Application of complete N-of-1 trial design in bioequivalence-biosimilar drug development
Bibliographic record
Abstract
Biosimilars play a crucial role in increasing the accessibility and affordability of biological therapies; thus, precise and reliable assessment methods are essential for their regulatory approval and clinical adoption. Currently, the 2-sequence 2-period crossover design is recommended for two-treatment biosimilar studies. However, such designs may be inadequate for the practical assessment when multiple test or reference products are involved, particularly in scenarios such as: (1) bridging biosimilar results across regulatory regions (e.g. the European Union, Canada, and United States), or (2) evaluating biosimilarity across different dosage forms or routes of administration. To address these challenges, multi-treatment designs such as Latin-square design, Williams design, and balanced incomplete block design can be considered. More recently, the complete N-of-1 trial design, which contains all permutations of treatments with replacement, has gained attention in biosimilar drug development, especially with the presence of carryover effects. However, detailed statistical methodologies and comprehensive performance comparisons of these designs are lacking in the context of multi-formulation studies. This study employs a linear mixed-effects model to estimate the contrast of treatment effects across three drug products within the framework of the designs under investigation. Subsequently, the relationship between sample size and relative efficiency is explored under same significance level and statistical power. The findings indicate that, for a given sample size, the complete N-of-1 design consistently achieves the lowest estimation variance relative to the alternative designs, thereby representing a more efficient design for biosimilar assessment under the conditions examined.
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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 | 0.178 | 0.185 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".