Other publications

1. The Evolution of Master Protocol Clinical Trial Designs: A Systematic Literature Review, 2020

Elias Laurin Meyer, Peter Mesenbrink, Cornelia Dunger-Baldauf, Hans-Jürgen Fülle, Ekkehard Glimm, Yuhan Li, Martin Posch, Franz König (2020). The Evolution of Master Protocol Clinical Trial Designs: A Systematic Literature Review, Clinical Therapeutics, ISSN 0149-2918, https://doi.org/10.1016/j.clinthera.2020.05.010 

 

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Abstract

Purpose

Recent years have seen a change in the way that clinical trials are being conducted. There has been a rise of designs more flexible than traditional adaptive and group sequential trials which allow the investigation of multiple substudies with possibly different objectives, interventions, and subgroups conducted within an overall trial structure, summarized by the term master protocol. This review aims to identify existing master protocol studies and summarize their characteristics. The review also identifies articles relevant to the design of master protocol trials, such as proposed trial designs and related methods.

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2. Are p-values Useful to Judge the Evidence Against the Null Hypotheses in Complex Clinical Trials? A Comment on “The Role of p-values in Judging the Strength of Evidence and Realistic Replication Expectations”, Statistics in Biopharmaceutical Research, 2021

 

Statistics in Biopharmaceutical Research: Vol 12, No 2Martin Posch & Franz König. Are p-values Useful to Judge the Evidence Against the Null Hypotheses in Complex Clinical Trials? A Comment on “The Role of p-values in Judging the Strength of Evidence and Realistic Replication Expectations” Statistics in Biopharmaceutical Research, Volume 13, 2021, Issue 1 (p.p.43-45).  DOI: 10.1080/19466315.2020.1847182

ABSTRACT

In this commentary to Eric Gibson comprehensive discussion on “The Role of p-values in Judging the Strength of Evidence and Realistic Replication Expectations” (Gibson 2020), the authors discuss the use of p-values in designs with interim analysis, adaptations and multiple treatments and subgroups and the interpretation of corresponding adjusted p-values.

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3. Optimizing subgroup selection in two‐stage adaptive enrichment and umbrella designs, Statistics in Medicine, 2021

 

Nicolás M. Ballarini, Thomas Burnett, Thomas Jaki, Christoper Jennison, Franz König, Martin Posch. Optimizing subgroup selection in two‐stage adaptive enrichment and umbrella designs, Statistics in Medicine, 2021. DOI:10.1002/sim.8949

ABSTRACT

We design two‐stage confirmatory clinical trials that use adaptation to find the subgroup of patients who will benefit from a new treatment, testing for a treatment effect in each of two disjoint subgroups. Our proposal allows aspects of the trial, such as recruitment probabilities of each group, to be altered at an interim analysis. We use the conditional error rate approach to implement these adaptations with protection of overall error rates. Applying a Bayesian decision‐theoretic framework, we optimize design parameters by maximizing a utility function that takes the population prevalence of the subgroups into account. We show results for traditional trials with familywise error rate control (using a closed testing procedure) as well as for umbrella trials in which only the per‐comparison type 1 error rate is controlled. We present numerical examples to illustrate the optimization process and the effectiveness of the proposed designs.

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4. Collaborative Platform Trials to Fight COVID‐19: Methodological and Regulatory Considerations for a Better Societal Outcome (2021)

Olivier Collignon, Carl‐Fredrik Burman, Martin Posch, Anja Schiel (2021). Collaborative Platform Trials to Fight COVID‐19: Methodological and Regulatory Considerations for a Better Societal Outcome. https://doi.org/10.1002/cpt.2183

ABSTRACT

For the development of coronavirus disease 2019 (COVID‐19) drugs during the ongoing pandemic, speed is of essence whereas quality of evidence is of paramount importance. Although thousands of COVID‐19 trials were rapidly started, many are unlikely to provide robust statistical evidence and meet regulatory standards (e.g., because of lack of randomization or insufficient power). This has led to an inefficient use of time and resources. With more coordination, the sheer number of patients in these trials might have generated convincing data for several investigational treatments. Collaborative platform trials, comparing several drugs to a shared control arm, are an attractive solution. Those trials can utilize a variety of adaptive design features in order to accelerate the finding of life‐saving treatments. In this paper, we discuss several possible designs, illustrate them via simulations, and also discuss challenges, such as the heterogeneity of the target population, time‐varying standard of care, and the potentially high number of false hypothesis rejections in phase II and phase III trials. We provide corresponding regulatory perspectives on approval and reimbursement, and note that the optimal design of a platform trial will differ with our societal objective and by stakeholder. Hasty approvals may delay the development of better alternatives, whereas searching relentlessly for the single most efficacious treatment may indirectly diminish the number of lives saved as time is lost. We point out the need for incentivizing developers to participate in collaborative evidence‐generation initiatives when a positive return on investment is not met.

