Estimating measures of interaction on an additive scale for preventive exposures
Measures of interaction on an additive scale (relative excess risk due to interaction [RERI], attributable proportion [AP], synergy index [S]), were developed for risk factors rather than preventive factors. It has been suggested that preventive factors should be recoded to risk factors before calcu...
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| Published in | European journal of epidemiology Vol. 26; no. 6; pp. 433 - 438 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer
01.06.2011
Springer Netherlands Springer Nature B.V Springer Verlag |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0393-2990 1573-7284 1573-7284 |
| DOI | 10.1007/s10654-011-9554-9 |
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| Abstract | Measures of interaction on an additive scale (relative excess risk due to interaction [RERI], attributable proportion [AP], synergy index [S]), were developed for risk factors rather than preventive factors. It has been suggested that preventive factors should be recoded to risk factors before calculating these measures. We aimed to show that these measures are problematic with preventive factors prior to recoding, and to clarify the recoding method to be used to circumvent these problems. Recoding of preventive factors should be done such that the stratum with the lowest risk becomes the reference category when both factors are considered jointly (rather than one at a time). We used data from a case-control study on the interaction between ACE inhibitors and the ACE gene on incident diabetes. Use of ACE inhibitors was a preventive factor and DD ACE genotype was a risk factor. Before recoding, the RERI, AP and S showed inconsistent results (RERI = 0.26 [95% CI: -0.30; 0.82], AP = 0.30 [95% CI: -0.28; 0.88], S = 0.35 [95% CI: 0.02; 7.38]), with the first two measures suggesting positive interaction and the third negative interaction. After recoding the use of ACE inhibitors, they showed consistent results (RERI = -0.37 [95% CI: -1.23; 0.49], AP = -0.29 [95% CI: -0.98; 0.40], S = 0.43 [95% CI: 0.07; 2.60]), all indicating negative interaction. Preventive factors should not be used to calculate measures of interaction on an additive scale without recoding. |
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| AbstractList | Measures of interaction on an additive scale (relative excess risk due to interaction [RERI], attributable proportion [AP], synergy index [S]), were developed for risk factors rather than preventive factors. It has been suggested that preventive factors should be recoded to risk factors before calculating these measures. We aimed to show that these measures are problematic with preventive factors prior to recoding, and to clarify the recoding method to be used to circumvent these problems. Recoding of preventive factors should be done such that the stratum with the lowest risk becomes the reference category when both factors are considered jointly (rather than one at a time). We used data from a case-control study on the interaction between ACE inhibitors and the ACE gene on incident diabetes. Use of ACE inhibitors was a preventive factor and DD ACE genotype was a risk factor. Before recoding, the RERI, AP and S showed inconsistent results (RERI = 0.26 [95%CI: -0.30; 0.82], AP = 0.30 [95%CI: -0.28; 0.88], S = 0.35 [95%CI: 0.02; 7.38]), with the first two measures suggesting positive interaction and the third negative interaction. After recoding the use of ACE inhibitors, they showed consistent results (RERI = -0.37 [95%CI: -1.23; 0.49], AP = -0.29 [95%CI: -0.98; 0.40], S = 0.43 [95%CI: 0.07; 2.60]), all indicating negative interaction. Preventive factors should not be used to calculate