Learning from past mistakes: assessing trial quality, power and eligibility in non-renal systemic lupus erythematosus randomized controlled trials
Bibliographic record
Abstract
OBJECTIVES: To evaluate the post hoc study power of randomized controlled trials (RCTs) in the treatment of non-renal SLE and to determine the generalizability of these RCTs using an SLE database. METHODS: RCTs in non-renal SLE were identified using PubMed (1975-2007). Inclusion/exclusion criteria, trial quality (5-point scale) and results of each study were recorded. The inclusion/exclusion criteria were compared with an SLE database to determine the proportion of patients from the database who would theoretically be eligible for these trials. For each negative study, we calculated the post hoc study power. We also looked for temporal improvements of trials in the literature and examined if pharmaceutical involvement influenced trial quality. RESULTS: Sixty-four articles were included; the mean power of 30 negative studies was 24.6 +/- s.e.m. 3.9% (range 2.5-81.1%). Only one study had a power > 80%. Overall, potential eligibility of SLE patients in the database was 45.1 +/- s.e.m. 3.6%. Only 14 studies (21.9%) were of good quality. Fortunately, RCT quality is improving over time (trials <1995, compared with 1996-2002 and >2003; P < 0.001). Trials with pharmaceutical involvement had a significantly higher number of enrollees and better study quality. CONCLUSIONS: Negative RCTs in SLE were mostly underpowered but the generalizability of these trials was high. Determination of study power and the impact of eligibility criteria on generalizability of study results are crucial in the design of clinical trials to ensure applicability to clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.794 | 0.908 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.018 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".