পদ্ধতির তুলনা করুন

	ঝুঁকি-সমন্বিত চতুর্থ ধাপের গবেষণা	ইনভার্স প্রোবাবিলিটি অফ ট্রিটমেন্ট ওয়েটিং (IPW / IPTW)
ক্ষেত্র	মহামারীবিদ্যা	কার্যকারণ অনুমান
পরিবার	Process / pipeline	Regression model
উদ্ভবের বছর	1990s–2000s (formalized with ICH E2E and EMA PASS guidelines)	2000
প্রবর্তক	Regulatory and pharmacoepidemiology community (ICH, EMA, FDA frameworks)	Robins, Hernán & Brumback
ধরন	Observational / quasi-experimental clinical study design	Causal inference weighting estimator
মৌলিক উৎস	Strom, B. L. (Ed.). (2005). Pharmacoepidemiology (4th ed.). John Wiley & Sons. ISBN: 978-0470863107	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
অপর নাম	risk-adjusted post-marketing surveillance study, adjusted Phase IV trial, risk-stratified post-authorization study, PASS with risk adjustment	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
সম্পর্কিত	3	5
সারসংক্ষেপ	A risk-adjusted Phase IV study is an observational or semi-experimental post-marketing study conducted after a drug or device has received regulatory approval. It uses statistical risk-adjustment techniques — such as propensity score matching, inverse probability weighting, or multivariable regression — to control for confounding by indication and baseline patient differences, thereby producing more credible safety, effectiveness, and utilization estimates than unadjusted real-world analyses.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.