Do Randomized Controlled Trials Discuss Healthcare Costs?
Bibliographic record
Abstract
BACKGROUND: Healthcare costs, particularly pharmaceutical costs, are a dominant issue for most healthcare organizations, but it is unclear if randomized controlled trials (RCTs) routinely discuss costs. Our objective was to assess the frequency and factors associated with the inclusion of costs in RCTs. METHODS AND FINDINGS: We randomly sampled 188 RCTs spanning three years (2003-2005) from six high impact journals. The sample size for RCTs was based on a calculation to estimate the inclusion of actual drug costs with a precision of +/-3%. Two reviewers independently extracted cost data and study characteristics. Frequencies were calculated and potential characteristics associated with the inclusion of costs were explored. Actual drug costs were included in 4.7% (9/188) of RCTs; any actual costs were included in 7.4% (14/188) of RCTs; and any mention of costs was included in 27.7% (52/188) of RCTs. As the amount of industry funding increased across RCTs, from non-profit to mixed to fully industry funded RCTs, there was a statistically significant reduction in the number of RCTs with any actual costs (Cochran-Armitage test, p = 0.005) and any mention of costs (Cochran-Armitage test, p = 0.02). Logistic regression analysis also indicated funding was associated with the inclusion of any actual cost (OR = 0.34, p = 0.009) or any mention of costs (OR = 0.63, p = 0.02). Journal, study conclusions, study location, primary author's country and product age were not associated with inclusion of cost information. CONCLUSION: While physicians are encouraged to consider costs when prescribing drugs for their patients, actual drug costs were provided in only 5% of RCTs and were not mentioned at all in 72% of RCTs. Industry funded trials were less likely to include cost information. No other factors were associated with the inclusion of cost information.
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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 | 0.582 | 0.928 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.024 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 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".