Optimal Decision Rules for Biomarker-Based Subgroup Selection for a Targeted Therapy in Oncology

Krisam, Johannes; Kieser, Meinhard

doi:10.3390/ijms160510354

Cited by 10 publications

(10 citation statements)

References 20 publications

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“…Especially, the framework allows us to assess when it is favourable to investigate the biomarker in a clinical trial and when it is actually more efficient to disregard the biomarker and to proceed with a classical trial design. This extends earlier decision theoretic methods that focused on the selection of the population for clinical trials incorporating a biomarker (Beckman et al, 2011;Krisam and Kieser, 2014;Götte et al, 2015;Kirchner et al, 2016;Krisam and Kieser, 2015;Graf et al, 2015).…”

Section: Introductionsupporting

confidence: 52%

Optimizing Trial Designs for Targeted Therapies

Ondra¹,

Jobjörnsson²,

Beckman³

et al. 2016

PLoS ONE

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An important objective in the development of targeted therapies is to identify the populations where the treatment under consideration has positive benefit risk balance. We consider pivotal clinical trials, where the efficacy of a treatment is tested in an overall population and/or in a pre-specified subpopulation. Based on a decision theoretic framework we derive optimized trial designs by maximizing utility functions. Features to be optimized include the sample size and the population in which the trial is performed (the full population or the targeted subgroup only) as well as the underlying multiple test procedure. The approach accounts for prior knowledge of the efficacy of the drug in the considered populations using a two dimensional prior distribution. The considered utility functions account for the costs of the clinical trial as well as the expected benefit when demonstrating efficacy in the different subpopulations. We model utility functions from a sponsor’s as well as from a public health perspective, reflecting actual civil interests. Examples of optimized trial designs obtained by numerical optimization are presented for both perspectives.

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Section: Introductionsupporting

confidence: 52%

Optimizing Trial Designs for Targeted Therapies

Ondra¹,

Jobjörnsson²,

Beckman³

et al. 2016

PLoS ONE

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show abstract

“…If the AED cannot be stopped for compelling efficacy after stage 1, a decision must be made whether to stop for futility or to continue to stage 2 with one or both populations. Decision criteria may be based on the observed treatment effect in S and C (and/or F ) after stage 1 (eg, 11 and our case study), conditional or predictive power (eg, 18 ), or Bayesian decision theory (eg, 19‐21 ). While type I error control is guaranteed regardless of how these choices are made, the decision criteria affect the probability of correct decisions after stage 1 and study power.…”

Section: Adaptive Enrichment Designs With Binary Endpointsmentioning

confidence: 99%

A pragmatic adaptive enrichment design for selecting the right target population for cancer immunotherapies

Duc

Heinzmann

Berge

et al. 2020

Pharmaceutical Statistics

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Summary One of the challenges in the design of confirmatory trials is to deal with uncertainties regarding the optimal target population for a novel drug. Adaptive enrichment designs (AED) which allow for a data‐driven selection of one or more prespecified biomarker subpopulations at an interim analysis have been proposed in this setting but practical case studies of AEDs are still relatively rare. We present the design of an AED with a binary endpoint in the highly dynamic setting of cancer immunotherapy. The trial was initiated as a conventional trial in early triple‐negative breast cancer but amended to an AED based on emerging data external to the trial suggesting that PD‐L1 status could be a predictive biomarker. Operating characteristics are discussed including the concept of a minimal detectable difference, that is, the smallest observed treatment effect that would lead to a statistically significant result in at least one of the target populations at the interim or the final analysis, respectively, in the setting of AED.

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“…Recently, Song [77] demonstrated application of the adaptive enrichment design of Wang et al [41] and a Bayesian extension utilizing the prediction methods of Huang et al [78] to second and first line trials in hepatocellular carcinoma, respectively, where the latter study utilized an earlier endpoint to predict longer-term responses. Krisam and Kieser [79] extended the adaptive enrichment approach of Jenkins et al [49] to the setting of a binary endpoint, deriving optimal decision rules by considering Bayes’ risk.…”

Section: A Movement Toward Adaptive Designsmentioning

confidence: 99%

Clinical trial designs incorporating predictive biomarkers

Renfro

Mallick

et al. 2016

Cancer Treatment Reviews

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Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer “targeted” drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research.

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Optimal Decision Rules for Biomarker-Based Subgroup Selection for a Targeted Therapy in Oncology

Cited by 10 publications

References 20 publications

Optimizing Trial Designs for Targeted Therapies

Optimizing Trial Designs for Targeted Therapies

A pragmatic adaptive enrichment design for selecting the right target population for cancer immunotherapies

Clinical trial designs incorporating predictive biomarkers

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