measures of interaction on an additive scale without recoding.Measures of interaction on an additive scale (relative excess risk due to interaction [RERI], attributable proportion [AP], synergy index [S]), were developed for risk factors rather than preventive factors. It has been suggested that preventive factors should be recoded to risk factors before calculating these measures. We aimed to show that these measures are problematic with preventive factors prior to recoding, and to clarify the recoding method to be used to circumvent these problems. Recoding of preventive factors should be done such that the stratum with the lowest risk becomes the reference category when both factors are considered jointly (rather than one at a time). We used data from a case-control study on the interaction between ACE inhibitors and the ACE gene on incident diabetes. Use of ACE inhibitors was a preventive factor and DD ACE genotype was a risk factor. Before recoding, the RERI, AP and S showed inconsistent results (RERI = 0.26 [95%CI: -0.30; 0.82], AP = 0.30 [95%CI: -0.28; 0.88], S = 0.35 [95%CI: 0.02; 7.38]), with the first two measures suggesting positive interaction and the third negative interaction. After recoding the use of ACE inhibitors, they showed consistent results (RERI = -0.37 [95%CI: -1.23; 0.49], AP = -0.29 [95%CI: -0.98; 0.40], S = 0.43 [95%CI: 0.07; 2.60]), all indicating negative interaction. Preventive factors should not be used to calculate measures of interaction on an additive scale without recoding. Measures of interaction on an additive scale (relative excess risk due to interaction [RERI], attributable proportion [AP], synergy index [S]), were developed for risk factors rather than preventive factors. It has been suggested that preventive factors should be recoded to risk factors before calculating these measures. We aimed to show that these measures are problematic with preventive factors prior to recoding, and to clarify the recoding method to be used to circumvent these problems. Recoding of preventive factors should be done such that the stratum with the lowest risk becomes the reference category when both factors are considered jointly (rather than one at a time). We used data from a case-control study on the interaction between ACE inhibitors and the ACE gene on incident diabetes. Use of ACE inhibitors was a preventive factor and DD ACE genotype was a risk factor. Before recoding, the RERI, AP and S showed inconsistent results (RERI = 0.26 [95% CI: -0.30; 0.82], AP = 0.30 [95% CI: -0.28; 0.88], S = 0.35 [95% CI: 0.02; 7.38]), with the first two measures suggesting positive interaction and the third negative interaction. After recoding the use of ACE inhibitors, they showed consistent results (RERI = -0.37 [95% CI: -1.23; 0.49], AP = -0.29 [95% CI: -0.98; 0.40], S = 0.43 [95% CI: 0.07; 2.60]), all indicating negative interaction. Preventive factors should not be used to calculate measures of interaction on an additive scale without recoding. Measures of interaction on an additive scale (relative excess risk due to interaction [RERI], attributable proportion [AP], synergy index [S]), were developed for risk factors rather than preventive factors. It has been suggested that preventive factors should be recoded to risk factors before calculating these measures. We aimed to show that these measures are problematic with preventive factors prior to recoding, and to clarify the recoding method to be used to circumvent these problems. Recoding of preventive factors should be done such that the stratum with the lowest risk becomes the reference category when both factors are considered jointly (rather than one at a time). We used data from a case-control study on the interaction between ACE inhibitors and