is simple and shares a lot of similarities with an interim analysis of an adaptive seamless Phase II/III design for subgroup selection and adaptive hypothesis adjustment [4,5,6,7]. In this regard, we call the corresponding design an informational Phase III design or informational design in short to contrast with an adaptive seamless Phase II/III design.…”

Section: Adaptive Informational Designmentioning

confidence: 97%

“…A key statistical issue about informational analysis is the impact on Type I error rate when the subgroup (or data in the sub-study) is included in the primary analysis. From a statistical perspective, a lot can be directly borrowed from the adaptive seamless Phase II/III design literature on subpopulation selection and adaptive hypothesis adjustment to address this issue (e.g., combination tests [5] and conditional error function methods [6,7]). We 7 follow [4] by taking advantage of the fact that the test-statistics under the settings we consider all have a multivariate normal distribution to derive an adjusted significance level to keep the Type I error rates under control.…”

Section: Type I Error Rate Controlmentioning

confidence: 99%

Adaptive Informational Design of Confirmatory Phase III Trials With an Uncertain Biomarker Effect to Improve the Probability of Success

Chen

Shentu

et al. 2016

Statistics in Biopharmaceutical Research

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Oncology drug developers sometimes decide to initiate Phase III randomized confirmatory trials at risk after significant preliminary anti-tumor activities are observed in small Phase I/II single arm studies.There are two clear challenges. First, these investigational drugs may have a greater benefit in a biomarker enriched population. But the limited data from Phase I/II can hardly provide the much-needed information for selecting a biomarker cutpoint or prioritizing a biomarker hypothesis for Phase III testing.Second, the data seldom provides any insight on how the treatment benefit evolves over time. Riskmitigation strategies such as conventional adaptive-designs that rely on interim analyses for modifying the study design are less reliable because the treatment effect observed at an interim analysis may not be the same as in the final analysis. The use of an intermediate endpoint for interim decision makes it even more unreliable because the predictive value of an intermediate endpoint is often unknown for drugs with a new mechanism of action. In this paper, we present an alternative design strategy to mitigate the risks.The idea is to add an analysis of the primary endpoint at end of the Phase III trial in a subgroup of patients representing the overall study population. We call it informational analysis and the corresponding design informational design to emphasize its difference from the conventional event-time or calendar-time driven interim analysis. From a high-level perspective, the subgroup analysis is equivalent to a Phase II trial conducted under the same study design at the same time in the same population at the same sites as the Phase III trial. It provides a more reliable resource of information for inference than a separate Phase II trial or a conventional interim analysis. The strategy is applied to address a wide range of statistical issues encountered in expedited development of personalized medicines, including alpha splitting between a biomarker subpopulation and the overall population and de-selection of non-performing biomarker subpopulations. Applications to hypothetical Phase III trials are illustrated. Although the strategy is motivated by oncology studies, it may be applied to drug development in other therapeutic areas with similar concerns.

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“…101 To realise the potential of biomarkerdriven clinical trials for ARDS, however, a few things must happen. 102,103 First, markers must be rapidly available for hospital inpatients to inform potential trial eligibility. By contrast with cancer, a biopsy is unlikely to be available for ARDS and patients and providers cannot afford to wait long for potential genetic results.…”

Section: The Next Step: Biologically Informed Trialsmentioning

confidence: 99%

Novel translational approaches to the search for precision therapies for acute respiratory distress syndrome

Meyer

Calfee

2017

The Lancet Respiratory Medicine

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In the 50 years since acute respiratory distress syndrome (ARDS) was first described, substantial progress has been made in identifying the risk factors for and the pathogenic contributors to the syndrome and in characterising the protein expression patterns in plasma and bronchoalveolar lavage fluid from patients with ARDS. Despite this effort, however, pharmacological options for ARDS remain scarce. Frequently cited reasons for this absence of specific drug therapies include the heterogeneity of patients with ARDS, the potential for a differential response to drugs, and the possibility that the wrong targets have been studied. Advances in applied biomolecular technology and bioinformatics have enabled breakthroughs for other complex traits, such as cardiovascular disease or asthma, particularly when a precision medicine paradigm, wherein a biomarker or gene expression pattern indicates a patient's likelihood of responding to a treatment, has been pursued. In this Review, we consider the biological and analytical techniques that could facilitate a precision medicine approach for ARDS.

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Biomarker‐driven population enrichment for adaptive oncology trials with time to event endpoints

Cited by 18 publications

References 2 publications

Nested combination tests with a time‐to‐event endpoint using a short‐term endpoint for design adaptations

Nested combination tests with a time‐to‐event endpoint using a short‐term endpoint for design adaptations

Adaptive Informational Design of Confirmatory Phase III Trials With an Uncertain Biomarker Effect to Improve the Probability of Success

Novel translational approaches to the search for precision therapies for acute respiratory distress syndrome

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