Issues relating to study design and risk of bias when including non‐randomized studies in systematic reviews on the effects of interventions
Bibliographic record
Abstract
Non-randomized studies may provide valuable evidence on the effects of interventions. They are the main source of evidence on the intended effects of some types of interventions and often provide the only evidence about the effects of interventions on long-term outcomes, rare events or adverse effects. Therefore, systematic reviews on the effects of interventions may include various types of non-randomized studies. In this second paper in a series, we address how review authors might articulate the particular non-randomized study designs they will include and how they might evaluate, in general terms, the extent to which a particular non-randomized study is at risk of important biases. We offer guidance for describing and classifying different non-randomized designs based on specific features of the studies in place of using non-informative study design labels. We also suggest criteria to consider when deciding whether to include non-randomized studies. We conclude that a taxonomy of study designs based on study design features is needed. Review authors need new tools specifically to assess the risk of bias for some non-randomized designs that involve a different inferential logic compared with parallel group trials. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.874 | 0.945 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.020 | 0.022 |
| Bibliometrics | 0.025 | 0.035 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.016 | 0.015 |
| Research integrity | 0.025 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 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".