विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| बेयसियन चरण I नैदानिक परीक्षण× | खुराक-प्रतिक्रिया विश्लेषण× | |
|---|---|---|
| क्षेत्र | महामारी विज्ञान | महामारी विज्ञान |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1990 | Conceptual roots 16th century; modern epidemiological application mid-20th century |
| प्रवर्तक≠ | O'Quigley, Pepe & Fisher (Continual Reassessment Method) | Paracelsus (conceptual foundation); formalized by John Snow and later Bradford Hill |
| प्रकार≠ | Adaptive Bayesian dose-finding design | Quantitative analytical method |
| मौलिक स्रोत≠ | O'Quigley, J., Pepe, M., & Fisher, L. (1990). Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics, 46(1), 33–48. DOI ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| उपनाम | Bayesian dose-finding trial, CRM trial, continual reassessment method trial, Bayesian dose-escalation study | exposure-response analysis, concentration-response modeling, dose-response modeling, DRA |
| संबंधित≠ | 5 | 4 |
| सारांश≠ | A Bayesian Phase I clinical trial uses prior probability models and sequential Bayes updating to find the maximum tolerated dose (MTD) of a new agent. Unlike the traditional 3+3 rule-based escalation, the Bayesian approach revises a dose-toxicity curve continuously as each patient's outcome is observed, allowing faster convergence to the true MTD while minimising exposure of patients to unsafe or subtherapeutic doses. | 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. |
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