You’ve probably seen @MauraBeaton’s announcement that the next US OHDSI face-to-face will be hosted by Columbia University in New York City on May2-3. Several folks have been asking about what is planned for that 2-day event. Here’s your quick answer:
We’re going to do a study together!
@mvanzandt and others in the community had suggested that a good community activity would be to bring everyone together to focus on one specific common goal: to generate reliable evidence on one clinical problem. We think that’s a great idea, so we’re going to do exactly that!
More specifically, our plan over the next 3 months prior to the face-to-face is to solicit the community for good candidate clinical questions that require evidence that we can generate using a comparative cohort design. The question could be a safety surveillance question, like what @jon_duke answered last year : ‘does levetiracetam increase the risk of angioedema, relative to phenytoin?’ or it could be a comparative effectiveness question, like the study @estone96 is leading: ‘does alendronate reduce the risk of hip fracture more than raloxifene?’. The questions do not necessarily need to examine the effects of drugs, but rather can represent any exposure to any intervention that is observable in our observational databases. For those who have taken the population-level effect estimation tutorial, you’ve heard @msuchard , @schuemie and me promote a general framework to define these types of problems using 5 core elements: 1) a target exposure cohort, 2) a comparator cohort, 3) an outcome cohort, 4) a time-at-risk period, and 5) a model specification.
At the face-to-face meeting, those in attendance will select 1 clinical question to focus on for the duration of the session, based on the community feedback and data availability. Then, the real fun will start: once aligned on the common problem, we will break out into groups to tackle the specific components required to design and implement the study. One group will focus on defining the target and comparator cohorts, ensuring that we have an adequate capture of the exposures of interest and proper application of whatever inclusion criteria are necessary to define the appropriate study population. A second group will create and validate an outcome cohort definition. A third group will produce a list of negative control outcomes that will be used for empirical calibration. A fourth group will make the analytic design choices and prepare an R package that implements the study using the CohortMethod package. These activities will all be pulled together into an open community protocol…and that initial draft will need to be completed on Day 1…then we’ll break to have fun in NYC.
On Day 2, we will get the study package running across the OHDSI network. If you have data in the OMOP Common Data Model v5 and want to participate in a network study, you’ll be able to kick off the study in the morning and (hopefully) have results to share with the community by the afternoon. While the study is running, we will hold a community brainstorm to discuss what we as a community can do to improve the validity and efficiency of observational research. From there, we will review the study diagnostics from all the participating data partners, and with any luck, have a final protocol and study results to share with the world!
That’s right, our goal for this face-to-face is to go from idea to results in two days (or less)! While this may seem incomprehensible in some circles, I’m confident if we work together as one community, we will be impressed by how much we can truly accomplish.
So who should come to the OHDSI F2F @ Columbia? Well, we want you there if any of the following describes you:
- You are passionate about the clinical questions that are to be studied, and can contribute to the clinical understanding of the problem and the synthesis of the evidence we generate with what is already known.
- You want to contribute to the epidemiological design of a clinical study, and see yourself leading the definition of exposure/outcome cohorts based on your clinical understanding and knowledge of observational databases.
- You want to apply your statistical expertise to the design of a population-level effect estimation study, and plan to contribute to the implementation of a R package that parameterizes the CohortMethod and associated OHDSI tools into an end-to-end study.
- You have observational data in the OMOP Common Data Model v5 format, and want to execute a network study against your data and share aggregate summary results with the community to advance our collective evidence about the question of interest.
Columbia’s Department of Biomedical Informatics is a wonderful venue for holding this intellectual ‘sprint’, but alas, as with most of New York City, space is at a premium. So, we will have to limit the number of participants for the OHDSI F2F. @MauraBeaton will be sending out a notice to allow you to express interest if you’d like to register and we’ll do our best to accommodate as many as we can.
I’m looking forward to collaborating with you all to generate reliable evidence that can meaningfully improve health. Happy hacking!