Kidney Function Endpoints in Kidney Transplant Trials: A Struggle for Power
Bibliographic record
Abstract
Kidney function endpoints are commonly used in randomized controlled trials (RCTs) in kidney transplantation (KTx). We conducted this study to estimate the proportion of ongoing RCTs with kidney function endpoints in KTx where the proposed sample size is large enough to detect meaningful differences in glomerular filtration rate (GFR) with adequate statistical power. RCTs were retrieved using the key word "kidney transplantation" from the National Institute of Health online clinical trial registry. Included trials had at least one measure of kidney function tracked for at least 1 month after transplant. We determined the proportion of two-arm parallel trials that had sufficient sample sizes to detect a minimum 5, 7.5 and 10 mL/min difference in GFR between arms. Fifty RCTs met inclusion criteria. Only 7% of the trials were above a sample size of 562, the number needed to detect a minimum 5 mL/min difference between the groups should one exist (assumptions: α = 0.05; power = 80%, 10% loss to follow-up, common standard deviation of 20 mL/min). The result increased modestly to 36% of trials when a minimum 10 mL/min difference was considered. Only a minority of ongoing trials have adequate statistical power to detect between-group differences in kidney function using conventional sample size estimating parameters. For this reason, some potentially effective interventions which ultimately could benefit patients may be abandoned from future assessment.
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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 | 0.798 | 0.837 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.049 | 0.014 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.031 | 0.033 |
| Open science | 0.016 | 0.023 |
| Research integrity | 0.021 | 0.060 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".