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Attribution of causation in epidemiology: Chain or mosaic?
0895s4356/96/$15.00 SSDI 0895s4356(95)00030-8
J Clin Epidemiol Vol. 49, No. 1, pp. 105-107, 1996 Copyright 0 1996 Elsevler Science Inc.
ELSEVIER
SECOND
Attribution
THOUGHTS
of Causation in Epidemiology: Chain or Mosaic? Bruce G. Chrlton DEPARTMENT
UNIVERSITY
OF EPIDEMIOLOGY
OF NEWCASTLE
KEY WORDS. Epidemiology,
causation,
PUBLIC
HEALTH,
NEWCASTLE
UPON
science, philosophy,
health
UPON
AND TYNE,
In the following essay I will argue that historical trends in epidemiological reasoning have meant that ascribing causal status to an independent variable has become easier, while refuting a causal hypothesis has become harder. The result has been the development of a broadly applicable, but dubiously valid, practice of multicausal risk factor epidemiology that is used to investigate etiology in a wide range of loosely defined health states [l-3]. Epidemiology has widened its scope, but at the cost of blunting its discriminative power. This process seems to have been driven by the demands of health policy decision making, and not by methodological advances in the criteria for valid inference. I will suggest that one consequence of the adoption of more inclusive methods is that contemporary epidemiological causal ascriptions do not have the generalizability or universality normally associated with scientific knowledge. Analytical epidemiology is concerned with studying associations between variables in a population, with the aim of determining causes of health-related states (usually disease). Causes may be described as necessary or sufficient [4,5]. Necessary causes are required in order that an effect may follow-exclude the necessary cause and you have prevented the effect. Sufficient causes are adequate by themselves to produce an effect-after a sufficient cause the effect will inevitably follow. The basic sciences are built on the concept of necessary cause, and sufficient causes are (virtually) never seen in biology [6]. The most famous criteria by which an agent is defined as the necessary cause of a disease are the Henle-Koch postulates. These criteria have been extended beyond their original concern with infectious pathogens to refer to any putative causal agent [7]. To satisfy the postulates an agent should be found in all cases of the disease (positive association), should be absent from all nondiseased individuals (negative association), and the disease should be reproducible in an animal model using a pure preparation of the agent. The principles underlying the Henle-Koch postulates are therefore twofold: to establish a specific arsociation between the putative cause and effect and then to devise a tightly controlled eqeriment in which exposure of the organism to the putative cause may be manipulated to determine whether it is a necessary cause. Experiment involves contriving a situation in which the putative cause is the only significant independent variable-all other known influencing variables being excluded or held constant. For ethical reasons the deliberate induction of disease is usually permissible only in animals. Application of the Henle-Koch postulates requires definition of a disease by a pathognomonic ksion, the presence of which is both necessary and sufficient for diagnosis. The purpose of animal models (or modelling disease in cell culture or computer simulation, etc.) is to reproduce the pathognomonic lesion. When a disease is not defined by a pathognomonic lesion it may be termed a syndrome. A syndromal (Received in revised form 6 February 1995).
MEDICAL TYNE,
NE2
SCHOOL, ‘+HH,
ENGLAND
policy
diagnosis defines a condition on the basis of a cluster of several features selected from a restricted list (these features may include symptoms, signs, and laboratory tests) rather than a unitary lesion. With a syndroma1 diagnosis, the Henle-Koch postulates are inapplicable because a pathognomonic lesion is (by definition) lacking. Investigation of etiology must then be “epidemiological’‘-at the level of the population rather than the individual. As syndromes make up a substantial proportion of present-day morbidity and mortality [6], various models of epidemiological causation have been devised to investigate them. The most famous are Bradford Hill’s criteria, but several other lists have been suggested [8,9]. These criteria include a specific (probabilistic, although not necessarily oneto-one) association between putative cause and disease, a doseresponse relationship between the putative cause and disease incidence, consistency of results between types of epidemiological study, and back-up with evidence from nonepidemiological sciences suggesting probable causal mechanisms. Bradford Hill’s criteria are similar in structure to the Henle-Koch postulates in that both require evidence of a positive and negative association, followed by observations suggesting that the putative cause is the most important independent variable. But unlike the HenleKoch postulates, Bradford Hill’s criteria are probabilistic and observational; they rely on statistical associations rather than a one-to-one association, and observation of disease and plausible experimental analogies are substituted for experimental induction of the pathognomonic lesion. The imputed causal association is at the group level, and does not indicate the cause of disease in individual subjects. Furthermore, unlike the Henle-Koch postulates, Bradford Hill’s criteria are multidisciplinary; requiring both population studies and back-up from various basic sciences. Although they aim to identify a single necessary cause of a disease, Bradford Hill’s criteria cannot do this with the fullest “scientific” rigor. Instead they were meant to constitute a sensible or reasonable standard for imputing causation in practice or for policy reasons-where knowledge of a disease may be incomplete, animal models may be lacking and controlled experiments are often impossible. Because information is incomplete, causation is to that extent uncertain. Gains in the scope of causal attribution are made by paying a price in diminished rigor. More recently a wide variety of “multicausal” models have been proposed for epidemiology. These share the idea that diseases (or other health-related states) are the consequence of a network, matrix, constellation, or web made up of determinants, component or contributory causes [4,9-131. Taken individually, each of these “risk factor” variables is not required to satisfy Bradford Hill’s criteria, just to influence the chance of developing a disease or other health-related state. Different mixtures of risk factors from a potentially large list can summate in different ways to produce the same end point, but no specific risk factor is a necessary cause either at the level of the individual or the
