Multiple Self-Controlled Case Series for Large-Scale Longitudinal Observational Databases

Simpson, Shawn E.; Madigan, David; Zorych, Ivan; Schuemie, Martijn J.; Ryan, Patrick B.; Suchard, Marc A.

doi:10.1111/biom.12078

Cited by 38 publications

(48 citation statements)

References 28 publications

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“…Queries are distributed and run locally, and only aggregate results are returned centrally. OHDSI develops new methods to analyze observational data, such as algorithms to minimize confounding (20) and methods to calibrate significance tests (21). They are implemented as an open-source set of tools that can be used by observational researchers around the world.…”

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confidence: 99%

Characterizing treatment pathways at scale using the OHDSI network

Hripcsak

Ryan

Duke

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Observational research promises to complement experimental research by providing large, diverse populations that would be infeasible for an experiment. Observational research can test its own clinical hypotheses, and observational studies also can contribute to the design of experiments and inform the generalizability of experimental research. Understanding the diversity of populations and the variance in care is one component. In this study, the Observational Health Data Sciences and Informatics (OHDSI) collaboration created an international data network with 11 data sources from four countries, including electronic health records and administrative claims data on 250 million patients. All data were mapped to common data standards, patient privacy was maintained by using a distributed model, and results were aggregated centrally. Treatment pathways were elucidated for type 2 diabetes mellitus, hypertension, and depression. The pathways revealed that the world is moving toward more consistent therapy over time across diseases and across locations, but significant heterogeneity remains among sources, pointing to challenges in generalizing clinical trial results. Diabetes favored a single first-line medication, metformin, to a much greater extent than hypertension or depression. About 10% of diabetes and depression patients and almost 25% of hypertension patients followed a treatment pathway that was unique within the cohort. Aside from factors such as sample size and underlying population (academic medical center versus general population), electronic health records data and administrative claims data revealed similar results. Large-scale international observational research is feasible.observational research | data network | treatment pathways A learning health system (1) must systematically evaluate the effects of medical interventions to enable evidence-based medical decision-making. Randomized clinical trials serve as the cornerstone for causal evidence about medical products (2, 3), but evidence from these trials may be limited by an insufficient number of persons exposed, insufficient length of exposure, and inadequate coverage of the target population, factors that limit external generalizability. Observational studies can contribute to the larger goal of causal inference at three stages: (i) the design of experiments, such as determining what are the current therapies that should be compared with a new therapy; (ii) the direct testing of clinical hypotheses on observational data (4-8) using methods to correct for nonrandom treatment assignment as part of the effect estimation process; and (iii) better understanding of population characteristics to improve the extrapolation of both observational and experimental results to new groups.Without sufficiently broad databases available in the first stage, randomized trials are designed without explicit knowledge of actual disease status and treatment practice. Literature reviews are restricted to the population choices of previous investigations, and pilot studi...

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confidence: 99%

Characterizing treatment pathways at scale using the OHDSI network

Hripcsak

Ryan

Duke

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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281

222

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“…Like multiple SCCS [9], multiple CSCCS can effectively handle the innocent bystander confounding problem (a.k.a. Simpson's Paradox).…”

Section: Challenges In Ehr Datamentioning

confidence: 99%

“…For this purpose, we extend the Self-Controlled Case Series (SCCS) [9] model that has been widely used in the Adverse Drug Reactions (ADRs) discovery community to handle continuous numeric response, hence the name of our model, Continuous Self-Controlled Case Series (CSCCS).…”

Section: Introductionmentioning

confidence: 99%

Computational Drug Repositioning Using Continuous Self-Controlled Case Series

Kuang

Thomson

Caldwell

et al. 2016

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources. Leveraging the patient-level temporal ordering information between numeric physiological measurements and various drug prescriptions provided in Electronic Health Records (EHRs), we propose a Continuous Self-controlled Case Series (CSCCS) model for CDR. As an initial evaluation, we look for drugs that can control Fasting Blood Glucose (FBG) level in our experiments. Applying CSCCS to the Marshfield Clinic EHR, well-known drugs that are indicated for controlling blood glucose level are rediscovered. Furthermore, some drugs with recent literature support for the potential effect of blood glucose level control are also identified.

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“…neonatal care [10], healthcare costs reduction [11], analysis of antibiotic resistance trends [12], and pharmacovigilance signal detection [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Big Data - Smart Health Strategies

Koutkias¹,

Thiessard²

2014

Yearb Med Inform

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SummaryObjectives: To select best papers published in 2013 in the field of big data and smart health strategies, and summarize outstanding research efforts. Methods: A systematic search was performed using two major bibliographic databases for relevant journal papers. The references obtained were reviewed in a two-stage process, starting with a blinded review performed by the two section editors, and followed by a peer review process operated by external reviewers recognized as experts in the field. Results: The complete review process selected four best papers, illustrating various aspects of the special theme, among them: (a) using large volumes of unstructured data and, specifically, clinical notes from Electronic Health Records (EHRs) for pharmacovigilance; (b) knowledge discovery via querying large volumes of complex (both structured and unstructured) biological data using big data technologies and relevant tools; (c) methodologies for applying cloud computing and big data technologies in the field of genomics, and (d) system architectures enabling high-performance access to and processing of large datasets extracted from EHRs. Conclusions: The potential of big data in biomedicine has been pinpointed in various viewpoint papers and editorials. The review of current scientific literature illustrated a variety of interesting methods and applications in the field, but still the promises exceed the current outcomes. As we are getting closer towards a solid foundation with respect to common understanding of relevant concepts and technical aspects, and the use of standardized technologies and tools, we can anticipate to reach the potential that big data offer for personalized medicine and smart health strategies in the near future. KeywordsBig data, cloud computing, personalized medicine, smart health strategies Yearb Med Inform 2014:48-51 http://dx

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Multiple Self-Controlled Case Series for Large-Scale Longitudinal Observational Databases

Cited by 38 publications

References 28 publications

Characterizing treatment pathways at scale using the OHDSI network

Characterizing treatment pathways at scale using the OHDSI network

Computational Drug Repositioning Using Continuous Self-Controlled Case Series

Big Data - Smart Health Strategies

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