Does Type of Sponsorship of Randomized Controlled Trials Influence Treatment Effect Size Estimates in Rehabilitation
Bibliographic record
Abstract
BACKGROUND: Sponsorship bias could affect research results to inform decision makers when using the results of these trials. The extent to which sponsorship bias affect results in the field of physical therapy has been unexplored in the literature. Therefore, the main aim of this study was to evaluate the influence of sponsorship bias on the treatment effects of randomized controlled trials in physical therapy area. METHODS: This was a meta-epidemiological study. A random sample of randomized controlled trials included in meta-analyses of physical therapy area were identified. Data extraction including assessments of appropriate influence of funders was conducted independently by two reviewers. To determine the association between biases related to sponsorship biases and effect sizes, a two-level analysis was conducted using a meta-meta-analytic approach. RESULTS: We analyzed 393 trials included in 43 meta-analyses. The most common sources of sponsorship for this sample of physical therapy trials were government (n = 205, 52%), followed by academic (n = 44, 11%) and industry (n = 39, 10%). The funding was not declared in a high percentage of the trials (n = 85, 22%). The influence of the trial sponsor was assessed as being appropriate in 246 trials (63%) and considered inappropriate/unclear in 147 (37%) of them. We have moderate evidence to say that trials with inappropriate/unclear influence of funders tended to have on average a larger effect size than those with appropriate influence of funding (effect size = 0.15; 95% confidence interval = -0.03 to 0.33). CONCLUSIONS: Based on our sample of physical therapy trials, it seems that most of the trials are funded by either government and academia and a small percentage are funded by the industry. Treatment effect size estimates were on average 0.15 larger in trials with lack of appropriate influence of funders as compared with trials with appropriate influence of funding. Contrarily to other fields, industry funding was relatively small and their influence perhaps less marked. All these results could be explained by the relative youth of the field and/or the absence of clear industry interests. In front of the call for action by the World Health Organization to strengthen rehabilitation in health systems, these results raise the issue of the need of public funding in the field.
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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 | 0.618 | 0.887 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.021 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".