Statistical consideration of assessing bioequivalence in the new KFDA regulation
Why this work is in the frame
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Bibliographic record
Abstract
2000년도 의약분업으로 심화된 대체 조제에 대한 논란은 의사의 사전 동의 없이 대체조제 가능 품목을 식품의약품안전청장이 생물학적 동등성이 있다고 인정한 품목으로 제한함으로서 마무리되었다. 이에 발맞추어 식품의약품안전청(KFDA)은 후발 의약품에 대한 생물학적 동등성 시험 기준을 개정하였으며, 이 중에서 눈에 띄는 부분은 통계적인 고려 사항이다. 특히, 대조약과 시험약간의 동등성 판단을 위한 비교항목의 로그 변환을 명시한 것은 통계적 검증의 타당성 확보뿐만 아니라 국제적인 의약품 허가 추세에 부응한 바람직한 조치라 할 수 있다. 본 논문에서는 개정된 생물학적 동등성 시험 기준에서 고려 된 AUC와 Gmax의 로그 변환(log transformation)을 중심으로 그 배경과 통계적 의미를 고찰해 보고, 기존의 생물학적 동등성 판단 기준과 비교 검토해 보고자 한다. 또한 모의실험을 통해 개정된 기준의 통계적 타당성을 확인하고, 올바른 생물학적 동등성 판단을 위한 최소 표본수를 제안해 보기로 한다. On the new medical system separating the prescription and dispensing of the drug, the qualification of pharmacist in substitution of prescribed medicine was restricted, except bioequivalence-certified drugs. Also, Korean Food and Drug Association(KFDA) revised the bioequivalence regulation on August, 2001 Among many changes from old guideline, impressive one is the statistical consideration. Specially, to estimate and analyze bioequivalence measures, AUC and <TEX>$C_{max}$</TEX>, the log-transformed model is recommended and the equivalence interval is modified from <TEX>$\pm$</TEX>20 rule to [In(0.8),In(1.25)] one. This meaningful act is very hope-for because it is statistically reasonable and is agreed with worldwide bioequivalence guideline, including USA, EU, Japan and Canada. In this paper, we introduce the new regulation of assessing bioequivalence, announced at August, 2001, mainly on statistical view points. Key points for the new regulation are discussed and the minimum sample size based on simulation studies are proposed.
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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 | 0.004 | 0.033 |
| 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) | 0.001 | 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 it