Selection biases may have led to beneficial effects of moderate drinking being over estimated; however, they are unlikely to entirely explain the J-shaped curve. Even if all beneficial effects were eliminated, our ranking of alcohol as a public health burden would not change, nor our efforts to limit its harm.European Research Council (Grant ID: ERC-StG-2012-309337_AlcoholLifecourse) and UK Medical Research Council/Alcohol Research UK (Grant ID: MR/M006638/1)This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1111/add.1358

Bell, Steven

Britton, Annie

Britton, A

Bell, S

UCL Discovery

MICHAEL ROERECKE1,2,3Institute for Mental Health Policy Research, Centre for Addiction andMental Health (CAMH), Toronto, Canada,1 Dalla Lana School ofPublic Health (DLSPH), University of Toronto, Toronto, Canada2 andPAHO/WHO Collaborating Centre for Addiction and Mental Health,Toronto, Canada3E-mail: m.roerecke@web.deReferences1. Naimi T. S., Stockwell T., Zhao J., Xuan Z., Dangardt F., Saitz R.et al. Selection biases in observational studies affect associationsbetween ‘moderate’ alcohol consumption and mortality.Addiction 2017; 112: 207–14.2. Brookhart M. A. Counterpoint: the treatment decision design.Am J Epidemiol 2015; 182: 840–5.3. Vandenbroucke J., Pearce N. Point: incident exposures, preva-lent exposures, and causal inference: does limiting studies topersons who are followed from ﬁrst exposure onward damageepidemiology? Am J Epidemiol 2015; 182: 826–33.4. Fillmore K. M., Kerr W. C., Stockwell T., Chikritzhs T., BostromA.Moderate alcohol use and reduced mortality risk: systematicerror in prospective studies. Addict Res Theory 2006; 14:101–32.5. Stockwell T., Zhao J., Panwar S., Roemer A., Naimi T.,Chikritzhs T. Do ‘moderate’ drinkers have reduced mortalityrisk? a systematic review and meta-analysis of alcoholconsumption and all-cause mortality. J Stud Alcohol Drugs2016; 77: 185–98.6. Rehm J., Roerecke M., Room R. All-cause mortality risks for‘moderate drinkers’: what are the implications for burden-of-disease studies and low risk-drinking guidelines? J Stud AlcoholDrugs 2016; 77: 203–4.7. Royston P., Sauerbrei W. Multivariable Model-Building: A Prag-matic Approach to Regression Analysis Based on FractionalPolynomials for Modelling Continuous Variables. Chichester, UK:Jon Wiley & Sons; 2008.8. Roerecke M., Rehm J. Alcohol consumption, drinkingpatterns, and ischemic heart disease: a narrative review ofmeta-analyses and a systematic review and meta-analysis ofthe impact of heavy drinking occasions on risk for moderatedrinkers. BMC Med 2014; 12: 182.THE PROTECTIVE EFFECTS OF MODERATEDRINKING: LIES, DAMNED LIES, AND…SELECTION BIASES?Selection biases may have led to beneﬁcial effects of moderatedrinking being over estimated; however, they are unlikely toentirely explain the J-shaped curve. Even if all beneﬁcialeffects were eliminated, our ranking of alcohol as a publichealth burden would not change, nor our efforts to limit itsharm.More than a decade ago it was almost taken for grantedthat moderate alcohol consumption conferred protectivehealth effects, with those expressing scepticism groupedalongside ‘doubters of manned lunar missions andmembers of the Flat Earth Society’ [1]. Since then therehas been a steady stream of studies that have set out tointerrogate this association from multiple angles to deter-mine the robustness of this claim [2–4]. Naimi et al. [5]add to this body of work by succinctly mapping outpotential methodological issues that fall under theumbrella of selection biases.In the interest of furthering this debate, we will attemptto play devil’s advocate in this commentary.To begin, we need to consider the bigger picture. Doesthis potential underestimation of harm from alcoholmatterto our rating of alcohol as a public health burden, or are wesimply tinkering around the edges? If the