Treatment effectiveness in a rare oncology indication: Lessons from an external control cohort study

Oksen, Dina; Prince, Patricia; Boutmy, Emmanuelle; Garry, Elizabeth M.; Estrin, Adina; Johne, Andreas; Verpillat, Patrice; Gatto, Nicolle M.

doi:10.1111/cts.13315

Cited by 7 publications

(6 citation statements)

References 38 publications

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“…Conducting a SAT with a concurrent prospective EC (Fig 2A) can be developed under the pragmatic trial framework, 107 with the advantage of improved external data quality compared with precollected data. 35,61 Prespecification of the study population, clinical assessments, and outcomes of EC will likely improve the degree of matching with the SAT. Prospective data collection ensures contemporaneous data and minimizes time-related biases, and it also promotes the development of disease registries and databases, which will benefit future research.…”

Section: Innovative Clinical Trial Designs Leveraging Ecmentioning

confidence: 99%

Framework for the Use of External Controls to Evaluate Treatment Outcomes in Precision Oncology Trials

Siu,

Lin,

Cho

et al. 2024

JCO Precis Oncol

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Advances in genomics have enabled anticancer therapies to be tailored to target specific genomic alterations. Single-arm trials (SATs), including those incorporated within umbrella, basket, and platform trials, are widely adopted when it is not feasible to conduct randomized controlled trials in rare biomarker-defined subpopulations. External controls (ECs), defined as control arm data derived outside the clinical trial, have gained renewed interest as a strategy to supplement evidence generated from SATs to allow comparative analysis. There are increasing examples demonstrating the application of EC in precision oncology trials. The prospective application of EC in conducting comparative studies is associated with distinct methodological challenges, the specific considerations for EC use in biomarker-defined subpopulations have not been adequately discussed, and a formal framework is yet to be established. In this review, we present a framework for conducting a prospective comparative analysis using EC. Key steps are (1) defining the purpose of using EC to address the study question, (2) determining if the external data are fit for purpose, (3) developing a transparent study protocol and a statistical analysis plan, and (iv) interpreting results and drawing conclusions on the basis of a prespecified hypothesis. We specify the considerations required for the biomarker-defined subpopulations, which include (1) specifying the comparator and biomarker status of the comparator group, (2) defining lines of treatment, (3) assessment of the biomarker testing panels used, and (4) assessment of cohort stratification in tumor-agnostic studies. We further discuss novel clinical trial designs and statistical techniques leveraging EC to propose future directions to advance evidence generation and facilitate drug development in precision oncology.

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Section: Innovative Clinical Trial Designs Leveraging Ecmentioning

confidence: 99%

Framework for the Use of External Controls to Evaluate Treatment Outcomes in Precision Oncology Trials

Siu,

Lin,

Cho

et al. 2024

JCO Precis Oncol

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“…Once the data source(s) has been selected, eligibility criteria from the trial cohort should be applied to the RWD-derived external control to the extent possible, as this will support comparability (although additional adjustments may be needed to balance confounders following application of eligibility criteria). 47 It is important that the definition of patient populations be informed by the specific research question to facilitate drawing external control patients from the same source population as the reference trial. This approach may enable better balance in the baseline characteristics between the two populations; however, there are many challenges to implementing strict inclusion/ exclusion criteria in practice.…”

Section: Reviewmentioning

confidence: 99%

Regulatory and HTA Considerations for Development of Real‐World Data Derived External Controls

Curtis

Solà-Morales

Heidt

et al. 2023

Clin Pharma and Therapeutics

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Regulators and Health Technology Assessment (HTA) bodies are increasingly familiar with, and publishing guidance on, external controls derived from real‐world data (RWD) to generate real‐world evidence (RWE). We recently conducted a systematic literature review (SLR) evaluating publicly available information on the use of RWD‐derived external controls to contextualize outcomes from uncontrolled trials submitted to the European Medicines Agency (EMA), the US Food and Drug Administration (FDA), and/or select HTA bodies. The review identified several key operational and methodological aspects for which more detailed guidance and alignment within and between regulatory agencies and HTA bodies is necessary. This paper builds on the SLR findings by delineating a set of key takeaways for the responsible generation of fit‐for‐purpose RWE. Practical methodological and operational guidelines for designing, conducting, and reporting RWD‐derived external control studies are explored and discussed. These considerations include: (i) early engagement with regulators and HTA bodies during the study planning phase; (ii) consideration of the appropriateness and comparability of external controls across multiple dimensions, including eligibility criteria, temporality, population representation, and clinical evaluation; (iii) ensuring adequate sample sizes, including hypothesis testing considerations; (iv) implementation of a clear and transparent strategy for assessing and addressing data quality, including data missingness across trials and RWD; (v) selection of comparable and meaningful endpoints that are operationalized and analyzed using appropriate analytic methods; and (vi) conduct of sensitivity analyses to assess the robustness of findings in the context of uncertainty and sources of potential bias.

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Section: Introductionmentioning

confidence: 99%

“…In an attempt to adjust for confounders, analyses have often focused on recreating the inclusion criteria of clinical trials in real-world cohorts [8][9][10]14,16,[18][19][20][21][22][23][24][25][26] . The most common method is patient matching based on propensity score 27 .…”

Section: Introductionmentioning

confidence: 99%

“…Then, the real-world data cohort is adjusted by including only patients with high propensity score. Propensity score approaches, including variations or adaptation of this method, still constitute the main proposed technique for adjusting for confounders in real-world cohorts based on the conditions of control trial arms 8,10,16,19,21,22,24,26,29,30 . However, propensity score approaches have been shown to be limited in this aspect since a probabilistic empirical approach is highly sensitive to undetected confounders and biases of the data 1,8,10,11,14,25 .…”

Section: Introductionmentioning

confidence: 99%

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Exploring the discrepancies between clinical trials and real-world data by accounting for Selection criteria, Operations, and Measurements of Outcome

Marzano,

Darwich,

Dan

et al. 2024

Preprint

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The potential of real-world data to inform clinical trial design and supplement control arms has gained much interest in recent years. The most common approach relies on matching real-world patient cohorts to clinical trial baseline covariates using propensity score techniques. However, recent studies pointed out that there is a lack of replicability, generalisability, and consensus. Further, few studies consider differences in operational processes. Systematically accounting for confounders, including hidden effects related to the clinical treatment process and clinical trial study protocol, would potentially allow for improved translation between clinical trial and real-world data and enable learning across translational activities. In this paper, we propose an approach that aims to explore and examine these confounders by investigating the impact of selection criteria and operations on the measurements of outcome. We tested the approach on small cell lung cancer patients receiving platinum-based chemotherapy regimens (n=1,224). The results showed that the discrepancy between real-world and clinical trial data potentially depends on differences in covariate characteristics and operations (e.g., censoring mechanism, the process of pre-trial patient selection related to ECOG-performance status 2 patients). This work builds on current approaches and suggests areas of improvement for systematically accounting for differences in outcomes between study cohorts. Continued development of the method presented here could pave the way for transferring learning across studies and developing mutual translation between the real-world and clinical trials to inform future studies design.

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Treatment effectiveness in a rare oncology indication: Lessons from an external control cohort study

Cited by 7 publications

References 38 publications

Framework for the Use of External Controls to Evaluate Treatment Outcomes in Precision Oncology Trials

Framework for the Use of External Controls to Evaluate Treatment Outcomes in Precision Oncology Trials

Regulatory and HTA Considerations for Development of Real‐World Data Derived External Controls

Exploring the discrepancies between clinical trials and real-world data by accounting for Selection criteria, Operations, and Measurements of Outcome

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