Single-Inhaler Triple Therapy in Patients with Advanced COPD: Bayesian Modeling of the Healthcare Resource Utilization Data and Associated Costs from the IMPACT Trial
Bibliographic record
Abstract
Objectives: In the IMPACT trial (NCT02164513), triple therapy with fluticasone furoate/umeclidinium/vilanterol (FF/UMEC/VI) showed clinical benefit compared with dual therapy with either FF/VI or UMEC/VI in the treatment of chronic obstructive pulmonary disease (COPD). We used data from IMPACT to determine whether this translated into differences in COPD-related healthcare resource utilization (HRU) costs in a United Kingdom (UK) setting. Methods: In a within-trial analysis, individual patient data from the IMPACT intention-to-treat (ITT) population were analyzed to estimate rates of COPD-related HRU with FF/UMEC/VI, FF/VI, or UMEC/VI. A Bayesian approach was applied to address issues typically encountered with this kind of data, namely data missing due to early study withdrawal, subjects with zero reported HRU, and skewness. Rates of HRU were estimated under alternate assumptions of data being missing at random (MAR) or missing not at random (MNAR). UK-specific unit costs were then applied to estimated HRU rates to calculate treatment-specific costs. Results: Under each MNAR scenario, per patient per year (PPPY) rates of COPD-related HRU were lowest amongst those patients who received treatment with FF/UMEC/VI compared with those receiving either FF/VI or UMEC/VI. Although absolute HRU rates and costs were typically higher for all treatment groups under MNAR scenarios versus MAR, final economic conclusions were robust to patient withdrawals. Conclusions: PPPY rates were typically lower with FF/UMEC/VI versus FF/VI or UMEC/VI.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".