protective effectswere convincingly dismissed, would people’s drinkinghabits change?It is estimated that world-wide alcohol use accountedfor 2.8 million deaths in 2013 [6] and it was ranked 6thout of 25 leading risk factors in terms of disability adjustedlife years (DALYS). Included in these estimates is aprotective effect for diabetes, ischaemic heart disease andischaemic stroke (amounting to an estimated 2.3 millionfewer DALYS [7]). However, this represents a tinyproportion of all DALYS (1.5 billion). Even if we were toassume that all beneﬁcial effects are wiped out due toselection in observational studies, these rankings wouldnot change substantially.It is also claimed widely that a randomized controlledtrial (RCT) is the panacea for all concerns about testingthe protective effects of alcohol consumption. However,even assuming that ethical hurdles can be surmounted,this view is too simplistic. Experimental studies (random-ized or not) are still prone to selection biases, for exam-ple in terms of who participates, adherence to exposure,loss to follow-up and so on [8,9]. Dismissing evidencefrom observational studies as second-class is wrong,Naimi et al. even note that observational data analysedcorrectly can come to the same conclusion as RCTs[10,11] .This requires researchers to move beyond simplyrepeating standard analyses of alcohol consumption mea-sured at one point in time and health outcomes at alater date [12]. The selection biases outlined by Naimiand colleagues are not unique to alcohol epidemiology;they affect countless other risk factors [13–15] and wecan learn a great deal from these ﬁelds. For example,we agree wholeheartedly with the need to examineassociations between alcohol and health outcomes usinga life-course perspective [16]. With longitudinal data wecan attempt to measure the magnitude of selection biasusing, for example, inverse probability weightingmethods [17]. The probability of exiting a study (dueto causes that are often related to ill health) is used toweight observed data to ‘compensate’ for selection.© 2016 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction. Addiction, 112, 215–221This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,provided the original work is properly cited.218 CommentariesThis method has been demonstrated neatly for estimatesof cognitive decline in smokers compared to never-smokers, which were 56–86% larger after accountingfor attrition [13].We do not doubt that selection effects are a realphenomenon that may have led to protective effects ofmoderate drinking being over-estimated; however, theyare unlikely to explain the ubiquitous J-shaped curveentirely [18]. If this were the case one would anticipatethat protective effects would also be observed foralcohol-related cancers; however, this is not what isfound [19]. Naimi et al. also frame their arguments ina hypothetical world whereby seemingly none of theissues of selection due to health status or early death ap-ply to non-drinkers. The major challenges lying aheadare to understand when to anticipate selection bias, todetermine the severity of the threat and to utilizeavailable analytical methods to diminish its effect. Inorder for this ﬁeld to progress we need to move beyondtraditional analyses and account more effectively for thecomplex, dynamic relationship between alcoholconsumption and health over the life-course—then, andonly then, may we get closer to the truth.Declaration of interestsNone.AcknowledgmentsThe alcohol life-course project (http://www.ucl.ac.uk/alcohol-lifecourse) is funded by grants from theEuropean Research Council (ERC-StG-2012-309337_AlcoholLifecourse) and UK Medical ResearchCouncil/Alcohol Research UK (MR/M006638/1).Keywords Alcohol, causal inference, life-course,longitudinal, selection bias, study design.ANNIE BRITTON1 & STEVEN BELL2Department of Epidemiology