8/12/2019 1976 - Against Popperized Epidemiology. - Jacobsen

1/3

International Journal of Epidemiology Oxford University Press 1976 Vol. 5 No . 1Printed in Great Britain

Against Popperized EpidemiologyM JACOBSEN1Jacobsen, M. (Inst itute of Occupational Medicine, Roxburgh Place, Edinburgh, EH8 9SU, Scotland). AgainstPopperized Epidemiology. International Jou rnal of Epidemiology 1976 , 5 : 9 -11 .The recomm endation that Popper s philosophy of science should be adopted by ep idemiologists is disputed.Reference is made to other authors who have shown that the most constructive elements in Popper s ideas havebeen advocated by earlier philosophers and have been used in epidemiology without abandoning inductivereasoning. It is argued that Popper s denigrat ion of inductive m ethods is p art icularly harmful to e pidemiology .Inductive reasoning and statistical inference play a key role in the science; it is suggested that unfamiliarity withthese ideas contributes to widespread misunderstanding of the func tion o f epidemiology. Atten tion is drawn toa com mon fallacy invo lving correlations between three random variables. The prevalence of the fallacy may berelated to confusion between deductive and inductive logic.

In a recent paper (1) Dr. Carol Buck protests thatepidemiologists should not be regarded, by them-selves or by others, as data-gathering adjuncts tothe medical-scientific community. She argues thatcreative epidemiology requires the formulation ofrefutable hypotheses and that epidemiologistsshould involve themselves more in the "excitingprocess of scientific deduction". Her view is thatthe achievement of these laudable objectives wouldbe promoted if epidemiologists were to adopt theideas of Karl Popper. Yet Dr. Buck herself andDavies (2) refer to some of Popper's predecessorswho understood and emphasized the importanceof attempting to refute scientific hypotheses. Dr.Davies asks 'why pick on Popper?'

A major distinguishing feature in Popper'sphilosophy is his insistence that his ideas are'directly opposed to all attempts to operate withthe ideas of inductive logic' (3). Dr. Buck acceptsthis view enthusiastically. I agree with Dr. Davies.This is not a helpful way to free epidemiologistsfrom the misunderstanding that their task is merelyto 'gather data from which other scientists willbuild hypotheses' (1). On the contrary, I haveargued elsewhere (4) that because good medicalpractice relies heavily on the deductive art ofdiagnosis, many physicians appear to have greatdifficulty in appreciating the rules and discipline ofepidemiological research, since here the inductivemethod of reasoning is paramount. Unfamiliaritywith inductive reasoning is one of the barriers ob-structing an understanding of the role of statistical

1 Institute of OccupationalEdinburgh EH8 9SU, Scotland. Medicine, Roxburgh Place,

inference in epidemiology; and statistical inferencelies at the heart of epidemiology. Why? Becauseepidemiology is concerned largely with the study ofobservations which are subject to random fluctua-tions.Dr. Buck warns against 'an excessive recourse tostatistical devices for tidying up epidemiologicaldat a'. It is useful to draw the attention of epidemio-logists to Cox's objection to equating statisticalinference with data summarization (5). Cox remindsus that in statistical inference 'an essential elementis the uncertainty involved in passing from theobservations to the underlying population'. InCox's formulation it is the element of uncertaintywhich makes the inference statistical; the directionof the argument, from the observations to th e(statistical) population sampled, is characteristicof the inductive method. Note that if this inductivestep is taken with a proper regard to the logicunderlying (for instance) a test of statistical signifi-cance (the logic includes its restricted relevance tothe population sampled), then the induction leadsnaturally to what Dr. Buck calls the 'deductivelymore powerful' reason for replication in epidemio-logy: to identify circumstances under which apreviously observed association does not exist.

