Premium
Twice‐weighted multiple interval estimation of a marginal structural model to analyze cost‐effectiveness
Author(s) -
Goldfeld K.S.
Publication year - 2013
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.6017
Subject(s) - marginal structural model , causal inference , estimation , observational study , interval estimation , computer science , confounding , inference , interval (graph theory) , econometrics , marginal cost , confidence interval , medicine , statistics , mathematics , artificial intelligence , economics , management , combinatorics , microeconomics
Cost‐effectiveness analysis is an important tool that can be applied to the evaluation of a health treatment or policy. When the observed costs and outcomes result from a nonrandomized treatment, making causal inference about the effects of the treatment requires special care. The challenges are compounded when the observation period is truncated for some of the study subjects. This paper presents a method of unbiased estimation of cost‐effectiveness using observational study data that is not fully observed. The method—twice‐weighted multiple interval estimation of a marginal structural model—was developed in order to analyze the cost‐effectiveness of treatment protocols for advanced dementia residents living nursing homes when they become acutely ill. A key feature of this estimation approach is that it facilitates a sensitivity analysis that identifies the potential effects of unmeasured confounding on the conclusions concerning cost‐effectiveness. Copyright © 2013 John Wiley & Sons, Ltd.