Comparing direct acting antivirals for hepatitis C using observational data – Why and how?
Bibliographic record
Abstract
The World Health Organisation's goal of hepatitis C virus (HCV) elimination by 2030 will require lower drug prices. Estimates of comparative efficacy promote competition between pharmaceutical companies but direct acting antivirals have been approved for the treatment of HCV without comparative trials. We emulated a randomized trial to answer the question of whether easy to treat patients with genotype 1 HCV could be treated with sofosbuvir/ledipasvir (SOF/LDV) rather than sofosbuvir/velpatasvir (SOF/VEL). Patients without comorbidities or end stage liver disease were selected from the British Colombia Hepatitis Testers Cohort. To create a conceptual trial, we matched each patient starting SOF/VEL (a 'case') to the patient starting SOF/LDV with the closest propensity score (a 'control'). We estimated the probability of treatment failure under a Bayesian logistic model with a random effect for each case-control set and used that model to give an estimate of a risk difference for the conceptual trial. Treatment failure was recorded for 27 of 825 (3%) cases and for 29 of 602 (5%) matched controls. Estimates from our model were treatment success rates of 97% (95% credible interval, CrI, 95%-98%) for treatment with SOF/VEL, 95% (95% CrI 93%-97%) for treatment with SOF/LDV and a risk difference between treatments of 2% (95% CrI 0%-4%). This risk difference is evidence that SOF/LDV is not inferior to SOF/VEL for easy to treat patients with genotype 1 HCV. The approach is a template for comparing drugs when there are no data from comparative trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".