In common with some other epidemiologists Dr.Buck appears to regard applied statistics essentiallyas a set of numerical 'devices'. How ever, in epidem i-ology as elsewhere, the application of probabilitytheory to real data embraces more than signifi-cance testing, standardization and data summariza-tion. For instance, an increasingly important partof modern epidemiology is directed to determiningthe relationship between the dose of a suspected or

y

,

pj

j

g

http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/http://ije.oxfordjournals.org/

8/12/2019 1976 - Against Popperized Epidemiology. - Jacobsen

2/3

10 I N T E R N A T I O N A L J O U R N A L O F E P I D E M I O L O G Yknown environmental pollutant and the degree ofresponse in human populations. The solution tothis quantitative problem requires, among otherthings, statistical point estimation (of the regressioncoefficient for instance) and interval estimation.The latter may involve controversial inferencesabout the distribution of the unknown regressioncoefficient being estimated or the interval con-taining the estimate itself at a fixed probabilitylevel. The fact that significance testing may berelatively unim port ant in such situations, and tha t itmay be difficult to force the pertinent researchquestions into a formal hypothesis implying arefutable prediction, does not convince me that thisactivity therefore lies outside the boundary ofscience. Of course, once the dose-response rela-tionship has been estimated then this provides thebasis for subsequent predictions which are refutablein principle. Indeed I have suggested two wayswhereby such epidemiologically derived predictionscan be tested in practice (6). The important pointto note however is that the initial estimation of thedose-response relationship requires inductive argu-ments of statistical inference, from the sample tothe population parameters of interest. Deductionsand hypotheses follow.

The discussion in the two preceding paragraphsillustrates Fisher 's point that 'deductive argumentsare, in fact, often only stages in an inductiveprocess' (7). Popper (3) would be unmoved by theseconsiderations. He distinguishes between inductiveprocesses, which he relegates to the realm 'psy-chology of knowledge', and the deductive method,which he considers as part of the 'logic of know-ledge'. He then asserts that he is concerned onlywith the latter. ( 'There is no need even to mentioninduction.') Even if this view is acceptable to onewho is dedicated specifically to the search for awholly abstract theory of knowledge, it is difficultto see why Dr. Buck commends it to epidemiolo-gists who are involved directly in the practice of anempirical science which leans heavily on inductivemethods .For her 'exercise in Popperian epidemiology'Dr. Buck (1) reformulates the 'infectious hypothe-sis' concerning cancer of the uterine cervix. Herhypothesis asserts, inter alia, a negative correlationbetween the risk of cervical cancer and progesteronelevels. Dr. Buck then proceeds to discuss testablepredictions from the hypothesis. One of them states'A history of repeated spontaneous abortionshould be associated with cervical cancer,because susceptibility to abortion is to someextent related to low levels of progesterone.'

This prediction does not follow from the hypothesis(quite apart from the careful caveats made by Dr.Buck herself). L et X, Fa nd Z represent the randomvariables of cervical cancer incidence, progesteronelevel and spontaneous abortion incidence respec-tively; let rx and rzz be the correlationcoefficients between XY, ZY and XZ. Dr. Buck'sprediction can be written as

'ifan d r0. Now it is easyto write down linear regression models [5] em-bodying the left-hand sides of the inequalities [1]and [2], and they look deceptively similar toequations [3] and [4].

E(XE(ZE(X [5]If ax and Sj are know n to be negative it does notfollow that yl is positive.Why is the deductive algebraic syllogism of [3]and [4] not applicable to the models [5]? Becausethe models refer to random systems; they are notsimple linear equations. The coefficient y1 is anunknown population parameter. I t may be esti-mated from appropriate data using inductive,statistical arguments. The degree of uncertainty inthe estimate would be related to the variance of therandom variable X. (In reality, we cannot 'know'tha t

8/12/2019 1976 - Against Popperized Epidemiology. - Jacobsen

3/3

A G A IN S T P O P P E R IZ E D E P ID E M IO L O G Y 11be shown that there are conditions constrainingrxz to be positive when rXT and rZT have the samealgebraic sign. The inequalities

[6][7]are sufficient conditions to satisfy [2]. The necessarycondition for rxz