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Measuring and estimating treatment effect on count outcome in randomized trial and observational studies
Karolinska Institute, Department of Epidemiology and Biostatistics.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electronics, Mathematics and Natural Sciences, Mathematics. (Matematik)
2015 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 44, no 5, p. 1080-1095Article in journal (Refereed) Published
Abstract [en]

When estimating treatment effect on count outcome of given population, one uses different models in different studies, resulting in non-comparable measures of treatment effect. Here we show that the marginal rate differences in these studies are comparable measures of treatment effect. We estimate the marginal rate differences by log-linear models and show that their finite-sample maximum-likelihood estimates are unbiased and highly robust with respect to effects of dispersing covariates on outcome. We get approximate finite-sample distributions of these estimates by using the asymptotic normal distribution of estimates of the log-linear model parameters. This method can be easily applied to practice.

Place, publisher, year, edition, pages
2015. Vol. 44, no 5, p. 1080-1095
Keywords [en]
Treatment effect measure, Marginal rate difference, Finite-sample estimate
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hig:diva-14076DOI: 10.1080/03610926.2013.776686ISI: 000351220400015Scopus ID: 2-s2.0-84924976048OAI: oai:DiVA.org:hig-14076DiVA, id: diva2:615354
Available from: 2013-04-10 Created: 2013-04-09 Last updated: 2018-03-13Bibliographically approved

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Wang, Xiaoqin

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  • en-US
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  • nn-NB
  • de-DE
  • Other locale
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  • asciidoc
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