Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization
Bibliographic record
Abstract
Importance: Mendelian randomization (MR) studies use genetic variation associated with modifiable exposures to assess their possible causal relationship with outcomes and aim to reduce potential bias from confounding and reverse causation. Objective: To develop the STROBE-MR Statement as a stand-alone extension to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guideline for the reporting of MR studies. Design, Setting, and Participants: The development of the STROBE-MR Statement followed the Enhancing the Quality and Transparency of Health Research (EQUATOR) framework guidance and used the STROBE Statement as a starting point to draft a checklist tailored to MR studies. The project was initiated in 2018 by reviewing the literature on the reporting of instrumental variable and MR studies. A group of 17 experts, including MR methodologists, MR study design users, developers of previous reporting guidelines, and journal editors, participated in a workshop in May 2019 to define the scope of the Statement and draft the checklist. The draft checklist was published as a preprint in July 2019 and discussed on the preprint platform, in social media, and at the 4th Mendelian Randomization Conference. The checklist was then revised based on comments, further refined through 2020, and finalized in July 2021. Findings: The STROBE-MR checklist is organized into 6 sections (Title and Abstract, Introduction, Methods, Results, Discussion, and Other Information) and includes 20 main items and 30 subitems. It covers both 1-sample and 2-sample MR studies that assess 1 or multiple exposures and outcomes, and addresses MR studies that follow a genome-wide association study and are reported in the same article. The checklist asks authors to justify why MR is a helpful method to address the study question and state prespecified causal hypotheses. The measurement, quality, and selection of genetic variants must be described and attempts to assess validity of MR-specific assumptions should be well reported. An item on data sharing includes reporting when the data and statistical code required to replicate the analyses can be accessed. Conclusions and Relevance: STROBE-MR provides guidelines for reporting MR studies. Improved reporting of these studies could facilitate their evaluation by editors, peer reviewers, researchers, clinicians, and other readers, and enhance the interpretation of their results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.777 | 0.925 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.011 | 0.021 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".