Health Evidence Knowledge Accelerator (HEvKA) Daily Progress July 3, 2024

[Brian Alper]

5 people (BA, CE, EA, KR, KS) participated in up to 2 active working group meetings.

The Funding the Ecosystem Infrastructure Working Group discussed the developing proposal for the NIH PAR-23-236 R24 Early-stage Biomedical Data Repositories and Knowledgebases funding opportunity with a title of Setting Evidence-Based Medicine on Fast Healthcare Interoperability Resources (EBMonFHIR) as the data standard for biomedical research  including use cases for a systematic review team working across multiple tools (PICO Portal, Covidence, SRDR+, Cochrane RevMan).

The Communications Working Group discussed the potential to restart the ClinicalResearchOnFHIR Working Group if there is adequate interest from participants and communicated with two related groups – one working on a “FHIR-like” query language for cohort definitions and another working on coordination across FHIR, OMOP and i2b2 data models to provide a data dictionary for research datasets.

Quote for Thought: “Losers quit when they fail. Winners fail until they succeed.” ―Robert T. Kiyosaki

 

 

To get involved or stay informed: HEvKA Project Page on FEvIR Platform, HEvKA Project Page on HL7 Confluence, or join any of the groups that are now meeting in the following weekly schedule:

Weekly Meeting Schedule and Link:

 

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Day	Time (Eastern)	Team
Monday	8-9 am	Project Management
Monday	9-10 am	Setting the Scientific Record on FHIR WG
Monday	2-3 pm	Statistic Terminology WG
Tuesday	9-10 am	Measuring the Rate of Scientific Knowledge Transfer WG
Tuesday	2-3 pm	StatisticsOnFHIR WG (a CDS EBMonFHIR sub-WG)
Tuesday	3-4 pm	Ontology Management WG
Wednesday	8-9 am	Funding the Ecosystem Infrastructure WG
Wednesday	9-10 am	Communications(Awareness, Scholarly Publications) WG
Thursday	8-9 am	EBM Implementation Guide WG (a CDS EBMonFHIR sub-WG)
Thursday	9-10 am	Computable EBM Tools Development WG
Friday	9-10 am	Risk of Bias Terminology WG
Friday	10-11 am	GRADE Ontology WG
Friday	12-1 pm	Project Management

 

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Brian S. Alper, MD, MSPH, FAAFP, FAMIA

CEO, Computable Publishing LLC

bal...@computablepublishing.com

 

 

Making Science Machine-Interpretable
http://computablepublishing.com 

 

Lead, HEALTH EVIDENCE KNOWLEDGE ACCELERATOR

President, Scientific Knowledge Accelerator Foundation

 

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[Brian Alper]

There were no HEvKA meetings today due to the United States holiday.

 

Releases on the FEvIR Platform:

The Fast Evidence Interoperability Resources (FEvIR) Platform is available for use now, but is “pre-release”.  The current version is 0.242.0 (July 4, 2024). Viewing resources is open without login.  Signing in is free and required to create content (which can then only be edited by the person who created the content).

• Release 0.242.0 (July 4, 2024) improves efficiency and performance across index and search functions for titles, descriptions, comments, and classifications of all Resources and cited artifacts.

 

Quote for Thought: “It is never too late to be what you might have been.” ―George Eliot

[Brian Alper]

8 people (BA, HK, HL, JB, KS, MA, PW, SM) participated in up to 2 active working group meetings.

 

The Risk of Bias Terminology Working Group found the term inadequate accounting for heterogeneity approved with 6 affirmative votes. Discussions on the term inadequate sensitivity analysis helped us recognize that the term is not limited to ‘synthesis bias’ as it can be applied to analyses that are not syntheses. We revised the term to be a type of analysis bias. We also included modeling assumptions in addition to missing or distorted data as the types of things one may address via sensitivity analysis. We also moved the entire ‘synthesis bias’ portion of the hierarchy to be a type of ‘analysis bias’. There is currently 1 Risk of Bias term open for voting:

Term	Definition	Alternative Terms (if any)	Comment for application (if any)
inadequate sensitivity analysis	An analysis bias due to inadequate approach to determine the implications of missing data, distorted data, or modeling assumptions.		Sensitivity analysis is the process of accounting for the implications of missing or distorted data or modeling assumptions . Methods of sensitivity analysis to account for missing data include but are not limited to best-case scenario, worst-case scenario, and last-observation-carried-forward. Methods of sensitivity analysis to account for distorted data include but are not limited to intention-to-treat analysis, per-protocol analysis, and completer analysis. Methods of sensitivity analysis to account for modeling assumptions include but are not limited to variations in prior probabilities and changes in the statistical model. The term 'inadequate sensitivity analysis' matches the ROBIS signaling question 4.5 ''Were the findings robust, e.g. as demonstrated through funnel plot or sensitivity analyses?'   A funnel plot may be used to detect missing data due to publication bias. Although funnel plot asymmetry has been equated with publication bias, the funnel plot displays a tendency for the intervention effects estimated in smaller studies to differ from those estimated in larger studies, and such small-study effects may be due to reasons other than publication bias. (Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997; 315: 629-634.) Consider also inadequate accounting for heterogeneity.

