A Comparative Analysis of Models Used to Evaluate the Cost-Effectiveness of Dabigatran Versus Warfarin for the Prevention of Stroke in Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: A number of models exploring the cost-effectiveness of dabigatran versus warfarin for stroke prevention in atrial fibrillation have been published. These studies found dabigatran was generally cost-effective, considering well-accepted willingness-to-pay thresholds, but estimates of the incremental cost-effectiveness ratios (ICERs) varied, even in the same setting. The objective of this study was to compare the findings of the published economic models and identify key model features accounting for differences. METHODS: All aspects of the economic evaluations were reviewed: model approach, inputs, and assumptions. A previously published model served as the reference model for comparisons of the selected studies in the US and UK settings. The reference model was adapted, wherever possible, using the inputs and key assumptions from each of the other published studies to determine if results could be reproduced in the reference model. Incremental total costs, incremental quality-adjusted life years (QALYs), and ICERs (cost per QALY) were compared between each study and the corresponding adapted reference model. The impact of each modified variable or assumption was tracked separately. RESULTS: The selected studies were in the US setting (2), the Canadian setting (1), and the UK setting (2). All models used the Randomized Evaluation of Long-Term Anticoagulation study (RE-LY) as the main source for clinical inputs, and all used a Markov modelling approach, except one that used discrete event simulation. The reference model had been published in the Canadian and UK settings. In the UK setting, the reference model reported an ICER of UK£4,831, whereas the other UK-based analysis reported an ICER of UK£23,082. When the reference model was modified to use the same population characteristics, cost inputs, and utility inputs, it reproduced the results of the other model (ICER UK£25,518) reasonably well. Key reasons for the different results between the two models were the assumptions on the event utility decrement and costs associated with intracranial haemorrhage, as well as the costs of warfarin monitoring and disability following events. In the US setting, the reference model produced an ICER similar to the ICER from one of the US models (US$15,115/QALY versus US$12,386/QALY, respectively) when modelling assumptions and input values were transferred into the reference model. Key differences in results could be explained by the population characteristics (age and baseline stroke risk), utility assigned to events and specific treatments, adjustment of stroke and intracranial haemorrhage risk over time, and treatment discontinuation and switching. The reference model was able to replicate the QALY results, but not the cost results, reported by the other US cost-effectiveness analysis. The parameters driving the QALY results were utility values by disability levels as well as utilities assigned to specific treatments, and event and background mortality rates. CONCLUSIONS: Despite differences in model designs and structures, it was mostly possible to replicate the results published by different authors and identify variables responsible for differences between ICERs using a reference model approach. This enables a better interpretation of published findings by focusing attention on the assumptions underlying the key model features accounting for differences.
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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".