Zwahlen, Marcel

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Published by Oxford University Press on behalf of the International Epidemiological Association The Author 2009; all rights reserved. Advance Access publication 22 September 2009International Journal of Epidemiology 2009;38:1197–1198doi:10.1093/ije/dyp294Commentary: Cornfield on cigarette smokingand lung cancer and how to assess causalityMarcel ZwahlenAccepted 30 July 2009It has now been 50 years—10 years before the firstlanding on the moon—that Cornfield and colleagues1tried to put to rest the discussion and argumentsquestioning the role of cigarette smoking in theaetiology of lung cancer. In a 40-page article inthe Journal of the National Cancer Institute, several ofthe leading figures in the USA of the developingdiscipline called ‘chronic disease epidemiology’ sum-marized the current state of knowledge that impli-cated cigarette smoking as the leading factorcontributing to the development of lung cancer. Thisreview article did not adhere to current standardsfor a systematic review. It is nevertheless remarkablein many ways. It reviewed not only the epidemiologi-cal evidence ranging from descriptive to analyticalepidemiology, but also included studies of carcinogen-esis in laboratory animals. It examined, one by one,the arguments against the cigarette–lung cancerhypothesis.A group of epidemiology students in Bern recentlydiscussed this article as part of their training. Theywere surprised that half a century ago epidemiologistsclearly recognized the various possible biases inherentin descriptive and observational epidemiology and thedifficulties in interpreting such evidence. Thosearguing against the cigarette–lung cancer hypothesisused several lines of argument. Descriptive epidemiology—showing dramaticincreases in lung cancer incidence and mortalityin the first half of the 20th century—provides anincorrect picture due to time trends in diagnosticaccuracy, better recording and changes in the agestructure of the populations in the USA and Europe. Case–control studies are plagued by selection andinformation bias, resulting in risk associations thatmay be spurious. Prospective studies can be flawed in ways that alsoproduce spurious associations. The majority of smokers do not develop lungcancer, thus refuting a simplistic all-or-none mech-anism of disease development. Results from animal studies are inconsistent. There may be an unmeasured characteristic ‘X’ thatis the real cause of lung cancer but associated withbecoming a smoker, thus producing a spurioussmoking–lung cancer association. In the absence of evidence from controlled experi-ments in humans, confounding by such a factor ‘X’cannot be ruled out.In taking up these arguments, Cornfield and collea-gues implicitly worked through a ‘check list’ to judgecausality of risk factor–disease associations, which isreminiscent of the list put forward by Sir AustinBradford Hill2 in 1965, 6 years later. This seemsto confirm a rule that for anything named after aperson, there is someone else who had said or doneit before (fortunately, I cannot remember to whomthis rule should be accredited). Cornfield and collea-gues, predating Hill, stressed that it is important tosee a coherent pattern in population-based incidencerates, consistent findings across populations andstudy designs and a strong association on the relativerisk scale, with a robust dose–response pattern.Cornfield and colleagues could not truly refute allof the counter arguments. However, they organizedtheir arguments with something we nowadayswould recognize as the Bayes factor.3,4 For eachdimension of evidence, the likelihood ratio was infor-mally discussed in which the likelihood of the dataunder the cigarette–lung cancer hypothesis was com-pared with the likelihood of the data under the alter-native hypothesis. This approach was particularlyclear when Cornfield and colleagues dealt with thecriticism of Berkson, that the prospective studies onsmokers and non-smokers could have given biasedresults because people who did not smoke were ‘bio-logically self-protective’5: ‘in this event, during theearlier period after selection, the death rate ofthe smokers in the study would be higher than thedeath rate of the non-smokers in the study, even ifdeath rates were unrelated to smoking habits in thegeneral population. If smoking is unrelated to deathfrom lung cancer (or other causes), the death rate ofthe smokers would tend to equalize with that of thenon-smokers as the study progressed’.1 But this wasnot what was observed, rather ‘the observed associa-tion between cigarette smoking and lung cancer wasInstitute of Social and Preventive Medicine (ISPM), Universityof Bern, Bern, Switzerland. E-mail: zwahlen@ispm.unibe.chstronger in the latter part than in the early part of thestudy’.1For Cornfield and colleagues, the most difficult chal-lenge was the argument implicating a confoundingvariable ‘X’. Here, Cornfield and colleagues had tocombine arguments about the strength of the