Quantitative Bias Analysis in Regulatory Settings

Lash, Timothy L.; Fox, Matthew P.; Cooney, Darryl; Lu, Yun; Forshee, Richard A.

doi:10.2105/ajph.2016.303199

Cited by 38 publications

(37 citation statements)

References 18 publications

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“…Research using electronic databases are essential to US vaccine safety, and improved methods for quantifying and communicating about uncertainty in this line of research are needed. 43 While our simulations were conducted in the context of immunization schedule safety research, our findings are broadly applicable to other EHRbased pharmacoepidemiological research. Our results serve to encourage researchers to acknowledge the potential for misclassification bias in EHR-based studies, and to use quantitative techniques for identifying, measuring, and correcting this bias.…”

Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

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Bias from outcome misclassification in immunization schedule safety research

Newcomer

Kulldorff

et al. 2018

Pharmacoepidemiology and Drug

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Bias from outcome misclassification in immunization schedule safety research

Newcomer

Kulldorff

et al. 2018

Pharmacoepidemiology and Drug

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show abstract

“…There are even methods to eliminate the need for both database reorganization and semantic mapping [31]. While these approaches may be more flexible and avoid cumbersome ETL and/or mapping processes, it is unclear how they fare with respect to the sensitivity and specificity of their exposure and outcome definitions making it challenging to understand or assess bias in their results [32, 33].…”

Section: Discussionmentioning

confidence: 99%

The Generalized Data Model for clinical research

Danese¹,

Halperin²,

Duryea³

et al. 2019

BMC Med Inform Decis Mak

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Background Most healthcare data sources store information within their own unique schemas, making reliable and reproducible research challenging. Consequently, researchers have adopted various data models to improve the efficiency of research. Transforming and loading data into these models is a labor-intensive process that can alter the semantics of the original data. Therefore, we created a data model with a hierarchical structure that simplifies the transformation process and minimizes data alteration. Methods There were two design goals in constructing the tables and table relationships for the Generalized Data Model (GDM). The first was to focus on clinical codes in their original vocabularies to retain the original semantic representation of the data. The second was to retain hierarchical information present in the original data while retaining provenance. The model was tested by transforming synthetic Medicare data; Surveillance, Epidemiology, and End Results data linked to Medicare claims; and electronic health records from the Clinical Practice Research Datalink. We also tested a subsequent transformation from the GDM into the Sentinel data model. Results The resulting data model contains 19 tables, with the Clinical Codes, Contexts, and Collections tables serving as the core of the model, and containing most of the clinical, provenance, and hierarchical information. In addition, a Mapping table allows users to apply an arbitrarily complex set of relationships among vocabulary elements to facilitate automated analyses. Conclusions The GDM offers researchers a simpler process for transforming data, clear data provenance, and a path for users to transform their data into other data models. The GDM is designed to retain hierarchical relationships among data elements as well as the original semantic representation of the data, ensuring consistency in protocol implementation as part of a complete data pipeline for researchers. Electronic supplementary material The online version of this article (10.1186/s12911-019-0837-5) contains supplementary material, which is available to authorized users.

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“…In other areas of epidemiology, there is increased demand for bias assessment to be integrated at both the peer review and regulatory level [54,55], as QBA quantifies the possible magnitude, direction and uncertainty around the bias for decision makers. Regulators commonly request additional data or analysis to address bias in studies making use of external comparators (see Box 3) and QBA can be used to address this need [25,56].…”

Section: Step 3: Assessing the Threat Of Bias Via Quantitative Bias Amentioning

confidence: 99%

A Framework for Methodological Choice and Evidence Assessment for Studies Using External Comparators from Real-World Data

et al. 2020

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Several approaches have been proposed recently to accelerate the pathway from drug discovery to patient access. These include novel designs such as using controls external to the clinical trial where standard randomised controls are not feasible. In parallel, there has been rapid growth in the application of routinely collected healthcare 'real-world' data for post-market safety and effectiveness studies. Thus, using real-world data to establish an external comparator arm in clinical trials is a natural next step. Regulatory authorities have begun to endorse the use of external comparators in certain circumstances, with some positive outcomes for new drug approvals. Given the potential to introduce bias associated with observational studies, there is a need for recommendations on how external comparators should be best used. In this article, we propose an evaluation framework for real-world data external comparator studies that enables full assessment of available evidence and related bias. We define the principle of exchangeability and discuss the applicability of criteria described by Pocock for consideration of the exchangeability of the external and trial populations. We explore how trial designs using real-world data external comparators fit within the evidence hierarchy and propose a four-step process for good conduct of external comparator studies. This process is intended to maximise the quality of evidence based on careful study design and the combination of covariate balancing, bias analysis and combining outcomes.

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Quantitative Bias Analysis in Regulatory Settings

Cited by 38 publications

References 18 publications

Bias from outcome misclassification in immunization schedule safety research

Bias from outcome misclassification in immunization schedule safety research

The Generalized Data Model for clinical research

A Framework for Methodological Choice and Evidence Assessment for Studies Using External Comparators from Real-World Data

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