Can a Mendelian Randomization Study Predict the Results of a Clinical Trial? Yes and No
Bibliographic record
Abstract
Randomized controlled trials are considered at the top of the evidence hierarchy. However, in several cases randomized trials cannot be conducted or have not yet been completed. In such settings observational studies may provide important inference, yet traditional statistical adjustment methods fall short of controlling for all potential confounders, as unknown confounders cannot be taken care of by even the most sophisticated statistical tools. The mendelian randomization study is a type of research design which simultaneously exploits random transmission of genes and genetic linkage to obtain inferential estimates from the association between specific genetic variants known to modulate given risk factors and the corresponding outcomes of interests. Despite several developments in this field, there remain several areas of further research, and discrepancies between mendelian randomization studies and the corresponding randomized trials have already been recognized. Nonetheless, it is likely that this novel type of study will be used more commonly in the future, and a working knowledge of its pros, cons, and range of validity is crucial for conscientious interpretation and application. We thus aimed to concisely yet poignantly introduce the scholarly reader to this novel type of research design, notwithstanding that complementarity prevails in most cases over overlap between mendelian randomization studies and randomized trials.
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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 | 0.493 | 0.804 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".