Loading…

Reflection on modern methods: risk ratio regression-simple concept yet complex computation

The risk ratio (RR) is the ratio of the outcome among the exposed to risk of the outcome among the unexposed. This is a simple concept, which makes one wonder why it has not gained the same popularity as the odds ratio. Using logistic regression to estimate the odds ratio is quite common in epidemio...

Full description

Saved in:
Bibliographic Details
Published in:International journal of epidemiology 2023-02, Vol.52 (1), p.309-314
Main Authors: Mittinty, Murthy N, Lynch, John
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c381t-f28823f421a7a81114b4c2abcb40ece92d42faa5c23ec1e091a168ef8a63bf403
cites cdi_FETCH-LOGICAL-c381t-f28823f421a7a81114b4c2abcb40ece92d42faa5c23ec1e091a168ef8a63bf403
container_end_page 314
container_issue 1
container_start_page 309
container_title International journal of epidemiology
container_volume 52
creator Mittinty, Murthy N
Lynch, John
description The risk ratio (RR) is the ratio of the outcome among the exposed to risk of the outcome among the unexposed. This is a simple concept, which makes one wonder why it has not gained the same popularity as the odds ratio. Using logistic regression to estimate the odds ratio is quite common in epidemiology and interpreting the odds ratio as a risk ratio, under the assumption that the outcome is rare, is also common. On one hand, estimating the odds ratio is simple but interpreting it is hard. On the other, estimating the risk ratio is challenging but its interpretation is straightforward. Issues with estimating risk ratio still remain after four decades. These issues include convergence of the algorithm, the choice of regression specification (e.g. log-binomial, Poisson) and many more. Various new computational methods are available which help overcome the issue of convergence and provide doubly robust estimates of RR.
doi_str_mv 10.1093/ije/dyac220
format article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9908057</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2739431684</sourcerecordid><originalsourceid>FETCH-LOGICAL-c381t-f28823f421a7a81114b4c2abcb40ece92d42faa5c23ec1e091a168ef8a63bf403</originalsourceid><addsrcrecordid>eNpVkd1LwzAUxYMobk6ffJc-ClKXr7apD4IMv2AgiL74EtL0dstsm5m04v57MzeHQuCEe38595KD0CnBlwTnbGwWMC5XSlOK99CQ8JTHLBXJPhpihnGcZBkZoCPvFxgTznl-iAYs5STlLBuit2eoatCdsW0UTmNLcEGgm9vSX0XO-PfIqdCOHMwceB_A2JtmWUOkbath2UUr6MJ9Xfr60b5bP2iP0UGlag8nWx2h17vbl8lDPH26f5zcTGPNBOniigpBWcUpUZkShBBecE1VoQuOQUNOS04rpRJNGWgCOCeKpAIqoVJWVByzEbre-C77ooFSQ9s5VculM41yK2mVkf87rZnLmf2UeY4FTrJgcL41cPajB9_JxngNda1asL2XNGM5Z2EoD-jFBtXOeu-g2o0hWK7TkCENuU0j0Gd_N9uxv9_PvgGG6YpC</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2739431684</pqid></control><display><type>article</type><title>Reflection on modern methods: risk ratio regression-simple concept yet complex computation</title><source>Oxford Journals Online</source><creator>Mittinty, Murthy N ; Lynch, John</creator><creatorcontrib>Mittinty, Murthy N ; Lynch, John</creatorcontrib><description>The risk ratio (RR) is the ratio of the outcome among the exposed to risk of the outcome among the unexposed. This is a simple concept, which makes one wonder why it has not gained the same popularity as the odds ratio. Using logistic regression to estimate the odds ratio is quite common in epidemiology and interpreting the odds ratio as a risk ratio, under the assumption that the outcome is rare, is also common. On one hand, estimating the odds ratio is simple but interpreting it is hard. On the other, estimating the risk ratio is challenging but its interpretation is straightforward. Issues with estimating risk ratio still remain after four decades. These issues include convergence of the algorithm, the choice of regression specification (e.g. log-binomial, Poisson) and many more. Various new computational methods are available which help overcome the issue of convergence and provide doubly robust estimates of RR.</description><identifier>ISSN: 0300-5771</identifier><identifier>EISSN: 1464-3685</identifier><identifier>DOI: 10.1093/ije/dyac220</identifier><identifier>PMID: 36416437</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Education Corner ; Humans ; Logistic Models ; Odds Ratio</subject><ispartof>International journal of epidemiology, 2023-02, Vol.52 (1), p.309-314</ispartof><rights>The Author(s) 2022. Published by Oxford University Press on behalf of the International Epidemiological Association.</rights><rights>The Author(s) 2022. Published by Oxford University Press on behalf of the International Epidemiological Association. