Sustained Virological Response as a Surrogate Marker for Mortality, Decompensated Cirrhosis, or Hepatocellular Carcinoma in People With Chronic Hepatitis C Virus Infection Treated With Direct-Acting Antivirals: Protocol for a Bayesian and Causal Mediation Analysis
Bibliographic record
Abstract
BACKGROUND: Sustained virological response (SVR) is commonly used as a marker of treatment success in people with chronic hepatitis C virus (HCV) infection. However, there is uncertainty on whether SVR is a validated surrogate marker of successful chronic HCV infection treatment. OBJECTIVE: This research project aims to evaluate whether SVR is a good surrogate for all-cause mortality, decompensated cirrhosis, any specific aspect of liver decompensation (jaundice, ascites, hepatic encephalopathy, hepatorenal syndrome, or variceal hemorrhage), or hepatocellular carcinoma in people with chronic HCV infection eligible to receive direct-acting antiviral drugs. METHODS: We will use two ongoing systematic reviews on the effectiveness of direct-acting antiviral drugs in chronic HCV infection as our data sources. The analysis plan is to estimate the regression coefficients or between-studies correlation between SVR and an event using three different Bayesian approaches with OpenBUGS, as outlined in the guidance by the evidence synthesis unit, and estimate the average proportion of the effect mediated through SVR by causal mediation analysis using R. RESULTS: As of June 19, 2025, the two systematic reviews (one on randomized clinical trials and one on observational studies) on the effectiveness of direct-acting antiviral drugs in chronic HCV infection are ongoing. CONCLUSIONS: We will use the German Institute of Quality and Efficiency in Health Care criterion for surrogacy for cancer, with at least 50% of the treatment effect mediated through SVR, but the information will be reported in a way that allows people to interpret the information using their own criteria.
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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 | 0.081 | 0.101 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.016 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.066 | 0.013 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".