the ACE gene on incident diabetes. Use of ACE inhibitors was a preventive factor and DD ACE genotype was a risk factor. Before recoding, the RERI, AP and S showed inconsistent results (RERI = 0.26 [95%CI: -0.30; 0.82], AP = 0.30 [95%CI: -0.28; 0.88], S = 0.35 [95%CI: 0.02; 7.38]), with the first two measures suggesting positive interaction and the third negative interaction. After recoding the use of ACE inhibitors, they showed consistent results (RERI = -0.37 [95%CI: -1.23; 0.49], AP = -0.29 [95%CI: -0.98; 0.40], S = 0.43 [95%CI: 0.07; 2.60]), all indicating negative interaction. Preventive factors should not be used to calculate measures of interaction on an additive scale without recoding. Measures of interaction on an additive scale (relative excess risk due to interaction [RERI], attributable proportion [AP], synergy index [S]), were developed for risk factors rather than preventive factors. It has been suggested that preventive factors should be recoded to risk factors before calculating these measures. We aimed to show that these measures are problematic with preventive factors prior to recoding, and to clarify the recoding method to be used to circumvent these problems. Recoding of preventive factors should be done such that the stratum with the lowest risk becomes the reference category when both factors are considered jointly (rather than one at a time). We used data from a case-control study on the interaction between ACE inhibitors and the ACE gene on incident diabetes. Use of ACE inhibitors was a preventive factor and DD ACE genotype was a risk factor. Before recoding, the RERI, AP and S showed inconsistent results (RERI = 0.26 [95%CI: −0.30; 0.82], AP = 0.30 [95%CI: −0.28; 0.88], S = 0.35 [95%CI: 0.02; 7.38]), with the first two measures suggesting positive interaction and the third negative interaction. After recoding the use of ACE inhibitors, they showed consistent results (RERI = −0.37 [95%CI: −1.23; 0.49], AP = −0.29 [95%CI: −0.98; 0.40], S = 0.43 [95%CI: 0.07; 2.60]), all indicating negative interaction. Preventive factors should not be used to calculate measures of interaction on an additive scale without recoding. Measures of interaction on an additive scale (relative excess risk due to interaction [RERI], attributable proportion [AP], synergy index [S]), were developed for risk factors rather than preventive factors. It has been suggested that preventive factors should be recoded to risk factors before calculating these measures. We aimed to show that these measures are problematic with preventive factors prior to recoding, and to clarify the recoding method to be used to circumvent these problems. Recoding of preventive factors should be done such that the stratum with the lowest risk becomes the reference category when both factors are considered jointly (rather than one at a time). We used data from a case-control study on the interaction between ACE inhibitors and the ACE gene on incident diabetes. Use of ACE inhibitors was a preventive factor and DD ACE genotype was a risk factor. Before recoding, the RERI, AP and S showed inconsistent results (RERI = 0.26 [95%CI: -0.30; 0.82], AP = 0.30 [95%CI: -0.28; 0.88], S = 0.35 [95%CI: 0.02; 7.38]), with the first two measures suggesting positive interaction and the third negative interaction. After recoding the use of ACE inhibitors, they showed consistent results (RERI = -0.37 [95%CI: -1.23; 0.49], AP = -0.29 [95%CI: -0.98; 0.40], S = 0.43 [95%CI: 0.07; 2.60]), all indicating negative interaction. Preventive factors should not be used to calculate measures of interaction on an additive scale without recoding.[PUBLICATION ABSTRACT] |
| Author | Klungel, Olaf H. Rovers, Maroeska M. Grobbee, Diederick E. Knol, Mirjam J. Groenwold, Rolf H. H. VanderWeele, Tyler J. |
