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Using inverse weighting and predictive inference to estimate the effects of time‐varying treatments on the discrete‐time hazard
Author(s) -
Dawson Ree,
Lavori Philip W.
Publication year - 2002
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1111
Subject(s) - inverse probability weighting , weighting , inference , statistics , hazard , hazard ratio , econometrics , proportional hazards model , predictive inference , truncation (statistics) , mathematics , computer science , propensity score matching , confidence interval , medicine , bayesian inference , bayesian probability , artificial intelligence , chemistry , organic chemistry , radiology , frequentist inference
We estimate the effects of non‐randomized time‐varying treatments on the discrete‐time hazard, using inverse weighting. We consider the special monotone pattern of treatment that develops over time as subjects permanently discontinue an initial treatment, and assume that treatment selection is sequentially ignorable. We use a propensity score in the hazard model to reduce the potential for finite‐sample bias due to inverse weighting. When the number of subjects who discontinue treatment at any given time is small, we impose scientific restrictions on the potentially observable discontinuation hazards to improve efficiency. We use predictive inference to account for the correlation of the potential hazards, when comparing outcomes under different durations of initial treatment. Copyright © 2002 John Wiley & Sons, Ltd.