Synthetic control arms in studies of multiple myeloma and diffuse large B‐cell lymphoma

Banerjee, Rahul; Midha, Shonali; Kelkar, Amar H.; Goodman, Aaron M.; Prasad, Vinay; Mohyuddin, Ghulam Rehman

doi:10.1111/bjh.17945

Cited by 12 publications

(8 citation statements)

References 25 publications

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“…The use of suboptimal or inferior control in oncology trials has been increasingly concerning in recent years [ 5 – 7 , 10 , 11 , 34 ]. Per the Declaration of Helsinki , clinical trials, whether initiated by an investigator or for registration purposes, should be conducted under prior ethical approval [ 4 ].…”

Section: Discussionmentioning

confidence: 99%

Use of suboptimal control arms in randomized clinical trials of investigational cancer drugs in China, 2016–2021: An observational study

Zhang,

Chen,

Cheng

et al. 2023

PLoS Med

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Background The use of suboptimal controls in randomized trials of new cancer drugs can produce potentially unreliable clinical efficacy results over the current standard of care and expose patients to substandard therapy. We aim to investigate the proportion of randomized trials of investigational cancer drugs that used a suboptimal control arm and the number of trial participants at risk of exposure to suboptimal treatments in China. The association between the use of a suboptimal control and concluding statistical significance on the primary endpoint was also examined. Methods and findings This observational study included randomized controlled trials (RCTs) of cancer drugs that were authorized by specific Chinese institutional review boards between 2016 and 2021, supporting investigational new drug applications of these drugs in China. The proportion of trials that used a suboptimal control arm and the total number of trial participants at risk of exposure to suboptimal treatments were calculated. In a randomized trial for a specific condition, a comparator was deemed suboptimal if it was not recommended by clinical guidelines published in priori or if there existed a regimen with a higher level of recommendation for the indication. The final sample included 453 Phase II/III and Phase III randomized oncology trials. Overall, 60 trials (13.2%) adopted a suboptimal control arm. Among them, 58.3% (35/60) used comparators that were not recommended by a prior guideline for the indication. The cumulative number of trial participants at risk of exposure to suboptimal treatments totaled 18,610 by the end of 2021, contributing 15.1% to the total number of enrollees of all sampled RCTs in this study. After adjusting for the year of ethical approval, region of participant recruitment, line of therapy, and cancer site, second-line therapies (adjusted odds ratio [aOR] = 2.7, 95%CI [1.2, 5.9]), adjuvant therapies (aOR = 8.9, 95% CI [3.4, 23.1]), maintenance therapies (aOR = 5.2, 95% CI [1.6, 17.0]), and trials recruiting participants in China only (aOR = 4.1, 95% CI [2.1, 8.0]) were more likely to adopt a suboptimal control. For the 105 trials with publicly available results, no statistically significant difference was observed between the use of a suboptimal control and concluding positive on the primary endpoint (100.0% [12/12] versus 83.9% [78/93], p = 0.208). The main limitation of this study is its reliance on clinical guidelines that could vary across cancer types and time in assessing the quality of the control groups. Conclusions In this study, over one-eighth of randomized trials of cancer drugs registered to apply for regulatory approval in China used a suboptimal comparator. Our results highlight the necessity to refine the design of randomized trials to generate optimal clinical evidence for new cancer therapies.

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Section: Discussionmentioning

confidence: 99%

Use of suboptimal control arms in randomized clinical trials of investigational cancer drugs in China, 2016–2021: An observational study

Zhang,

Chen,

Cheng

et al. 2023

PLoS Med

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“…There could be some ways of improving the value of data and thus increasing the utility of single-arm trials. 63 Thus, to decrease the uncertainty of such uncontrolled trials, comparisons using external controls have been growingly reported in oncohematology, for instance, in acute lymphoblastic leukemia, 15 large B-cell lymphoma, 64 anaplastic lymphoma, 17 follicular lymphoma, 18 metastatic nonsmall-cell lung cancer, 65 endometrial cancer, 16 and glioblastoma. 66 Such indirect comparisons require a complex implementation to be valid, as recently reported.…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

Enriching single-arm clinical trials with external controls: possibilities and pitfalls

et al. 2023

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For the past decade, it has become commonplace to provide rapid answers and early patient access to innovative treatments in the absence of randomized clinical trials (RCT), with benefits estimated from single-arm trials. This trend is important in oncology, notably when assessing new targeted therapies. Some of those uncontrolled trials further include an external/synthetic control group as an innovative way to provide an indirect comparison to a pertinent control group. We aimed to provide some guidelines as a comprehensive tool for critical appraisal of those comparisons or for performing one. We used the example of ciltacabtagene autoleucel for the treatment of adult patients with relapsed or refractory multiple myeloma after three or more treatment lines as an illustrative example. A 3-step guidance is proposed. The first step includes the definition of an estimand, which encompasses the treatment effect and targeted population (whole population or restricted to single-arm trial or external controls), reflecting a clinical question. The second step relies on the adequate selection of external controls from previous RCTs or real-world data from patient cohorts, registries, or electronic patient files. The third step consists of choosing the statistical approach targeting the treatment effect defined above, and depends on the available data (individual-level data or aggregated external data). The validity of the treatment effect derived from indirect comparisons heavily depends on careful methodological considerations included in the proposed 3-step procedure. Because the level of evidence of a well-conducted RCT cannot be guaranteed, the evaluation is more important than in standard settings.

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“…Synthetic data, which have been used in clinical simulation tests for AI models in diverse settings, aid in identifying and rectifying potential errors before deployment, thereby minimizing “alert fatigue” and bolstering trust in AI models [ 132 ]. Synthetic control arms utilizing external patient-level data are efficient and ethical alternatives that can be used in clinical trials to save resources [ 133 134 ].…”

Section: Potential and Application Of Generative Models In Clinical I...mentioning

confidence: 99%

Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals

Kim,

Cho,

Jang

et al. 2024

Korean J Radiol

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The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.

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Synthetic control arms in studies of multiple myeloma and diffuse large B‐cell lymphoma

Cited by 12 publications

References 25 publications

Use of suboptimal control arms in randomized clinical trials of investigational cancer drugs in China, 2016–2021: An observational study

Use of suboptimal control arms in randomized clinical trials of investigational cancer drugs in China, 2016–2021: An observational study

Enriching single-arm clinical trials with external controls: possibilities and pitfalls

Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals

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