Cost-effectiveness of ranibizumab in the treatment of visual impairment due to diabetic macular edema
Bibliographic record
Abstract
Objective Ranibizumab, an anti-vascular endothelial growth factor designed for ocular use, has been deemed cost-effective in multiple indications by several Health Technology Assessment bodies. This study assessed the cost-effectiveness of ranibizumab monotherapy or combination therapy (ranibizumab plus laser photocoagulation) compared with laser monotherapy for the treatment of visual impairment due to diabetic macular edema (DME). Methods A Markov model was developed in which patients moved between health states defined by best-corrected visual acuity (BCVA) intervals and an absorbing 'death' state. The population of interest was patients with DME due to type 1 or type 2 diabetes mellitus. Baseline characteristics were based on those of participants in the RESTORE study. Main outputs were costs (in 2013 CA$) and health outcomes (in quality-adjusted life-years [QALYs]) and the incremental cost-effectiveness ratio (ICER) was calculated. This cost-utility analysis was conducted from healthcare system and societal perspectives in Quebec. Results From a healthcare system perspective, the ICERs for ranibizumab monotherapy and combination therapy vs laser monotherapy were CA$24 494 and CA$36 414 per QALY gained, respectively. The incremental costs per year without legal blindness for ranibizumab monotherapy and combination therapy vs laser monotherapy were CA$15 822 and CA$20 616, respectively. Based on the generally accepted Canadian ICER threshold of CA$50 000 per QALY gained, ranibizumab monotherapy and combination therapy were found to be cost-effective compared with laser monotherapy. From a societal perspective, ranibizumab monotherapy and combination therapy provided greater benefits at lower costs than laser monotherapy (ranibizumab therapy dominated laser therapy). Conclusions Ranibizumab monotherapy and combination therapy resulted in increased quality-adjusted survival and time without legal blindness and lower costs from a societal perspective compared with laser monotherapy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".