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c381t-f28823f421a7a81114b4c2abcb40ece92d42faa5c23ec1e091a168ef8a63bf403</citedby><cites>FETCH-LOGICAL-c381t-f28823f421a7a81114b4c2abcb40ece92d42faa5c23ec1e091a168ef8a63bf403</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36416437$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mittinty, Murthy N</creatorcontrib><creatorcontrib>Lynch, John</creatorcontrib><title>Reflection on modern methods: risk ratio regression-simple concept yet complex computation</title><title>International journal of epidemiology</title><addtitle>Int J Epidemiol</addtitle><description>The risk ratio (RR) is the ratio of the outcome among the exposed to risk of the outcome among the unexposed. This is a simple concept, which makes one wonder why it has not gained the same popularity as the odds ratio. Using logistic regression to estimate the odds ratio is quite common in epidemiology and interpreting the odds ratio as a risk ratio, under the assumption that the outcome is rare, is also common. On one hand, estimating the odds ratio is simple but interpreting it is hard. On the other, estimating the risk ratio is challenging but its interpretation is straightforward. Issues with estimating risk ratio still remain after four decades. These issues include convergence of the algorithm, the choice of regression specification (e.g. log-binomial, Poisson) and many more. Various new computational methods are available which help overcome the issue of convergence and provide doubly robust estimates of RR.</description><subject>Education Corner</subject><subject>Humans</subject><subject>Logistic Models</subject><subject>Odds Ratio</subject><issn>0300-5771</issn><issn>1464-3685</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpVkd1LwzAUxYMobk6ffJc-ClKXr7apD4IMv2AgiL74EtL0dstsm5m04v57MzeHQuCEe38595KD0CnBlwTnbGwWMC5XSlOK99CQ8JTHLBXJPhpihnGcZBkZoCPvFxgTznl-iAYs5STlLBuit2eoatCdsW0UTmNLcEGgm9vSX0XO-PfIqdCOHMwceB_A2JtmWUOkbath2UUr6MJ9Xfr60b5bP2iP0UGlag8nWx2h17vbl8lDPH26f5zcTGPNBOniigpBWcUpUZkShBBecE1VoQuOQUNOS04rpRJNGWgCOCeKpAIqoVJWVByzEbre-C77ooFSQ9s5VculM41yK2mVkf87rZnLmf2UeY4FTrJgcL41cPajB9_JxngNda1asL2XNGM5Z2EoD-jFBtXOeu-g2o0hWK7TkCENuU0j0Gd_N9uxv9_PvgGG6YpC</recordid><startdate>20230208</startdate><enddate>20230208</enddate><creator>Mittinty, Murthy N</creator><creator>Lynch, John</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20230208</creationdate><title>Reflection on modern methods: risk ratio regression-simple concept yet complex computation</title><author>Mittinty, Murthy N ; Lynch, John</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c381t-f28823f421a7a81114b4c2abcb40ece92d42faa5c23ec1e091a168ef8a63bf403</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Education Corner</topic><topic>Humans</topic><topic>Logistic Models</topic><topic>Odds Ratio</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mittinty, Murthy N</creatorcontrib><creatorcontrib>Lynch, John</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal of epidemiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mittinty, Murthy N</au><au>Lynch, John</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reflection on modern methods: risk ratio regression-simple concept yet complex computation</atitle><jtitle>International journal of epidemiology</jtitle><addtitle>Int J Epidemiol</addtitle><date>2023-02-08</date><risdate>2023</risdate><volume>52</volume><issue>1</issue><spage>309</spage><epage>314</epage><pages>309-314</pages><issn>0300-5771</issn><eissn>1464-3685</eissn><abstract>The risk ratio (RR) is the ratio of the outcome among the exposed to risk of the outcome among the unexposed. This is a simple concept, which makes one wonder why it has not gained the same popularity as the odds ratio. Using logistic regression to estimate the odds ratio is quite common in epidemiology and interpreting the odds ratio as a risk ratio, under the assumption that the outcome is rare, is also common. On one hand, estimating the odds ratio is simple but interpreting it is hard. On the other, estimating the risk ratio is challenging but its interpretation is straightforward. Issues with estimating risk ratio still remain after four decades. These issues include convergence of the algorithm, the choice of regression specification (e.g. log-binomial, Poisson) and many more. Various new computational methods are available which help overcome the issue of convergence and provide doubly robust estimates of RR.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>36416437</pmid><doi>10.1093/ije/dyac220</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0300-5771
ispartof International journal of epidemiology, 2023-02, Vol.52 (1), p.309-314
issn 0300-5771
1464-3685
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9908057
source Oxford Journals Online
subjects Education Corner
Humans
Logistic Models
Odds Ratio
title Reflection on modern methods: risk ratio regression-simple concept yet complex computation
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T05%3A19%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Reflection%20on%20modern%20methods:%20risk%20ratio%20regression-simple%20concept%20yet%20complex%20computation&rft.jtitle=International%20journal%20of%20epidemiology&rft.au=Mittinty,%20Murthy%20N&rft.date=2023-02-08&rft.volume=52&rft.issue=1&rft.spage=309&rft.epage=314&rft.pages=309-314&rft.issn=0300-5771&rft.eissn=1464-3685&rft_id=info:doi/10.1093/ije/dyac220&rft_dat=%3Cproquest_pubme%3E2739431684%3C/proquest_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c381t-f28823f421a7a81114b4c2abcb40ece92d42faa5c23ec1e091a168ef8a63bf403%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2739431684&rft_id=info:pmid/36416437&rfr_iscdi=true