Literature DB >> 15618523

Additive and multiplicative models for the joint effect of two risk factors.

A Berrington de González1, D R Cox.   

Abstract

Simple tests are given for consistency of the data with additive and with multiplicative effects of two risk factors on a binary outcome. A combination of the procedures will show whether data are consistent with neither, one or both of the models of no additive or no multiplicative interaction. Implications for the size of the study needed to detect differences between the models are also addressed. Because of the simple form of the test statistics, combination of evidence from different studies or strata is straightforward. Illustration of how the method could be extended to data from a 2xRxC table is also given.

Mesh:

Year:  2005        PMID: 15618523     DOI: 10.1093/biostatistics/kxh024

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  8 in total

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Journal:  Int J Epidemiol       Date:  2017-04-01       Impact factor: 7.196

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6.  Predicting breast cancer risk using interacting genetic and demographic factors and machine learning.

Authors:  Veli-Matti Kosma; Arto Mannermaa; Hamid Behravan; Jaana M Hartikainen; Maria Tengström
Journal:  Sci Rep       Date:  2020-07-06       Impact factor: 4.379

7.  The synergy factor: a statistic to measure interactions in complex diseases.

Authors:  Mario Cortina-Borja; A David Smith; Onofre Combarros; Donald J Lehmann
Journal:  BMC Res Notes       Date:  2009-06-15

8.  CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer.

Authors:  Mark D M Leiserson; Hsin-Ta Wu; Fabio Vandin; Benjamin J Raphael
Journal:  Genome Biol       Date:  2015-08-08       Impact factor: 13.583

  8 in total

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