| Author_xml | – sequence: 1 givenname: Mirjam J. surname: Knol fullname: Knol, Mirjam J. – sequence: 2 givenname: Tyler J. surname: VanderWeele fullname: VanderWeele, Tyler J. – sequence: 3 givenname: Rolf H. H. surname: Groenwold fullname: Groenwold, Rolf H. H. – sequence: 4 givenname: Olaf H. surname: Klungel fullname: Klungel, Olaf H. – sequence: 5 givenname: Maroeska M. surname: Rovers fullname: Rovers, Maroeska M. – sequence: 6 givenname: Diederick E. surname: Grobbee fullname: Grobbee, Diederick E. |
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| Keywords | Relative excess risk due to interaction Synergy index Preventive factors Interaction Human Evaluation scale Estimation Exposure Index Epidemiology Relative risk Prevention Additive Public health |
| Language | English |
| License | CC BY 4.0 Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. |
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| References | MarenholzIKerscherTBauerfeindAAn interaction between filaggrin mutations and early food sensitization improves the prediction of childhood asthmaJ Allergy Clin Immunol20091239119161934892610.1016/j.jaci.2009.01.0511:CAS:528:DC%2BD1MXktV2itrw%3D ZouGYOn the estimation of additive interaction by use of the four-by-two table and beyondAm J Epidemiol20081682122241851142810.1093/aje/kwn104 RothmanKJMeasuring interactions. Epidemiology: an introduction2002OxfordUniversity Press168180 VanderweeleTJRobinsJMThe identification of synergism in the sufficient-component-cause frameworkEpidemiology2007183293391743544110.1097/01.ede.0000260218.66432.88 RichardsonDBKaufmanJSEstimation of the relative excess risk due to interaction and associated confidence boundsAm J Epidemiol20091697567601921162010.1093/aje/kwn411 YangXMaRCSoWYWhite blood cell count and renin-angiotensin system inhibitors for the risk of cancer in type 2 diabetesDiabetes Res Clin Pract2010871171251993251910.1016/j.diabres.2009.10.0121:CAS:528:DC%2BC3cXntVaqsw%3D%3D HosmerDWLemeshowSConfidence interval estimation of interactionEpidemiology19923452456139113910.1097/00001648-199209000-000121:STN:280:DyaK3s%2FgtlKhug%3D%3D AssmannSFHosmerDWLemeshowSMundtKAConfidence intervals for measures of interactionEpidemiology19967286290872844310.1097/00001648-199605000-000121:STN:280:DyaK283pt1Onsw%3D%3D GreenlandSRothmanKJConcepts of interaction. Modern epidemiology1998PhiladelphiaLippincott-Raven Publishers329342 RothmanKJThe estimation of synergy or antagonismAm J Epidemiol197610350651112749521:STN:280:DyaE283gtlahtA%3D%3D Vanderweele TJ, Knol MJ. Remarks on antagonism. Am J Epidemiol. (accepted for publication). GreenlandSLashTLRothmanKJConcepts of interaction. Modern epidemiology2008PhiladelphiaLippincott Williams & Wilkins7183 HallqvistJAhlbomADiderichsenFReuterwallCHow to evaluate interaction between causes: a review of practices in cardiovascular epidemiologyJ Intern Med1996239377382864222910.1046/j.1365-2796.1996.431782000.x1:STN:280:DyaK283it1Clug%3D%3D Knol MJ, Tweel IV, Grobbee DE, Numans ME, Geerlings MI. Estimating interaction on an additive scale between continuous determinants in a logistic regression model. Int J Epidemiol. 2007. BozkurtOVerschurenWMvan Wieren-de WijerBMGenetic variation in the renin-angiotensin system modifies the beneficial effects of ACE inhibitors on the risk of diabetes mellitus among hypertensivesJ Hum Hypertens2008227747801856317110.1038/jhh.2008.621:CAS:528:DC%2BD1cXht1Cms7vL HallanSde MutsertRCarlsenSDekkerFWAasarodKHolmenJObesity, smoking, and physical inactivity as risk factors for CKD: are men more vulnerable?Am J Kidney Dis2006473964051649061710.1053/j.ajkd.2005.11.027 de MutsertRJagerKJZoccaliCDekkerFWThe effect of joint exposures: examining the presence of interactionKidney Int2009756776811919067410.1038/ki.2008.645 Rothman KJ. Interactions between causes. Mod Epidemiol. 