Heterogeneity in pragmatic randomised trials: sources and management
Bibliographic record
Abstract
BACKGROUND: Pragmatic trials aim to generate evidence to directly inform patient, caregiver and health-system manager policies and decisions. Heterogeneity in patient characteristics contributes to heterogeneity in their response to the intervention. However, there are many other sources of heterogeneity in outcomes. Based on the expertise and judgements of the authors, we identify different sources of clinical and methodological heterogeneity, which translate into heterogeneity in patient responses-some we consider as desirable and some as undesirable. For each of them, we discuss and, using real-world trial examples, illustrate how heterogeneity should be managed over the whole course of the trial. MAIN TEXT: Heterogeneity in centres and patients should be welcomed rather than limited. Interventions can be flexible or tailored and control interventions are expected to reflect usual care, avoiding use of a placebo. Co-interventions should be allowed; adherence should not be enforced. All these elements introduce heterogeneity in interventions (experimental or control), which has to be welcomed because it mimics reality. Outcomes should be objective and possibly routinely collected; standardised assessment, blinding and adjudication should be avoided as much as possible because this is not how assessment would be done outside a trial setting. The statistical analysis strategy must be guided by the objective to inform decision-making, thus favouring the intention-to-treat principle. Pragmatic trials should consider including process analyses to inform an understanding of the trial results. Needed data to conduct these analyses should be collected unobtrusively. Finally, ethical principles must be respected, even though this may seem to conflict with goals of pragmatism; consent procedures could be incorporated in the flow of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.556 | 0.861 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.019 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".