 

To participate you can join the Scientific Evidence Code System (SEVCO) Expert Working Group at https://fevir.net/resources/Project/27845.

The GRADE Ontology Working Group spent most of the meeting on the “Imprecision” term.   For the “Publication bias” term we noted 11 votes (5 affirmative, 6 negative) and reviewed 7 comments.

The vote on "Imprecision" was 11-0 with no dissenting votes. However, there were 3 comments.  In discussing the comments, we changed the Definition from “The amount of variation…” to “The degree of variation…” We also changed the beginning of the Comment for application from “Imprecision is related to random error.” to “Imprecision is a result of statistical heterogeneity and adjustments for random error.” and we revised the ending to “When there is limited information, such as when the sample size is not large enough (e.g., less than the optimal information size) or the number of events is too small to provide a reliable estimate of effect, there is a higher potential for random error and some imprecision may be assumed.”

 

Term	Definition	Alternative Terms (if any)	Comment for application (if any)
Imprecision	The degree of variation or spread among the probable values for the estimate of effect.		Imprecision is a result of statistical heterogeneity and adjustments for error. Measures of imprecision that are commonly used include confidence intervals, credible intervals, standard deviation, standard error, and a range of probable values.   In the GRADE approach, imprecision is rated relative to a threshold. The rating of imprecision is based on whether the range of probable values for the estimate of effect includes values on both sides of a threshold. Thresholds may be set related to the null effect, a minimally important difference, or classifying magnitudes of effect (e.g., small, moderate, or large). In the context of making a decision, the threshold is often specified as the value for which the decision would differ.   When there is limited information, such as when the sample size is not large enough (e.g., less than the optimal information size) or the number of events is too small to provide a reliable estimate of effect, there is a higher potential for random error and some imprecision may be assumed.
Publication bias	The situation in which findings of a review are distorted due to systematic differences between the studies included and the studies that are eligible for the review but not identified due to the studies remaining unpublished or obscurely published.		Studies may be obscurely published if they are published in journals with limited access due to financial, language, or indexing barriers, or are only available as abstracts, theses, conference proceedings, preprints, or other less accessible formats.   Publication bias is a study selection bias in which the publicly available studies are not representative of all conducted studies.   Publication bias arises from the failure to identify all studies that have been conducted, either published (i.e., publicly available) or unpublished. The term 'studies' means evidence or research results in any form where such studies would meet the study eligibility criteria without consideration of criteria regarding the form of publication. The phrase 'publicly available studies' means the studies are available to the broad academic community and the public through established distribution channels in any form, including forms with restricted access.  Established distribution channels include peer-reviewed journals, books, conference proceedings, dissertations, reports by governmental or research organizations, preprints, and study registries.   Publication bias often leads to an overestimate in the effect in favor of the study hypothesis, because studies with statistically significant positive results are more likely to be publicly available.

 

Please visit the term pages via the links in the table above and click the Comment button if you would like to share any comments that will be openly viewed by anyone visiting the page.  You may also click the Vote button to anonymously register your agreement or disagreement with this term.  If you vote ‘No’ you need to add a comment (along with your vote, not publicly shared with your name) explaining what change is needed to reach agreement.

To make voting on multiple terms easier you can use My Ballot at https://fevir.net/myballot -- You can go to My Ballot and mark any term with Yes if you approve, or with No (and add your comment to express what change is needed), or you can navigate to each term page to see additional detail.

 

 

 

Releases on the FEvIR Platform:

The Fast Evidence Interoperability Resources (FEvIR) Platform is available for use now, but is “pre-release”.  The current version is 0.243.0 (July 5, 2024). Viewing resources is open without login.  Signing in is free and required to create content (which can then only be edited by the person who created the content).

• Release 0.242.0 (July 4, 2024) improves the data entry experience for the Reference datatype by adding a ‘Limit by Resource Type’ function to the 'Search Resources to Select One' and 'Search Resources to Add Multiple Entries' buttons to allow searching within relevant resource types and selecting Resources to autofill the data entry form.

 

Quote for Thought: “The longest distance between two points is a shortcut.” ―John Maxwell