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5. Type I error Considerations in Master Protocols with Common Control in Oncology Trials: Report of an American Statistical Association Biopharmaceutical Section Open Forum Discussion, Statistics in Biopharmaceutical Research, 2021

Statistics in Biopharmaceutical Research: Vol 12, No 2Rajeshwari Sridhara, Olga Marchenko, Qi Jiang,Richard Pazdur, Martin Posch, Mary Redman, Yevgen Tymofyeyev, Xiaoyun (Nicole) Li, Marc Theoret, Yuan Li Shen, Thomas Gwise, Lorenzo Hess, Michael Coory, Andrew Raven, Naoto Kotani, Kit Roes, Filip Josephson, Scott Berry, Richard Simon & Bruce Binkowitz. A Type I error Considerations in Master Protocols with Common Control in Oncology Trials: Report of an American Statistical Association Biopharmaceutical Section Open Forum Discussion, Statistics in Biopharmaceutical Research,  2021. DOI:10.1080/19466315.2021.1906743

ABSTRACT

This article provides a summary of discussions from the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section Open Forum organized by the ASA BIOP Statistical Methods in Oncology Scientific Working Group in coordination with the US FDA Oncology Center of Excellence on October 8, 2020. Diverse stakeholders including experts from international regulatory agencies, academicians, and members from the pharmaceutical industry engaged in a debate on type I error considerations in master protocols with a common control. Although there were concerns in specific situations where type I error adjustment may be necessary, the panelists agreed that adjustment of type I error for multiplicity when a common control is used may not be necessary if the hypotheses are inferentially independent.

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6. The use of external controls: To what extent can it currently be recommended?, Pharmaceutical Statistics, 2021

Hans Ulrich Burger, Christoph Gerlinger, Chris Harbron, Armin Koch, Martin Posch, Justine Rochon, Anja Schiel. The use of external controls: To what extent can it currently be recommended?, Pharmaceutical Statistics, April 2021. DOI:10.1002/pst.2120

ABSTRACT

With more and better clinical data being captured outside of clinical studies and greater data sharing of clinical studies, external controls may become a more attractive alternative to randomized clinical trials (RCTs). Both industry and regulators recognize that in situations where a randomized study cannot be performed, external controls can provide the needed contextualization to allow a better interpretation of studies without a randomized control. It is also agreed that external controls will not fully replace RCTs as the gold standard for formal proof of efficacy in drug development and the yardstick of clinical research. However, it remains unclear in which situations conclusions about efficacy and a positive benefit/risk can reliably be based on the use of an external control. This paper will provide an overview on types of external control, their applications and the different sources of bias their use may incur, and discuss potential mitigation steps. It will also give recommendations on how the use of external controls can be justified.

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7. Use of Non-concurrent Common Control in Master Protocols in Oncology Trials: Report of an American Statistical Association Biopharmaceutical Section Open Forum Discussion, Statistics in Biopharmaceutical Research, 2021

Statistics in Biopharmaceutical Research: Vol 12, No 2Rajeshwari Sridharaa Oncology Center of Excellence US FDA;,Olga Marchenko,Qi Jiang,Richard Pazdur,Martin PoschORCID Icon,Scott Berry,Marc Theoret,Yuan Li Shen,Thomas Gwise,Lorenzo Hess,Andrew Raven,Khadija Rantell,Kit Roes,Richard Simon,Mary Redman,Yuan Ji &Cindy Lu. Use of Non-concurrent Common Control in Master Protocols in Oncology Trials: Report of an American Statistical Association Biopharmaceutical Section Open Forum Discussion, Statistics in Biopharmaceutical Research,  2021. DOI:10.1080/19466315.2021.1906743

ABSTRACT

This article summarizes the discussions from the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section Open Forum that took place on December 10, 2020 and was organized by the ASA BIOP Statistical Methods in Oncology Scientific Working Group, in coordination with the US FDA Oncology Center of Excellence. Diverse stakeholders including experts from international regulatory agencies, academicians, and representatives of the pharmaceutical industry engaged in a discussion on the use of non-concurrent control in Master Protocols for oncology trials.

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