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A blended link approach to relative risk regression

Clark, Robert; Barr, Margo

Description

A binary health outcome may be regressed on covariates using a log link, rather than more typical link functions such as the logit. This allows the exponentiated regression coefficient for each covariate to be interpreted as a relative risk conditional on the remaining covariates. Relative risks are simpler to interpret than the odds ratios which arise with a logit link. There are practical and conceptual challenges in log-link binary regression, mainly due to the requirement that probabilities...[Show more]

dc.contributor.authorClark, Robert
dc.contributor.authorBarr, Margo
dc.date.accessioned2020-12-20T20:51:27Z
dc.date.available2020-12-20T20:51:27Z
dc.identifier.issn0962-2802
dc.identifier.urihttp://hdl.handle.net/1885/217784
dc.description.abstractA binary health outcome may be regressed on covariates using a log link, rather than more typical link functions such as the logit. This allows the exponentiated regression coefficient for each covariate to be interpreted as a relative risk conditional on the remaining covariates. Relative risks are simpler to interpret than the odds ratios which arise with a logit link. There are practical and conceptual challenges in log-link binary regression, mainly due to the requirement that probabilities are less than or equal to 1. Viable probabilities are now usually achieved by the imposition of a constraint on the parameter space, but the log link function is still more work to apply in practice. We propose instead a new smooth link function which is equal to the log up to a cutoff and a linearly scaled logit function above the cutoff. The new approach is conceptually clearer, simpler to implement and generally less biased, and it retains the relative risk interpretation for all but the highest risk individuals. Alternative binary regressions are compared using a simulation study and a diabetic retinopathy dataset.
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherArnold Publishers
dc.sourceStatistical Methods in Medical Research
dc.titleA blended link approach to relative risk regression
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolumeOnline
dc.date.issued2017
local.identifier.absfor010401 - Applied Statistics
local.identifier.absfor010405 - Statistical Theory
local.identifier.ariespublicationu5586678xPUB5
local.type.statusPublished Version
local.contributor.affiliationClark, Robert, Administrative Portfolio, ANU
local.contributor.affiliationBarr, Margo, New South Wales Department of Health
local.identifier.doi10.1177/0962280217698174
dc.date.updated2020-11-23T10:04:32Z
CollectionsANU Research Publications

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