If You Want to Perform a Cost-effectiveness Trial, First Do a Modeling Study
Bibliographic record
Abstract
Rheumatoid arthritis (RA) has become an expensive disease to treat with the introduction of biological therapies in the early 2000s. Therefore, it is of crucial importance that researchers, together with clinicians, search for treatment strategies with the best value for money. That is why we would like to thank the authors of the paper entitled, “Effect on Costs and Quality-adjusted Life-years of Treat-to-target Treatment Strategies Initiating Methotrexate, or Tocilizumab, or Their Combination in Early Rheumatoid Arthritis,” published in this issue of The Journal of Rheumatology , for their honorable attempt1. The authors describe the results of a preplanned cost-effectiveness analysis as a follow-up on the publication of the primary results of the U-Act-Early trial2. This trial was a 2-year, randomized, double-blind, double-dummy, strategy study at 21 rheumatology outpatient departments in the Netherlands, in which 3 treatment strategies were compared: start tocilizumab (TCZ) plus methotrexate (MTX); TCZ plus placebo-MTX (the TCZ arm); and MTX plus placebo-TCZ (the MTX arm). The primary results of the U-Act-Early trial showed a better immediate initiation of TCZ with or without MTX over initiation of MTX alone, but this difference had disappeared after 2 years due to tight control of disease activity in combination with active tapering during the trial, which resulted in comparable TCZ use at study end. In their current paper1, the authors hypothesized that initiating a TCZ-based strategy for patients with early RA using a strict treat-to-target approach and including a clear tapering strategy when in sustained remission, might become cost effective. They were not able to confirm this hypothesis, as they found that estimated from a societal perspective, TCZ + MTX compared to MTX is more expensive and only slightly more effective, whereas there is also a 23% chance that it is less effective [loss in … Address correspondence to Dr. W. Kievit, Radboud UMC, Department for Health Evidence (133), PO Box 9101, 6500HB Nijmegen, the Netherlands. Email: wietske.kievit{at}radboudumc.nl.
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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 | 0.036 | 0.120 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.008 |
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".