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Quantifying and estimating additive measures of interaction from case-control data
Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
Vise andre og tillknytning
Rekke forfattare: 62017 (engelsk)Inngår i: Modern stochastics: theory and applications, ISSN 2351-6054, Vol. 4, nr 2, s. 109-125Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In this paper we develop a general framework for quantifying how binary risk factors jointly influence a binary outcome. Our key result is an additive expansion of odds ratios as a sum of marginal effects and interaction terms of varying order. These odds ratio expansions are used for estimating the excess odds ratio, attributable proportion and synergy index for a case-control dataset by means of maximum likelihood from a logistic regression model. The confidence intervals associated with these estimates of joint effects and interaction of risk factors rely on the delta method. Our methodology is illustrated with a large Nordic meta dataset for multiple sclerosis. It combines four studies, with a total of 6265 cases and 8401 controls. It has three risk factors (smoking and two genetic factors) and a number of other confounding variables.

sted, utgiver, år, opplag, sider
2017. Vol. 4, nr 2, s. 109-125
Emneord [en]
Additive odds model, attributable proportion, case-control data, expansion of odds ratios, interaction of risk factors, logistic regression
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Identifikatorer
URN: urn:nbn:se:su:diva-148101DOI: 10.15559/17-VMSTA77ISI: 000411001100003OAI: oai:DiVA.org:su-148101DiVA, id: diva2:1150377
Tilgjengelig fra: 2017-10-18 Laget: 2017-10-18 Sist oppdatert: 2022-03-23bibliografisk kontrollert

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