Ordinal Outcomes Are Superior to Binary Outcomes for Designing and Evaluating Clinical Trials in Compensated Cirrhosis
Bibliographic record
Abstract
Background and Aims Prevention of decompensation is a primary therapeutic target in patients with compensated cirrhosis (CC). However, a major problem is the large sample size and long follow‐up required to demonstrate a significant treatment effect because of the relatively low baseline risk. For this reason, it has been recently suggested that ordinal outcomes may be used in this area to gain power and reduce sample size. The aim of this study was to assess the applicability of ordinal outcomes in cirrhosis. Approach and Results An inception cohort of 202 patients with CC (no ascites, gastrointestinal bleeding, encephalopathy, or jaundice) without esophageal varices was included, and 5‐year outcome is reported. Etiology was mostly viral and alcoholic, and there were no dropouts. Ordinal outcome was set according to six grades with a previously established prognostic ordinality: grade 1 = no disease progression; grade 2 = development of varices; grade 3 = bleeding alone; grade 4 = nonbleeding single decompensation; grade 5 = more than one decompensating event; and grade 6 = death. At the 60‐month time point, patients were distributed in grades 1 through 6 as follows: 129, 43, 2, 7, 5, and 16, respectively. Emulation of a clinical trial performed by dividing patients based on baseline platelet count into two groups (cutoff, 150 × 109/L) demonstrated a statistically significant outcome difference between groups when using ordinal outcomes not detectable by binary logistic or chi‐square or time‐to‐event analyses. Additionally, using ordinal outcomes in a hypothetical study to prevent decompensation resulted in sample‐size estimates 3‐to 4‐fold lower than using a binary composite endpoint. Conclusions Compared to traditional binary outcomes, the use of ordinal outcomes in trials of cirrhosis decompensation may provide more power and thus may require a smaller sample size.
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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 | 0.461 | 0.520 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".