Neuropathic pain clinical trials: factors associated with decreases in estimated drug efficacy
Bibliographic record
Abstract
Multiple recent pharmacological clinical trials in neuropathic pain have failed to show beneficial effect of drugs with previously demonstrated efficacy, and estimates of drug efficacy seems to have decreased with accumulation of newer trials. However, this has not been systematically assessed. Here, we analyze time-dependent changes in estimated treatment effect size in pharmacological trials together with factors that may contribute to decreases in estimated effect size. This study is a secondary analysis of data from a previous published NeuPSIG systematic review and meta-analysis, updated to include studies published up till March 2017. We included double-blind, randomized, placebo-controlled trials examining the effect of drugs for which we had made strong or weak recommendations for use in neuropathic pain in the previously published review. As the primary outcome, we used an aggregated number needed to treat for 50% pain reduction (alternatively 30% pain reduction or moderate pain relief). Analyses involved 128 trials. Number needed to treat values increased from around 2 to 4 in trials published between 1982 and 1999 to much higher (less effective) values in studies published from 2010 onwards. Several factors that changed over time, such as larger study size, longer study duration, and more studies reporting 50% or 30% pain reduction, correlated with the decrease in estimated drug effect sizes. This suggests that issues related to the design, outcomes, and reporting have contributed to changes in the estimation of treatment effects. These factors are important to consider in design and interpretation of individual study data and in systematic reviews and meta-analyses.
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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 | 0.363 | 0.664 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.021 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".