and Public Health, University CollegeLondon, London, UK1 and Department of Public Health andPrimary Care, University of Cambridge, Cambridge, UK2E-mail: a.britton@ucl.ac.ukReferences1. Stockwell T. R. Alcohol and cardiovascular disease: still aresearch priority? Med J Aust 2000; 173: 116–7.2. Chikirzhs T., Fillmore K., Stockwell T. A healthy dose ofscepticism: four good reasons to think again about protectiveeffects of alcohol on coronary heart disease. Drug AlcoholRev 2009; 28: 441–4.3. Chikritzhs T., Stockwell T., Naimi T., Andreasson S., DangardtF., LiangW. Has the leaning tower of presumed health beneﬁtsfrom ‘moderate’ alcohol use ﬁnally collapsed? Addiction 2015;110: 726–7.4. Fekjær H. O. Alcohol—a universal preventive agent? A criticalanalysis. Addiction 2013; 108: 2051–7.5. Naimi T. S., Stockwell T., Zhao J., Xuan Z., Dangardt F., Saitz R.et al. Selection biases in observational studies affectassociations between ‘moderate’ alcohol consumption andmortality.Addiction2017;112: 207–14.6. Forouzanfar M. H., Alexander L., Anderson H. R., BachmanV.F., Biryukov S., Brauer M. et al. Global, regional, and nationalcomparative risk assessment of 79 behavioural, environmen-tal and occupational, andmetabolic risks or clusters of risks in188 countries, 1990–2013: a systematic analysis for theGlobal Burden of Disease Study 2013. Lancet 2015; 386:2287–323.7. Rehm J. The risks associated with alcohol use and alcoholism.Alcohol Res Health 2011; 34: 135–43.8. Bell S., Britton A. A second-class science? A defence ofobservational epidemiology to make causal inferences.Addiction 2014; 109: 163–4.9. Hébert J. R., Frongillo E. A., Adams S. A., Turner-McGrievy G.M., Hurley T. G., Miller D. R. et al. Perspective: randomizedcontrolled trials are not a panacea for diet-related research.Adv Nutr Int Rev J 2016; 7: 423–32.10. Hernán M. A., Alonso A., Logan R., Grodstein F., Michels K.B., Willett W. C. et al. Observational studies analyzed likerandomized experiments: an application to postmenopausalhormone therapy and coronary heart disease. Epidemiology2008; 19: 766–79.11. Delaney J. A. C., Daskalopoulou M. D. P., Stella S., Suissa S.Traditional versus marginal structural models to estimatethe effectiveness of β-blocker use on mortality aftermyocardial infarction. Pharmacoepidemiol Drug Saf 2009;18: 1–6.12. Roerecke M., Rehm J. Alcohol and ischaemic heart diseaserisk—ﬁnally moving beyond interpretation of observationalepidemiology. Addiction 2015; 110: 723–5.13. Weuve J., Tchetgen E. J. T., Glymour M. M., Beck T. L.,Aggarwal N. T., Wilson R. S. et al. Accounting for bias dueto selective attrition: the example of smoking and cognitivedecline. Epidemiology 2012; 23: 119–28.14. Banack H. R., Kaufman J. S. The ‘Obesity Paradox’ explained.Epidemiology 2013; 24: 461–2.15. ChristensenA. I., EkholmO.,GrayL.,GlümerC., JuelK.What iswrong with non-respondents? Alcohol-, drug- and smoking-related mortality and morbidity in a 12-year follow-up studyof respondents and non-respondents in the Danish HealthandMorbidity Survey.Addiction 2015; 110: 1505–12.16. Britton A., Ben-Shlomo Y., Benzeval M., Kuh D., Bell S. Lifecourse trajectories of alcohol consumption in the UnitedKingdom using longitudinal data from nine cohort studies.BMC Med 2015; 13: 47.17. Hernán M. A., Hernández-Díaz S., Robins J. M. Astructural approach to selection bias. Epidemiology 2004;15: 615–25.18. Emberson J. R., Bennett D. A. Effect of alcohol on risk ofcoronary heart disease and stroke: causality, bias, or a bit ofboth? Vasc Health Risk Manag 2006; 2: 239.19. Bagnardi V., RotaM., Botteri E., Tramacere I., Islami F., FedirkoV. et al. Alcohol consumption and site-speciﬁc cancer risk: acomprehensive dose–response meta-analysis. Br J Cancer2015; 112: 580–93.© 2016 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction. Addiction, 112, 215–221Commentaries 219

