RESEARCH OPPORTUNITIES
Generalizability Network Research Study @aaveritt ’s presentation from the July 10th community call: https://drive.google.com/file/d/1acWhR5FCsTMWL7hxmYCgVlUt6r1bBFm1/view?usp=sharing @aaveritt know by replying to her forum post:Generalizability, Applicability, and Replicability of RCTs: A Study
OHDSI MEETINGS THIS WEEK
OHDSI Community Call - Tuesday at 12pm EThttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M59X2V1U61WC9ASID2Z5N3UT95-D1JL&rnd=811649.98682211121211
Architecture Working Group - Thursday at 10am EThttps://jjconferencing.webex.com/mw3100/mywebex/default.do?service=1&siteurl=jjconferencing&nomenu=true&main_url=%2Fmc3100%2Fe.do%3Fsiteurl%3Djjconferencing%26AT%3DMI%26EventID%3D610982452%26UID%3D501476547%26Host%3DQUhTSwAAAAQu4P1o9qm71JJ1Zj4-uvZbjQttsCinu71JCRxBAHAXnzjjRAiTspTzU9ojLmjMF4CcTBWw4zn1dqYPTWu5vJ9_0%26FrameSet%3D2%26MTID%3Dm3e1ceeca56f1e94c9fcf1ae98c10e02e1
GIS working group meeting - Next Monday (August 20th) at 10am ET
Simple, modern video meetings for the global workforce. Join from anywhere, including your desktop, browser, mobile device, or video room device.
ANNOUNCEMENTS
2018 Collaborator Showcase - Call for participation CLOSED https://www.ohdsi.org/collaborator-showcase/
2018 OHDSI Symposium - REGISTER NOW https://www.ohdsi.org/symposium-registration-2/
2018 OHDSI Symposium - TUTORIAL REGISTRATION OPEN https://www.ohdsi.org/tutorial-workshops/ https://www.ohdsi.org/tutorial-registration-2/
Intro tutorials are being offered free of cost, however registration does not guarantee a seat in the tutorial. When you register, you will be placed on the tutorial wait-list. The final participant list will be determined by the tutorial faculty.
Advanced tutorials also offer the free wait-list registration. In addition, we are also offering a limited number of paid tickets ($318.17) which will guarantee your seat in the tutorial.
Call for Community Feedback Guidelines for starting an OHDSI chapter Guidelines for starting an OHDSI working group
If you’re not trying to be real, you don’t have to get it right. That’s art.
COMMUNITY PUBLICATIONS
Improving reproducibility by using high-throughput observational studies with empirical calibration
MJ Schuemie, PB Ryan, G Hripcsak, D Madigan and MA Suchard,
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences , Sep 2018 13
Concerns over reproducibility in science extend to research using existing healthcare data; many observational studies investigating the same topic produce conflicting results, even when using the same data. To address this problem, we propose a paradigm shift. The current paradigm centres on generating one estimate at a time using a unique study design with unknown reliability and publishing (or not) one estimate at a time. The new paradigm advocates for high-throughput observational studies using consistent and standardized methods, allowing evaluation, calibration and unbiased dissemination to generate a more reliable and complete evidence base. We demonstrate this new paradigm by comparing all depression treatments for a set of outcomes, producing 17 718 hazard ratios, each using methodology on par with current best practice. We furthermore include control hypotheses to evaluate and calibrate our evidence generation process. Results show good transitivity and consistency between databases, and agree with four out of the five findings from clinical trials. The distribution of effect size estimates reported in the literature reveals an absence of small or null effects, with a sharp cut-off at p = 0.05. No such phenomena were observed in our results, suggesting more complete and more reliable evidence.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.
OpenPVSignal: Advancing Information Search, Sharing and Reuse on Pharmacovigilance Signals via FAIR Principles and Semantic Web Technologies
Treatment resistant depression incidence estimates from studies of health insurance databases depend strongly on the details of the operating definition.
D Fife, J Reps, M Soledad Cepeda, P Stang, M Blacketer and J Singh,
Heliyon , Jul 2018
Health services databases provide population-based data that have been used to describe the epidemiology and costs of treatment resistant depression (TRD). This retrospective cohort study estimated TRD incidence and, via sensitivity analyses, assessed the variation of TRD incidence within the range of implementation choices.In three US databases widely used for observational studies, we defined TRD as failure of two medications as evidenced by their replacement or supplementation by other medications, and set maximum durations (caps) for how long a medication regimen could remain in use and still be eligible to fail.TRD incidence estimates varied approximately 2-fold between the two databases (CCAE, Medicaid) that described socioeconomically different non-elderly populations; for a given cap varied 2-fold to 4-fold within each database across the other implementation choices; and if the cap was also allowed to vary, varied 6-fold or 7-fold within each database.The main limitations were typical of studies from health services databases and included the lack of complete -rather than recent - medical histories, the limited amount of clinical information, and the assumption that medication dispensed was consumed as directed.In retrospective cohort studies from health services databases, TRD incidence estimates vary widely depending on the implementation choices. Unless a firm basis for narrowing the range of these choices can be found, or a different analytic approach not dependent on such choices is adopted, TRD incidence and prevalence estimates from such databases will be difficult to compare or interpret.
Assessing the readiness of precision medicine interoperabilty: An exploratory study of the National Institutes of Health genetic testing registry.
JG Ronquillo, C Weng and WT Lester,
Journal of innovation in health informatics , Nov 2017 17
Precision medicine involves three major innovations currently taking place in healthcare: electronic health records, genomics, and big data. A major challenge for healthcare providers, however, is understanding the readiness for practical application of initiatives like precision medicine. To better understand the current state and challenges of precision medicine interoperability using a national genetic testing registry as a starting point, placed in the context of established interoperability formats. We performed an exploratory analysis of the National Institutes of Health Genetic Testing Registry. Relevant standards included Health Level Seven International Version 3 Implementation Guide for Family History, the Human Genome Organization Gene Nomenclature Committee (HGNC) database, and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT). We analyzed the distribution of genetic testing laboratories, genetic test characteristics, and standardized genome/clinical code mappings, stratified by laboratory setting.There were a total of 25472 genetic tests from 240 laboratories testing for approximately 3632 distinct genes. Most tests focused on diagnosis, mutation confirmation, and/or risk assessment of germline mutations that could be passed to offspring. Genes were successfully mapped to all HGNC identifiers, but less than half of tests mapped to SNOMED CT codes, highlighting significant gaps when linking genetic tests to standardized clinical codes that explain the medical motivations behind test ordering. Conclusion: While precision medicine could potentially transform healthcare, successful practical and clinical application will first require the comprehensive and responsible adoption of interoperable standards, terminologies, and formats across all aspects of the precision medicine pipeline.
