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Advancing a Framework for Regulatory Use of Real-World Evidence: When Real Is Reliable.

ABSTRACT: There is growing interest in regulatory use of randomized pragmatic trials and noninterventional real-world (RW) studies of effectiveness and safety, but there is no agreed-on framework for assessing when this type of evidence is sufficiently reliable. Rather than impose a clinical trial-like paradigm on RW evidence, like blinded treatments or complete, source-verified data, the framework for assessing the utility of RW evidence should be grounded in the context of specific study objectives, clinical events that are likely to be detected in routine care, and the extent to which systematic error (bias) is likely to impact effect estimation. Whether treatment is blinded should depend on how well the outcome can be measured objectively. Qualification of a data source should be based on (1) numbers of patients of interest available for study; (2) if "must-have" data are likely to be recorded, and if so, how and where; (3) the accessibility of systematic follow-up data for the time period of interest; and (4) the potential for systematic errors (bias) in data collection and the likely magnitude of any such bias. Accessible data may not be representative of an entire population, but still may provide reliable evidence about the experience of typical patients treated under conditions of conventional care. Similarly, RW data that falls short of optimal length of follow-up or study size may still be useful in terms of its ability to provide evidence for regulators for subgroups of special interest. Developing a framework to qualify RW evidence in the context of a particular study purpose and data asset will enable broader regulatory use of RW data for approval of new molecular entities and label changes. Reliable information about diverse populations and settings should also help us move closer to more affordable, effective health care.

SUBMITTER: Dreyer NA

PROVIDER: S-EPMC5944086 | biostudies-other | 2018 May

REPOSITORIES: biostudies-other

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Advancing a Framework for Regulatory Use of Real-World Evidence: When Real Is Reliable.

Dreyer Nancy A NA

Therapeutic innovation & regulatory science 20180319 3

There is growing interest in regulatory use of randomized pragmatic trials and noninterventional real-world (RW) studies of effectiveness and safety, but there is no agreed-on framework for assessing when this type of evidence is sufficiently reliable. Rather than impose a clinical trial-like paradigm on RW evidence, like blinded treatments or complete, source-verified data, the framework for assessing the utility of RW evidence should be grounded in the context of specific study objectives, cli ...[more]

PMID: 29714575

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Project description:Introduction Real-world evidence is important in regulatory and funding decisions. Manual data extraction from electronic health records (EHRs) is time-consuming and challenging to maintain. Automated extraction using natural language processing (NLP) and artificial intelligence may facilitate this process. Whereas NLP offers a faster solution than manual methods of extraction, the validity of extracted data remains in question. The current study compared manual and automated data extraction from the EHR of patients with advanced lung cancer. Methods Previously, we extracted EHRs from 1209 patients diagnosed with advanced lung cancer (stage IIIB or IV) between January 2015 and December 2017 at Princess Margaret Cancer Centre (Toronto, Canada) using the commercially available artificial intelligence engine, DARWEN (Pentavere, Ontario, Canada). For comparison, 100 of 333 patients that received systemic therapy were randomly selected and clinical data manually extracted by two trained abstractors using the same accepted gold standard feature definitions, including patient, disease characteristics, and treatment data. All cases were re-reviewed by an expert adjudicator. Accuracy and concordance between automated and manual methods are reported. Results Automated extraction required considerably less time (<1 day) than manual extraction (∼225 person-hr). The collection of demographic data (age, sex, diagnosis) was highly accurate and concordant with both methods (96%–100%). Accuracy (for either extraction approach) and concordance were lower for unstructured data elements in EHR, such as performance status, date of diagnosis, and smoking status (NLP accuracy: 88%–94%; Manual accuracy: 78%–94%; concordance: 71%–82%). Concurrent medications (86%–100%) and comorbid conditions (96%–100%), were reported with high accuracy and concordance. Treatment details were also accurately captured with both methods (84%–100%) and highly concordant (83%–99%). Detection of whether biomarker testing was performed was highly accurate and concordant (96%–98%), although detection of biomarker test results was more variable (accuracy 84%–100%, concordance 84%–99%). Features with syntactic or semantic variation requiring clinical interpretation were extracted with slightly lower accuracy by both NLP and manual review. For example, metastatic sites were more accurately identified through NLP extraction (NLP: 88%–99%; manual: 71%–100%; concordance: 70%–99%) with the exception of lung and lymph node metastases (NLP: 66%–71%; manual: 87%–92%; concordance: 58%) owing to analogous terms used in radiology reports not being included in the accepted gold standard definition. Conclusions Automated data abstraction from EHR is highly accurate and faster than manual abstraction. Key challenges include poorly structured EHR and the use of analogous terms beyond the accepted gold standard definition. The application of NLP can facilitate real-world evidence studies at a greater scale than could be achieved with manual data extraction

Dataset Information

Advancing a Framework for Regulatory Use of Real-World Evidence: When Real Is Reliable.

Publications

Advancing a Framework for Regulatory Use of Real-World Evidence: When Real Is Reliable.

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