A two‐stage Bayesian design for co‐development of new drugs and companion diagnostics

Karuri, Stella W.; Simon, Richard

doi:10.1002/sim.4462

Cited by 35 publications

(17 citation statements)

References 25 publications

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“…A Bayesian version of the adaptive enrichment design originally proposed by Wang et al [41] that allows for formal specification of prior confidence in a biomarker’s predictive ability was described by Karuri and Simon [76]. 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.…”

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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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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“…In Karuri and Simon (18) we introduced a phase III design for this setting in which futility monitoring of the test negative patients is performed based on a joint prior joint distribution for the treatment effects in test negative and test positive patients. That prior distribution enables the trial investigator to represent the prior evidence that treatment effect will be reduced for test negative patients and use that information in monitoring the clinical trial.…”

Section: Biomarker Stratified Designmentioning

confidence: 99%

Drug-Diagnostics Co-Development in Oncology

Jørgensen

2013

Front. Oncol.

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Developments in genomics are providing a biological basis for the heterogeneity of clinical course and response to treatment that have long been apparent to clinicians. The ability to molecularly characterize human diseases presents new opportunities to develop more effective treatments and new challenges for the design and analysis of clinical trials. In oncology, treatment of broad populations with regimens that benefit a minority of patients is less economically sustainable with expensive molecularly targeted therapeutics. The established molecular heterogeneity of human diseases requires the development of new paradigms for the design and analysis of randomized clinical trials as a reliable basis for predictive medicine. We review prospective designs for the development of new therapeutics and predictive biomarkers to inform their use. We cover designs for a wide range of settings. At one extreme is the development of a new drug with a single candidate biomarker and strong biological evidence that marker negative patients are unlikely to benefit from the new drug. At the other extreme are phase III clinical trials involving both genome-wide discovery of a predictive classifier and internal validation of that classifier. We have outlined a prediction based approach to the analysis of randomized clinical trials that both preserves the type I error and provides a reliable internally validated basis for predicting which patients are most likely or unlikely to benefit from a new regimen.

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“…Due to heterogeneity of the study population, the conclusion of a positive (or negative) study on an average sense does not guarantee that the new therapy benefits (or does not benefit) uniformly all patients in the study population (Rothwell, ; Rothwell et al, ; Kent and Hayward, ). Recently various enrichment strategies were suggested and implemented in comparative trials (Freidlin and Simon, ; Jiang et al, ; Wang et al, ; Simon, ; Karuri and Simon, ; U.S. Food and Drug Administration, ). For predictive enrichment, the idea is to apply a systematic, pre‐specified procedure to identify and validate a subpopulation whose patients would significantly benefit from the new therapy both clinically and statistically.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Enrichment Procedure to Identify Potential Responders to a New Therapy for Randomized, Comparative Controlled Clinical Studies

Zhao

Tian

et al. 2015

Biometrics

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Summary To evaluate a new therapy versus a control via a randomized, comparative clinical study or a series of trials, due to heterogeneity of the study patient population, a pre-specified, predictive enrichment procedure may be implemented to identify an “enrichable” subpopulation. For patients in this subpopulation, the therapy is expected to have a desirable overall risk-benefit profile. To develop and validate such a “therapy-diagnostic co-development” strategy, a three-step procedure may be conducted with three independent data sets from a series of similar studies or a single trial. At the first stage, we create various candidate scoring systems based on the baseline information of the patients via, for example, parametric models using the first data set. Each individual score reflects an anticipated average treatment difference for future patients who share similar baseline profiles. A large score indicates that these patients tend to benefit from the new therapy. At the second step, a potentially promising, enrichable subgroup is identified using the totality of evidence from these scoring systems. At the final stage, we validate such a selection via two-sample inference procedures for assessing the treatment effectiveness statistically and clinically with the third data set, the so-called holdout sample. When the study size is not large, one may combine the first two steps using a “cross-training-evaluation” process. Comprehensive numerical studies are conducted to investigate the operational characteristics of the proposed method. The entire enrichment procedure is illustrated with the data from a cardiovascular trial to evaluate a beta-blocker versus a placebo for treating chronic heart failure patients.

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A two‐stage Bayesian design for co‐development of new drugs and companion diagnostics

Cited by 35 publications

References 25 publications

Clinical trial designs incorporating predictive biomarkers

Clinical trial designs incorporating predictive biomarkers

Drug-Diagnostics Co-Development in Oncology

A Predictive Enrichment Procedure to Identify Potential Responders to a New Therapy for Randomized, Comparative Controlled Clinical Studies

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