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| 적응형 무작위 배정 임상시험× | 용량-반응 분석× | |
|---|---|---|
| 분야 | 역학 | 역학 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | Late 1990s–2000s (widespread adoption post-2010) | Conceptual roots 16th century; modern epidemiological application mid-20th century |
| 창시자≠ | Donald Berry and colleagues; formalized by FDA guidance in 2010 and 2019 | Paracelsus (conceptual foundation); formalized by John Snow and later Bradford Hill |
| 유형≠ | Experimental clinical trial design | Quantitative analytical method |
| 원전≠ | Berry, D. A. (2006). Bayesian clinical trials. Nature Reviews Drug Discovery, 5(1), 27–36. DOI ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| 별칭 | adaptive RCT, adaptive trial design, response-adaptive randomization trial, adaptive clinical trial | exposure-response analysis, concentration-response modeling, dose-response modeling, DRA |
| 관련≠ | 6 | 4 |
| 요약≠ | An adaptive randomized clinical trial (adaptive RCT) is a prospective experimental study that uses pre-specified rules to modify one or more trial aspects — such as sample size, allocation ratios, or treatment arms — based on accumulating data collected during the trial itself, while maintaining statistical validity and integrity of the study. | Dose-response analysis quantifies the relationship between the magnitude of an exposure (the dose) and the probability or rate of an outcome (the response). It is a core analytical strategy in epidemiology and toxicology, providing evidence that increasing exposure systematically increases — or decreases — the risk of disease. A demonstrated dose-response gradient is one of Bradford Hill's classic criteria supporting causal inference. |
| ScholarGate데이터셋 ↗ |
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