A Review of Causal Inference for External Comparator Arm Studies

Rippin, Gerd; Ballarini, Nicolás; Sanz, Héctor; Largent, Joan; Quinten, Chantal; Pignatti, Francesco

doi:10.1007/s40264-022-01206-y

Cited by 12 publications

(14 citation statements)

References 146 publications

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“…Conservativeness should be built into sample size determination for an EC study in order to account for different factors that may reduce the actual sample size available for analysis. For instance, a range of plausible effect sizes should be considered, and an additional percentage added to any calculated sample sizes to account for expected attrition due to the matching or weighting procedures used (e.g., exclusion of unmatched patients) (Rippin et al, 2022). Stricter levels of significance might also be considered in the sample size calculation to account for added variability from combining RW data with SATs or RCTs (e.g., by considering a type I error of 1% instead of 5%).…”

Section: Sample Sizementioning

confidence: 99%

Application of the target trial emulation framework to external comparator studies

Arnold,

Antunes,

Coles

et al. 2024

Front. Drug Saf. Regul.

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External comparator (EC) studies are increasingly being used to generate evidence that supports the evaluation of emerging pharmacological treatments for regulatory and health technology assessment (HTA) purposes. However, the reliability of evidence generated from EC studies can vary. In this paper, we outline how an existing framework for causal inference, the target trial emulation (TTE) framework, can be appropriately applied to improve the design and analysis of EC studies. Applying the TTE framework involves specifying the protocol of an ideal target trial which would answer the causal question of interest, then emulating its key elements under real-world (RW) settings. We describe each component of the original TTE framework and explain how it can be applied to EC studies, supplemented with practical recommendations. We also highlight special considerations and limitations in applying the TTE framework to EC studies. We describe how the TTE framework can be applied to improve the clarity, transparency, and reliability of evidence generated from EC studies.

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Section: Sample Sizementioning

confidence: 99%

Application of the target trial emulation framework to external comparator studies

Arnold,

Antunes,

Coles

et al. 2024

Front. Drug Saf. Regul.

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“…Specifically, statistical power can be maximized in comparative studies by selecting an analytic method (e.g., IPTW) that allows researchers to leverage information for all patients included in the sample. A recent publication by Rippin and colleagues (2022) 59 outlines key considerations for determining the appropriate external control sample size. Of particular interest to sponsors is the recommendation to have an external control arm which is substantially larger in terms of patient number than the reference trial (e.g., in the range of 1:2–1:5) depending on feasibility considerations for rare diseases.…”

Section: Key Takeawaysmentioning

confidence: 99%

“…Regulatory agencies in particular may conduct their own sensitivity analyses wherein they change the covariates included in the PS model. Assessing covariate balance following PS matching or IPTW often involves checking whether standardized mean differences are < 0.1, 59 calculated as the absolute value in the difference in means of a covariate across the treatment groups, divided by the standard deviation in the treated group 72 . Larger standardized biases indicate that groups are too different from one another for reliable comparison.…”

Section: Key Takeawaysmentioning

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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“…infeasibility may not support regular double-blinded placebo-controlled RCTs. [11][12][13][14][15] Moreover, by reducing or eliminating the need to enroll control patients for two RCT arms, an ECA can also increase e ciency, reduce delays, and lower costs in the evaluation of new therapies.…”

Section: Introductionmentioning

confidence: 99%

Utilization of anonymization techniques to create an external control arm for clinical trial data

Mehtälä¹,

Ali²,

Miettinen³

et al. 2023

Preprint

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Background Subject-level real-world data (RWD) collected during daily healthcare practices are increasingly used in medical research to assess questions that cannot be addressed in the context of a randomized controlled trial (RCT). A novel application of RWD arises from the need to create external control arms (ECAs) for single-arm RCTs. In the analysis of ECAs against RCT data, there is an evident need to manage and analyze RCT data and RWD in the same technical environment. In the Nordic countries, legal requirements may require that the original subject-level data be anonymized, i.e., modified so that the risk to identify any individual is minimal. The aim of this study was to investigate and compare how well pseudonymized and anonymized RWD perform in the creation of an ECA for an RCT. Methods This was a hybrid observational cohort study using clinical data from the control arm of the completed randomized phase II clinical trial (PACIFIC-AF) and RWD cohort from Finnish healthcare data sources. The initial pseudonymized RWD were anonymized within the (k, ε)-anonymity framework (a model for protecting individuals against identification). Propensity score matching and weighting methods were applied to the anonymized and pseudonymized RWD, to balance potential confounders against the RCT data. Descriptive statistics for the potential confounders and overall survival analyses were conducted prior to and after matching and weighting, using both the pseudonymized and anonymized RWD sets. Results Anonymization affected the baseline characteristics of potential confounders only marginally. The greatest difference was in the prevalence of chronic obstructive pulmonary disease (4.6% vs. 5.4% in the pseudonymized compared to the anonymized data, respectively). Moreover, the overall survival changed in anonymization by only 8% (95% CI 4–22%). Both the pseudonymized and anonymized RWD were able to produce matched ECAs for the RCT data. Anonymization after matching impacted overall survival analysis by 22% (95% CI -21–87%). Conclusions Anonymization is a viable technique for cases where flexible data transfer and sharing are required. However, as anonymization necessarily affects some aspects of the original data, careful consideration of anonymization strategy is recommended.

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A Review of Causal Inference for External Comparator Arm Studies

Cited by 12 publications

References 146 publications

Application of the target trial emulation framework to external comparator studies

Application of the target trial emulation framework to external comparator studies

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

Utilization of anonymization techniques to create an external control arm for clinical trial data

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