A conditional error function approach for adaptive enrichment designs with continuous endpoints

Placzek, Marius; Friede, Tim

doi:10.1002/sim.8154

Cited by 10 publications

(29 citation statements)

References 56 publications

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“…In other categories, empirical evidence is required. Here, studies using an adaptive enrichment design that adaptively select the population of interest can be used for efficient clinical development programmes [ 31 , 32 ].…”

Section: Summary and Discussionmentioning

confidence: 99%

Classification of Companion Diagnostics: A New Framework for Biomarker-Driven Patient Selection

Huber

Friede

Stingl

et al. 2021

Ther Innov Regul Sci

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Background Modern personalized medicine strategies builds on therapy companion diagnostics to stratify patients into subgroups with differential benefit/risk. In general, stratification for drug response implies a treatment-by-subgroup interaction. This interaction is usually suggested by the drug’s mechanism of action and investigated in pharmacological research or in clinical studies. In these candidate genes or pathway approaches, either biological reasons for a differential benefit/risk or statistical interaction regarding a pharmacological or clinical endpoint or both may be given. For successful drug approval, demonstration of a positive benefit/risk balance in the intended patient population is required. This also applies to situations with biomarker-selected populations. However, further regulatory considerations relate to the usefulness and plausibility of the selected patients and benefit/risk extrapolations or alternative therapy options in biomarker-negative populations. Methods To facilitate the specification of regulatory requirements and support the design of clinical development programmes, a systematic classification of biomarker-drug pairs is needed, in particular with regard to the expected underlying molecular mechanism and the clinical evidence. Results A classification of five biomarker-drug categories is proposed related to increasing evidence on the biomarker’s predictive value in relation to a specific drug. We classified biomarkers into five ascending categories with increasing evidence on the predictive nature of the biomarker in relation to a specific drug according to the comparative pharmacological and clinical evidence. Conclusions The proposed classification will facilitate regulatory decision-making and support drug development with respect to biomarker-related subgrouping, both, during clinical programme and at the time of marketing authorization application, since the grade of evidence on the differential power of the biomarker can be considered as an indicator for the usefulness of a biomarker-related subgrouping.

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Section: Summary and Discussionmentioning

confidence: 99%

Classification of Companion Diagnostics: A New Framework for Biomarker-Driven Patient Selection

Huber

Friede

Stingl

et al. 2021

Ther Innov Regul Sci

Self Cite

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“…There is a smaller body of work on clinical trials with subgroup selection, which has mainly used the combination testing approach, although methods based on the conditional error principle (Friede et al., 2012 ; Placzek & Friede, 2019 ; Stallard, Hamborg, Parsons, & Friede, 2014 ) and the group‐sequential approach (Magnusson & Turnbull, 2013 ) have also been proposed. The number of subgroups considered is usually small and different assumptions are made regarding the subgroup structure.…”

Section: Discussionmentioning

confidence: 99%

“…The number of subgroups considered is usually small and different assumptions are made regarding the subgroup structure. For instance, Placzek and Friede ( 2019 ) consider nested subgroups which might arise from using different thresholds on a continuous (or at least ordinal) biomarker. An overview is provided in Ondra et al.…”

Section: Discussionmentioning

confidence: 99%

Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods, simulation model and their implementation in R

2020

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Adaptive seamless designs combine confirmatory testing, a domain of phase III trials, with features such as treatment or subgroup selection, typically associated with phase II trials. They promise to increase the efficiency of development programmes of new drugs, e.g. in terms of sample size and / or development time. It is well acknowledged that adaptive designs are more involved from a logistical perspective and require more upfront planning, often in form of extensive simulation studies, than conventional approaches. Here we present a framework for adaptive treatment and subgroup selection using the same notation, which links the somewhat disparate literature on treatment selection on one side and on subgroup selection on the other. Furthermore, we introduce a flexible and yet efficient simulation model that serves both designs. As primary endpoints often take a long time to observe, interim analyses are frequently informed by early outcomes. Therefore, all methods presented accommodate both, interim analyses informed by the primary outcome or an early outcome. The R package asd, previously developed to simulate designs with treatment selection, was extended to include subpopulation selection (so-called adaptive enrichment designs). Here we describe the functionality of the R package asd and use it to present some worked-up examples motivated by clinical trials in chronic obstructive pulmonary disease and oncology. The examples illustrate various features of the R package providing at the same time insights into the operating characteristics of adaptive seamless studies.

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“…Variance heterogeneity across populations is a general issue for trials with multiple populations that applies similarly to procedures with FWER control (see e.g. Placzek and Friede, 2019). One can say, whenever control of the FWER is possible then control of the PWER is possible as well, since the latter just controls an average of family-error rates.…”

Section: Discussionmentioning

confidence: 99%

A liberal type I error rate for studies in precision medicine

Brannath¹,

Hillner²,

Rohmeyer³

2020

Preprint

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We introduce a new multiple type I error criterion for clinical trials with multiple populations. Such trials are of interest in precision medicine where the goal is to develop treatments that are targeted to specific sub-populations defined by genetic and/or clinical biomarkers. The new criterion is based on the observation that not all type I errors are relevant to all patients in the overall population. If disjoint sub-populations are considered, no multiplicity adjustment appears necessary, since a claim in one subpopulation does not affect patients in the other ones. For intersecting sub-populations we suggest to control the average multiple type error rate, i.e. the probably that a randomly selected patient will be exposed to an inefficient treatment. We call this the population-wise error rate, exemplify it by a number of examples and illustrate how to control it with an adjustment of critical boundaries or adjusted p-values. We furthermore define corresponding simultaneous confidence intervals. We finally illustrate the power gain achieved by passing from family-wise to population-wise error rate control with two simple examples and a recently suggest multiple testing approach for umbrella trials.

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A conditional error function approach for adaptive enrichment designs with continuous endpoints

Cited by 10 publications

References 56 publications

Classification of Companion Diagnostics: A New Framework for Biomarker-Driven Patient Selection

Classification of Companion Diagnostics: A New Framework for Biomarker-Driven Patient Selection

Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods, simulation model and their implementation in R

A liberal type I error rate for studies in precision medicine

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