The protective effects of moderate drinking: lies, damned lies, and… selection biases?

Annie Britton

Steven Bell

Crossref

Addiction

Commentary to: Selection biases in observational studies affect associations between ‘moderate’ alcohol consumption and mortalityEuropean Research Council (Grant ID: ERC-StG-2012-309337_AlcoholLifecourse) and UK Medical Research Council/Alcohol Research UK (Grant ID: MR/M006638/1)This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1111/add.1358

Apollo (Cambridge)

The protective effects of moderate drinking: lies, damnedlies, and… selection biases?Selection biases may have led to beneﬁcial effects of moderatedrinking being over estimated; however, they are unlikely toentirely explain the J-shaped curve. Even if all beneﬁcialeffects were eliminated, our ranking of alcohol as a publichealth burden would not change, nor our efforts to limit itsharm.More than a decade ago it was almost taken for grantedthat moderate alcohol consumption conferred protectivehealth effects, with those expressing scepticism groupedalongside ‘doubters of manned lunar missions andmembers of the Flat Earth Society’ [1]. Since then therehas been a steady stream of studies that have set out tointerrogate this association from multiple angles to deter-mine the robustness of this claim [2–4]. Naimi et al. [5]add to this body of work by succinctly mapping outpotential methodological issues that fall under theumbrella of selection biases.In the interest of furthering this debate, we will attemptto play devil’s advocate in this commentary.To begin, we need to consider the bigger picture. Doesthis potential underestimation of harm from alcoholmatterto our ratingof alcohol as a public health burden, or are wesimply tinkering around the edges? If the protective effectswere convincingly dismissed, would people’s drinkinghabits change?It is estimated that world-wide alcohol use accountedfor 2.8 million deaths in 2013 [6] and it was ranked 6thout of 25 leading risk factors in terms of disability adjustedlife years (DALYS). Included in these estimates is aprotective effect for diabetes, ischaemic heart disease andischaemic stroke (amounting to an estimated 2.3 millionfewer DALYS [7]). However, this represents a tinyproportion of all DALYS (1.5 billion). Even if we were toassume that all beneﬁcial effects are wiped out due toselection in observational studies, these rankings wouldnot change substantially.It is also claimed widely that a randomized controlledtrial (RCT) is the panacea for all concerns about testingthe protective effects of alcohol consumption. However,even assuming that ethical hurdles can be surmounted,this view is too simplistic. Experimental studies (random-ized or not) are still prone to selection biases, for examplein terms of who participates, adherence to exposure, lossto follow-up and so on [8,9]. Dismissing evidence fromobservational studies as second-class is wrong, Naimiet al. even note that observational data analysed correctlycan come to the same conclusion as RCTs [10,11] .This requires researchers to move beyond simplyrepeating standard analyses of alcohol consumptionmeasured at one point in time and health outcomes ata later date [12]. The selection biases outlined by Naimiand colleagues are not unique to alcohol epidemiology;they affect countless other risk factors [13–15] and wecan learn a great deal from these ﬁelds. For example,we agree wholeheartedly with the need to examineassociations between alcohol and health outcomes usinga life-course perspective [16]. With longitudinal data wecan attempt to measure the magnitude of selection biasusing, for example, inverse probability weightingmethods [17]. The probability of exiting a study (dueto causes that are often related to ill health) is used toweight observed data to ‘compensate’ for selection.This method has been demonstrated neatly for estimatesof cognitive decline in smokers compared to never-smokers, which were 56–86% larger after accountingfor attrition [13].We do not doubt that selection effects are a realphenomenon that may have led to protective effects ofmoderate drinking being over-estimated; however, theyare unlikely to explain the ubiquitous J-shaped curveentirely [18]. If this were the case one would anticipatethat protective effects would also be observed foralcohol-related cancers; however, this is not what isfound [19]. Naimi et al. also frame their arguments ina hypothetical world whereby seemingly none of theissues of selection due to health status or early death ap-ply to non-drinkers. The major challenges lying aheadare to understand when to anticipate selection bias, todetermine the severity of the threat and to utilizeavailable analytical methods to diminish its effect. Inorder for this ﬁeld to progress we need to move beyondtraditional analyses and account more effectively for thecomplex, dynamic relationship between alcoholconsumption and health over the life-course—then, andonly then, may we get closer to the truth.Declaration of interestsNone.AcknowledgmentsThe alcohol life-course project (http://www.ucl.ac.uk/alcohol-lifecourse) is funded by grants from theEuropean Research Council (ERC-StG-2012-309337_AlcoholLifecourse) and UK Medical ResearchCouncil/Alcohol Research UK (MR/M006638/1).Keywords Alcohol, causal inference, life-course,longitudinal, selection bias, study design.