associ-ation (a relative risk of about nine when comparingsmokers with non-smokers and one of a factor 60when comparing heavy smokers with non-smokers),and how strongly associated this factor ‘X’ wouldneed to be with smoking to produce these associa-tions. As outlined in Appendix A of their article,boundaries can be derived on how prevalent thefactor ‘X’ would need to be in smokers comparedwith non-smokers. These boundaries were then usedto argue that no one could suggest a candidate forsuch a characteristic ‘X’ and the odds of finding onein the future seemed very small indeed. Here,Cornfield and colleagues clearly illustrate why epide-miologists examine relative risk measures when asses-sing whether an observed association is likely to becausal. Furthermore, we find here an early exampleof explicitly and quantitatively accounting for anunobserved additional variable. The area of explicitlymodelling bias mechanisms or the influence of unob-served (often called latent) variables has gainedimportance in epidemiology in recent years.6–8In a Bayesian inference framework of continuouslearning from the available evidence,9 one revises theprior odds of a hypothesis (compared with an alterna-tive hypothesis) by multiplying it with the likelihoodratio of the data under the two hypotheses under con-sideration. When several independent sources of evi-dence are available, one can multiply the respectivelikelihood ratios to arrive at a final assessment,taking into account the totality of the evidence. It ishardly surprising that Jerome Cornfield (1912–79),who has been described as one of the most influentialbiostatisticians in the USA (with little formal trainingand no degree in statistics),10 later contributed to thetheory of Bayesian inference.11–13No single study or source of evidence was able to putthe debate on the association between cigarette smok-ing and lung cancer at rest. But, as our PhD studentsquickly realized, this seminal paper integrated thetotality of evidence to demonstrate that the cigarette–lung cancer hypothesis was much more likely to be truethan any of the alternative hypotheses put forward bythe opponents. Of course, our students raised the ques-tion when the evidence becomes compelling enough tojustify public health action. They were shocked whenrealizing how slow society can be in taking action.Cornfield and colleagues were well aware of the diffi-culties of changing a widespread habit that was sup-ported by a powerful industry: ‘. . . if the findings hadbeen made on a new agent, . . . and on one which didnot support a large industry, skilled in the arts of masspersuasion, the evidence for the hazardous nature ofthe agent would be generally regarded as beyond dis-pute’.1 Switzerland is finally introducing smoking bansin public places that are more or less stringent, depend-ing on the Canton. In the Canton of Bern, the smokingban came into law on 1 July 2009, almost 50 years afterthe Cornfield paper. But people can still light up insome restaurants, which provide separate smokingareas.AcknowledgementsI thank the following PhD students and scientists:Martin Adam, Matthias Egger, Martin Feller, SereinaHerzog, Eveline Nu¨esch, Olivia Keiser, Radek Panczak,Corina Ru¨egg and Ben Spycher, at the Institute ofSocial and Preventive Medicine in Bern, Switzerland,for discussing this historical paper during a lunchseminar on 2 July 2009, and providing helpful com-ments on drafts of this commentary.Conflicts of interest: None declared.References1 Cornfield J, Haenszel W, Hammond CE et al. Smokingand lung cancer: recent evidence and a discussion ofsome questions. J Natl Cancer Inst 1959;22:173–203.Reprinted in Int J Epidemiol 2009;38:1175–91.2 Hill AB. The environment and disease: association orcausation? Proc R Soc Med 1965;58:295–300.3 Goodman SN. Toward evidence-based medical statistics.2: the Bayes factor. Ann Intern Med 1999;130:1005–13.4 Goodman SN. Introduction to Bayesian methods I: mea-suring the strength of evidence. Clin Trials 2005;2:282–90.5 Berkson J. Smoking and lung cancer: some observationson two recent reports. J Am Stat Assoc 1958;53:28–38.6 Lash TL, Fink AK. Semi-automated sensitivity analysisto assess systematic errors in observational data.Epidemiology 2003;14:451–8.7 Greenland S. Multiple-bias modelling for analysis ofobservational data. J R Stat Soc Ser A 2005;168:267–306.8 Turner RM, Spiegelhalter DJ, Smith GCS et al. Biasmodelling in evidence synthesis. J R Stat Soc Ser A 2009;172:21–47.9 Berry DA. Bayesian clinical trials. Nat Rev Drug Discov2006;5:27–36.10 Armitage P, Colton T. Encyclopedia of Biostatistics. WestSussex: John Wiley & Sons, 1998.11 Cornfield J. Sequential trials, sequential analysis and thelikelihood principle. Am Stat 1966;20:18–23.12 Cornfield J. The Bayesian outlook and its application.Biometrics 1969;25:617–57.13 Cornfield J. Recent methodological contributions to clin-ical trials. Am J Epidemiol 1976;104:408–21.1198 INTERNATIONAL JOURNAL OF EPIDEMIOLOGY

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