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Measuring and estimating the interaction between exposures on a dichotomous outcome for observational studies
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics.
Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
2017 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 44, no 14, p. 2483-2498Article in journal (Refereed) Published
##### Abstract [en]

In observational studies for the interaction between exposures on a dichotomous outcome of a certain population, usually one parameter of a regression model is used to describe the interaction, leading to one measure of the interaction. In this article we use the conditional risk of an outcome given exposures and covariates to describe the interaction and obtain five different measures of the interaction, that is, difference between the marginal risk differences, ratio of the marginal risk ratios, ratio of the marginal odds ratios, ratio of the conditional risk ratios, and ratio of the conditional odds ratios. These measures reflect different aspects of the interaction. By using only one regression model for the conditional risk, we obtain the maximum-likelihood (ML)-based point and interval estimates of these measures, which are most efficient due to the nature of ML. We use the ML estimates of the model parameters to obtain the ML estimates of these measures. We use the approximate normal distribution of the ML estimates of the model parameters to obtain approximate non-normal distributions of the ML estimates of these measures and then confidence intervals of these measures. The method can be easily implemented and is presented via a medical example.

##### Place, publisher, year, edition, pages
2017. Vol. 44, no 14, p. 2483-2498
##### Keyword [en]
Interaction, interaction measure, point estimate, interval estimate, regression model
##### National Category
Probability Theory and Statistics
##### Identifiers
ISI: 000410837000002Scopus ID: 2-s2.0-84996537841OAI: oai:DiVA.org:hig-18760DiVA: diva2:780675
Available from: 2015-01-14 Created: 2015-01-14 Last updated: 2018-03-13Bibliographically approved

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Wang, Xiaoqin

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Wang, Xiaoqin
Mathematics
##### In the same journal
Journal of Applied Statistics
##### On the subject
Probability Theory and Statistics

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Cite
Citation style
• apa
• harvard-cite-them-right
• ieee
• modern-language-association-8th-edition
• vancouver
• Other style
More styles
Language
• sv-SE
• en-GB
• en-US
• fi-FI
• nn-NO
• nn-NB
• de-DE
• Other locale
More languages
Output format
• html
• text
• asciidoc
• rtf