Influence of attrition, missing data, compliance, and related biases and analyses strategies on treatment effects in randomized controlled trials in rehabilitation: a methodological review
Bibliographic record
Abstract
INTRODUCTION: Attrition, missing data, compliance, and related biases are three interrelated concepts. Previous research has found that these biases can affect the treatment estimates of randomized trials (RCTs). The extent to which the effects of attrition, missing data, compliance and related biases influence effect size estimates in rehabilitation as well as the effect of analytic strategies to mitigate these biases is unknown. EVIDENCE ACQUISITION: To compile and synthetize the empirical evidence regarding the effects of attrition and compliance related biases on treatment effect estimates in rehabilitation RCTs. Electronic searches were conducted. Studies were included if they investigated the effects of attrition, missing data, compliance and related biases on treatment estimates. The seven studies meeting inclusion criteria were coded for type of biases and summarized using a narrative and/or quantitative approach when appropriate. EVIDENCE SYNTHESIS: Findings demonstrated that trials reporting higher levels of attrition (differences in ES: 0.18 [95%CI: 0.15, 0.22 ]), exclusion of participants from analyses (differences in ES: 0.13 [95% CI: -0.03, 0.29]), lack of good control of incomplete outcome data (differences in ES: 0.14 [95%CI: -0.02, 0.30]) and analysis by "as treated"(differences in ES:-0.39 [95%CI: -0.99, 0.2]) or "per protocol" (differences in ES:-0.46 [95%CI: -0.92, 0]) analyses were more likely to have higher effects than those that did not. CONCLUSIONS: These findings suggest that attrition, missing data, compliance, and related biases have an influence in treatment effect estimates in rehabilitation trials. Therefore, these results should be taken into consideration when designing, conducting and reporting trials in the rehabilitation field.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.272 | 0.601 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".