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Estimating Confidence Regions of Common Measures of the Baseline and Treatment Effect on Dichotomous Outcome of a Population
Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
2015 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141Article in journal (Refereed) Epub ahead of print
Abstract [en]

In this article we estimate confidence regions of the common measures of (baseline, treatment effect) in observational studies, where the measure of a baseline is baseline risk or baseline odds while the measure of a treatment effect is odds ratio, risk difference, risk ratio or attributable fraction, and where confounding is controlled in estimation of both the baseline and treatment effect. We use only one logistic model to generate approximate distributions of the maximum-likelihood estimates of these measures and thus obtain the maximum-likelihood-based confidence regions for these measures. The method is presented via a real medical example.

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Keyword [en]
Baseline measure, effect measure, confidence region, logistic model
National Category
Probability Theory and Statistics
URN: urn:nbn:se:hig:diva-20369DOI: 10.1080/03610918.2015.1073301ScopusID: 2-s2.0-85006269459OAI: oai:DiVA.org:hig-20369DiVA: diva2:858425
Available from: 2015-10-02 Created: 2015-10-02 Last updated: 2017-01-10Bibliographically approved

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Wang, Xiaoqin
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