1986;311–326. AnderssonTAlfredssonLKallbergHZdravkovicSAhlbomACalculating measures of biological interactionEur J Epidemiol2005205755791611942910.1007/s10654-005-7835-x YangXSoWYMaRCLow LDL cholesterol, albuminuria, and statins for the risk of cancer in type 2 diabetes: the Hong Kong diabetes registryDiabetes Care200932182618321959262910.2337/dc09-07251:CAS:528:DC%2BD1MXhtlehs7jE J Hallqvist (9554_CR4) 1996; 239 9554_CR19 DB Richardson (9554_CR10) 2009; 169 S Hallan (9554_CR12) 2006; 47 KJ Rothman (9554_CR5) 1976; 103 9554_CR7 X Yang (9554_CR15) 2010; 87 X Yang (9554_CR14) 2009; 32 SF Assmann (9554_CR8) 1996; 7 9554_CR11 R Mutsert de (9554_CR16) 2009; 75 GY Zou (9554_CR18) 2008; 168 TJ Vanderweele (9554_CR6) 2007; 18 DW Hosmer (9554_CR9) 1992; 3 S Greenland (9554_CR3) 1998 S Greenland (9554_CR20) 2008 KJ Rothman (9554_CR1) 2002 T Andersson (9554_CR2) 2005; 20 O Bozkurt (9554_CR17) 2008; 22 I Marenholz (9554_CR13) 2009; 123 |
| References_xml | – reference: VanderweeleTJRobinsJMThe identification of synergism in the sufficient-component-cause frameworkEpidemiology2007183293391743544110.1097/01.ede.0000260218.66432.88 – reference: RothmanKJThe estimation of synergy or antagonismAm J Epidemiol197610350651112749521:STN:280:DyaE283gtlahtA%3D%3D – reference: AnderssonTAlfredssonLKallbergHZdravkovicSAhlbomACalculating measures of biological interactionEur J Epidemiol2005205755791611942910.1007/s10654-005-7835-x – reference: HallanSde MutsertRCarlsenSDekkerFWAasarodKHolmenJObesity, smoking, and physical inactivity as risk factors for CKD: are men more vulnerable?Am J Kidney Dis2006473964051649061710.1053/j.ajkd.2005.11.027 – reference: YangXMaRCSoWYWhite blood cell count and renin-angiotensin system inhibitors for the risk of cancer in type 2 diabetesDiabetes Res Clin Pract2010871171251993251910.1016/j.diabres.2009.10.0121:CAS:528:DC%2BC3cXntVaqsw%3D%3D – reference: RichardsonDBKaufmanJSEstimation of the relative excess risk due to interaction and associated confidence boundsAm J Epidemiol20091697567601921162010.1093/aje/kwn411 – reference: Knol MJ, Tweel IV, Grobbee DE, Numans ME, Geerlings MI. Estimating interaction on an additive scale between continuous determinants in a logistic regression model. Int J Epidemiol. 2007. – reference: MarenholzIKerscherTBauerfeindAAn interaction between filaggrin mutations and early food sensitization improves the prediction of childhood asthmaJ Allergy Clin Immunol20091239119161934892610.1016/j.jaci.2009.01.0511:CAS:528:DC%2BD1MXktV2itrw%3D – reference: ZouGYOn the estimation of additive interaction by use of the four-by-two table and beyondAm J Epidemiol20081682122241851142810.1093/aje/kwn104 – reference: Rothman KJ. Interactions between causes. Mod Epidemiol. 1986;311–326. – reference: HallqvistJAhlbomADiderichsenFReuterwallCHow to evaluate interaction between causes: a review of practices in cardiovascular epidemiologyJ Intern Med1996239377382864222910.1046/j.1365-2796.1996.431782000.x1:STN:280:DyaK283it1Clug%3D%3D – reference: BozkurtOVerschurenWMvan Wieren-de WijerBMGenetic variation in the renin-angiotensin system modifies the beneficial effects of ACE inhibitors on the risk of diabetes mellitus among hypertensivesJ Hum Hypertens2008227747801856317110.1038/jhh.2008.621:CAS:528:DC%2BD1cXht1Cms7vL – reference: de MutsertRJagerKJZoccaliCDekkerFWThe effect of joint exposures: examining the presence of interactionKidney Int2009756776811919067410.1038/ki.2008.645 – reference: Vanderweele TJ, Knol MJ. Remarks on antagonism. Am J Epidemiol. (accepted for publication). – reference: RothmanKJMeasuring interactions. Epidemiology: an introduction2002OxfordUniversity Press168180 – reference: AssmannSFHosmerDWLemeshowSMundtKAConfidence intervals for measures of interactionEpidemiology19967286290872844310.1097/00001648-199605000-000121:STN:280:DyaK283pt1Onsw%3D%3D – reference: YangXSoWYMaRCLow LDL cholesterol, albuminuria, and statins for the risk of cancer in type 2 diabetes: the Hong Kong diabetes registryDiabetes Care200932182618321959262910.2337/dc09-07251:CAS:528:DC%2BD1MXhtlehs7jE – reference: HosmerDWLemeshowSConfidence interval estimation of interactionEpidemiology19923452456139113910.1097/00001648-199209000-000121:STN:280:DyaK3s%2FgtlKhug%3D%3D – reference: GreenlandSRothmanKJConcepts of interaction. Modern epidemiology1998PhiladelphiaLippincott-Raven Publishers329342 – reference: GreenlandSLashTLRothmanKJConcepts of interaction. Modern epidemiology2008PhiladelphiaLippincott Williams & Wilkins7183 – start-page: 168 volume-title: Measuring interactions. Epidemiology: an introduction year: 2002 ident: 9554_CR1 – volume: 22 start-page: 774 year: 2008 ident: 9554_CR17 publication-title: J Hum Hypertens doi: 10.1038/jhh.2008.62 – volume: 169 start-page: 756 year: 2009 ident: 9554_CR10 publication-title: Am J Epidemiol doi: 10.1093/aje/kwn411 – ident: 9554_CR19 – volume: 168 start-page: 212 year: 2008 ident: 9554_CR18 publication-title: Am J Epidemiol doi: 10.1093/aje/kwn104 – volume: 123 start-page: 911 year: 2009 ident: 9554_CR13 publication-title: J Allergy Clin Immunol doi: 10.1016/j.jaci.2009.01.051 – ident: 9554_CR11 doi: 10.1093/ije/dym157 – volume: 32 start-page: 1826 year: 2009 ident: 9554_CR14 publication-title: Diabetes Care doi: 10.2337/dc09-0725 – volume: 239 start-page: 377 year: 1996 ident: 9554_CR4 publication-title: J Intern Med doi: 10.1046/j.1365-2796.1996.431782000.x – volume: 75 start-page: 677 year: 2009 ident: 9554_CR16 publication-title: Kidney Int doi: 10.1038/ki.2008.645 – volume: 7 start-page: 286 year: 1996 ident: 9554_CR8 publication-title: Epidemiology doi: 10.1097/00001648-199605000-00012 – volume: 87 start-page: 117 year: 2010 ident: 9554_CR15 publication-title: Diabetes Res Clin Pract doi: 10.1016/j.diabres.2009.10.012 – volume: 18 start-page: 329 year: 2007 ident: 9554_CR6 publication-title: Epidemiology doi: 10.1097/01.ede.0000260218.66432.88 – volume: 47 start-page: 396 year: 2006 ident: 9554_CR12 publication-title: Am J Kidney Dis doi: 10.1053/j.ajkd.2005.11.027 – start-page: 329 volume-title: Concepts of interaction. Modern epidemiology year: 1998 ident: 9554_CR3 – start-page: 71 volume-title: Concepts of interaction. Modern epidemiology year: 2008 ident: 9554_CR20 – volume: 20 start-page: 575 year: 2005 ident: 9554_CR2 publication-title: Eur J Epidemiol doi: 10.1007/s10654-005-7835-x – volume: 103 start-page: 506 year: 1976 ident: 9554_CR5 publication-title: Am J Epidemiol doi: 10.1093/oxfordjournals.aje.a112252 – ident: 9554_CR7 – volume: 3 start-page: 452 year: 1992 ident: 9554_CR9 publication-title: Epidemiology doi: 10.1097/00001648-199209000-00012 |
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| SubjectTerms | Angiotensin converting enzyme inhibitors Biological and medical sciences Cardiology Case-Control Studies Causality Confidence interval Data Interpretation, Statistical Diabetes Drug interactions Epidemiologic Research Design Epidemiology Estimating techniques General aspects Genotypes Humans Infectious Diseases Infinity Inhibitors Life Sciences Medical sciences Medicine Medicine & Public Health METHODS Miscellaneous Oncology Predisposing factors Prevention and actions Primary Prevention Public Health Public health. Hygiene Public health. Hygiene-occupational medicine Ratios Risk Assessment - methods Risk Assessment - statistics & numerical data Risk Factors Santé publique et épidémiologie Type 2 diabetes mellitus |
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