A Time-response Measure to Assess Clinical Equivalence in Rheumatoid Arthritis: an Assessment Using Data From Clinical Trials of Biosimilars
Bibliographic record
Abstract
Because of structural complexity, a “biosimilar” will not be exactly the same as its reference biologic treatment, but is required to be equivalent in all relevant attributes, including efficacy. Therapeutic equivalence is often assessed at a single time point and trajectory up to that time point ignored. This article describes a measure to assess therapeutic equivalence in rheumatoid arthritis that takes into account both the trajectory and the peak efficacy. This time-response measure is compared with the standard single-time-point measure via simulations based on recent clinical trials of biosimilars. Scenarios can be constructed where the single-time-point measure is more sensitive in detection of nonequivalence, particularly where the time-response curve is not monotone; but for a variety of trajectories the time-response measure has lower Type II error rate (higher power) for a given Type I error rate. Performance is adversely affected by missing data for both measures. A limitation of the time-response measure is that it assumes a two-parameter exponential model for the trajectory of efficacy over time. Results under poor model fit are also presented. Where similarity of clinical outcome over time is a concern, the time-response measure should be considered when comparing a biosimilar and its reference product.
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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 | 0.240 | 0.423 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".