© 2016 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction. AddictionCOMMENTARYThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in anymedium,provided the original work is properly cited.ANNIE BRITTON1 & STEVEN BELL2Department of Epidemiology and Public Health, University CollegeLondon, London, UK1 and Department of Public Health andPrimary Care, University of Cambridge, Cambridge, UK2E-mail: a.britton@ucl.ac.ukReferences1. Stockwell T. R. Alcohol and cardiovascular disease: still aresearch priority? Med J Aust 2000; 173: 116–7.2. Chikirzhs T., Fillmore K., Stockwell T. A healthy dose of scepti-cism: four good reasons to think again about protective effectsof alcohol on coronary heart disease. Drug Alcohol Rev 2009;28: 441–4.3. Chikritzhs T., Stockwell T., Naimi T., Andreasson S., DangardtF., LiangW. Has the leaning tower of presumed health beneﬁtsfrom ‘moderate’ alcohol use ﬁnally collapsed?Addiction 2015;110: 726–7.4. Fekjær H. O. Alcohol—a universal preventive agent? A criticalanalysis. Addiction 2013; 108: 2051–7.5. Naimi T. S., Stockwell T., Zhao J., Xuan Z., Dangardt F., Saitz R.et al. Selection biases in observational studies affectassociations between ‘moderate’ alcohol consumption andmortality. Addiction 2016; DOI: 10.1111/add.13451.6. ForouzanfarM. H., Alexander L., Anderson H. R., Bachman V.F., Biryukov S., Brauer M. et al. Global, regional, and nationalcomparative risk assessment of 79 behavioural, environmen-tal and occupational, andmetabolic risks or clusters of risks in188 countries, 1990–2013: a systematic analysis for theGlobal Burden of Disease Study 2013. Lancet 2015; 386:2287–323.7. Rehm J. The risks associated with alcohol use and alcoholism.Alcohol Res Health 2011; 34: 135–43.8. Bell S., Britton A. A second-class science? A defence ofobservational epidemiology to make causal inferences.Addiction 2014; 109: 163–4.9. Hébert J. R., Frongillo E. A., Adams S. A., Turner-McGrievy G.M., Hurley T. G., Miller D. R. et al. Perspective: randomizedcontrolled trials are not a panacea for diet-related research.Adv Nutr Int Rev J 2016; 7: 423–32.10. Hernán M. A., Alonso A., Logan R., Grodstein F., Michels K.B., Willett W. C. et al. Observational studies analyzed likerandomized experiments: an application to postmenopausalhormone therapy and coronary heart disease. Epidemiology2008; 19: 766–79.11. Delaney J. A. C., Daskalopoulou M. D. P., Stella S., Suissa S.Traditional versus marginal structural models to estimatethe effectiveness of β-blocker use on mortality aftermyocardial infarction. Pharmacoepidemiol Drug Saf 2009;18: 1–6.12. Roerecke M., Rehm J. Alcohol and ischaemic heart diseaserisk—ﬁnally moving beyond interpretation of observationalepidemiology. Addiction 2015; 110: 723–5.13. Weuve J., Tchetgen E. J. T., Glymour M. M., Beck T. L.,Aggarwal N. T., Wilson R. S. et al. Accounting for bias dueto selective attrition: the example of smoking and cognitivedecline. Epidemiology 2012; 23: 119–28.14. Banack H. R., Kaufman J. S. The ‘Obesity Paradox’ explained.Epidemiology 2013; 24: 461–2.15. Christensen A. I., EkholmO., Gray L., Glümer C., Juel K.Whatis wrong with non-respondents? Alcohol-, drug- andsmoking-related mortality and morbidity in a 12-yearfollow-up study of respondents and non-respondents in theDanish Health and Morbidity Survey. Addiction 2015; 110:1505–12.16. Britton A., Ben-Shlomo Y., Benzeval M., Kuh D., Bell S. Lifecourse trajectories of alcohol consumption in the UnitedKingdom using longitudinal data from nine cohort studies.BMC Med 2015; 13: 47.17. Hernán M. A., Hernández-Díaz S., Robins J. M. Astructural approach to selection bias. Epidemiology 2004;15: 615–25.18. Emberson J. R., Bennett D. A. Effect of alcohol on risk ofcoronary heart disease and stroke: causality, bias, or a bit ofboth? Vasc Health Risk Manag 2006; 2: 239.19. Bagnardi V., Rota M., Botteri E., Tramacere I., Islami F.,Fedirko V. et al. Alcohol consumption and site-speciﬁc cancerrisk: a comprehensive dose–response meta-analysis. Br JCancer 2015; 112: 580–93.2 Commentary© 2016 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction. Addiction

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The protective effects of moderate drinking: lies, damned lies, and… selection biases?

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