Salīdzināt metodes

	Risku koriģēts I fāzes klīniskais pētījums	Deva-atbildes analīze
Nozare	Epidemioloģija	Epidemioloģija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1990s–2000s	Conceptual roots 16th century; modern epidemiological application mid-20th century
Autors	Evolved from the Continual Reassessment Method (O'Quigley et al., 1990) extended with patient-level risk covariates	Paracelsus (conceptual foundation); formalized by John Snow and later Bradford Hill
Tips	Interventional clinical trial design	Quantitative analytical method
Pirmavots	Iasonos, A., Wilton, A. S., & Gonen, M. (2008). A review of stochastic dose-finding methods. Statistics in Medicine, 27(25), 5031–5046.	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641
Citi nosaukumi	risk-stratified Phase I trial, risk-adaptive dose-escalation study, covariate-adjusted Phase I study, risk-based dose-finding trial	exposure-response analysis, concentration-response modeling, dose-response modeling, DRA
Saistītās	5	4
Kopsavilkums	A risk-adjusted Phase I clinical trial is a first-in-human or dose-finding study that explicitly incorporates patient-level risk covariates — such as organ function, prior therapy, or genetic markers — into the dose-escalation model. Rather than treating all enrolled participants as homogeneous, the design accounts for individual differences in tolerance, allowing the recommended dose to vary by risk stratum. This approach is especially common in oncology, where patients with impaired renal function or heavily pre-treated disease may tolerate lower doses than the broader population.	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.