Are Biases Related to Attrition, Missing Data, and the Use of Intention to Treat Related to the Magnitude of Treatment Effects in Physical Therapy Trials?
Bibliographic record
Abstract
ABSTRACT: The objective of this study was to determine the association between biases related to attrition, missing data, and the use of intention to treat and changes in effect size estimates in physical therapy randomized trials. A meta-epidemiological study was conducted. A random sample of randomized controlled trials included in meta-analyses in the physical therapy discipline were identified. Data extraction including assessments of the use of intention to treat principle, attrition-related bias, and missing data was conducted independently by two reviewers. To determine the association between these methodological issues and effect sizes, a two-level analysis was conducted using a meta-meta-analytic approach. Three hundred ninety-three trials included in 43 meta-analyses, analyzing 44,622 patients contributed to this study. Trials that did not use the intention-to-treat principle (effect size = -0.13, 95% confidence interval = -0.26 to 0.01) or that were assessed as having inappropriate control of incomplete outcome data tended to underestimate the treatment effect when compared with trials with adequate use of intention to treat and control of incomplete outcome data (effect size = -0.18, 95% confidence interval = -0.29 to -0.08).Researchers and clinicians should pay attention to these methodological issues because they could provide inaccurate effect estimates. Authors and editors should make sure that intention-to-treat and missing data are properly reported in trial reports.
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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 | 0.645 | 0.878 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.022 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".