106 population. Multicausal models may be constructed using evidence considerably less conclusive or complete than is necessary for the Bradford Hill criteria, therefore the imputation of causation is correspondingly less secure. The historical shift in epidemiological reasoning from the rigor of the Henle-Koch postulates to the more relaxed criteria of a multicausal model, from the necessary cause to the probabilistic risk factor, and from the individual to the population as the unit of investigtion, can also be seen as a shift from the methods of science to the methods of the social sciences [12,14]. Throughout the development of the subject of epidemiology there has been a move away from the attempt to construct chains of necessary c~uscs, each defined with maximum rigor; toward the construction of an epidemiological mosaic made from pieces of etiological evidence, none of which, when taken individually, need be compelling [14]. Th e mosaic approach to establishing causation involves not an appeal to the minimum of clear evidence, but instead an appeal to a large mass of fragmentary evidence, cemented together by a plausible and systematic interpretation. Epidemiological practice has therefore become fundamentally both multidisciplinary and interdisciplinary-it uses multiple disciplines and works at the interface between them. While the individual items of data that constitute a mosaic may be judged to have greater or lesser scientific validity, the method by which these evidential fragments are interlinked is not a scientific process because the combination of evidence from diverse scientific disciplines cannot itself be a scientific discipline [IS, 161. Gaps in the available evidence from one discipline must be bridged or circumvented by making links to another discipline and back again [14]. Because it works between disciplines, and because data are drawn from numerous “incommensurable” (or qualitatively different) disciplines, epidemiology must rely on judgment to a much greater extent than the basic sciences. Integration between evidence from different disciplines is achieved by “common-sense” or “soft” methods such as analogy, illustrative examples, ad hoc hypothesis formation, and appeals to mutual consistency and plausibility [14,17]. Epidemiology is not, therefore, a discipline in its own right; instead it is an interdisciplinary practice; and epidemiological attribution of causation is not a science but an activity more akin to the arguing of a case in law: based on evidence but not dictated by the evidence. And because the validity of each fragment of evidence, taken one at a time, is not crucial to the case for causation, this kind of epidemiology also depends to a great extent on sheer weight of evidence to back its claims: the massed number of studies carries force rather than the quality of specific studies. It follows that epidemiological claims of causation cannot be refuted by any single, crucial contradictory item of evidence, no matter how strong or well replicated that countereviderice may be. Epidemiological hypotheses are supported by a “network” of linked evidence from numerous disciplines, and cutting any single strand may weaken a net, but does not break it. By contrast, disproving even a single item of evidence can, in principle at least, overthrow a whole scientific hypothesis, because a causal chain is only as strong as its weakest link. Contradictory findings cannot do more than alter the balance of probability of multifactorial epidemiological causation. This explains the long life, resilience, and apparent irrefutability of such epidemiological hypotheses in the face of powerful items of apparently contradictory evidence [6,14]. Science can be considered the best method yet discovered for generating reliable results 1181.This is achieved by the testing of inferences extrapolated along chains of necessary causes. A scientific hypothesis is meant to be “universal” and applicable in other situations, the
Charlton nature of which is defined. This quality of generalizability can be termed external walidity [4]. External validity refers to the extent that it is legitimate to extrapolate from a particular instance to a general law. Scientific disciplines might be defined as those areas of research where procedures for establishing external validity are well understood and where predictions tend to be highly reliable. Epidemiologists have not discovered new modes of valid causal reasoning or new ways of generating “reliable knowledge” by adopting multicausal risk factor models; such epidemiologists have merely devised less rigorous modes of reasoning that sacrifice validity in order to allow a wider range of data to be brought to bear on decision making in situations in which there is insufficient evidence for a scientifically valid answer [19,20]. The shift towards more and more relaxed criteria of causation and disproof has led epidemiology to move away from scientific status; and the subject is now better regarded as a practice for informing “evidence based” health administration [21]; and as a pre-scientific method for generating, but not testing, hypotheses [3,22]. The nonscientific status of “mosaic” reasoning may be obscured by an increased use of complex statistical techniques and modern technologies for data gathering, but at root “black box” epidemiology is usually driven by questions of policy rather than the search for analytical understanding. In summary, risk factor epidemiology cannot be regarded as a scientific discipline because it aims at concrete usefulness rather than abstract truthfulness. Properly considered, multicausal epidemiology is a pragmatic, problem-solving, evidence-based Technik [23] that uses any available and relevant data to give a “best current guess” answer to an important and urgent question of policy [3,19,20,21]. But there is an inevitable trade-off between methodological rigor and the impatient demands of health policy. Enhanced relevance and applicability of epidemiology have been attained only at the cost of diminished validity of its causal inferences. A mosaic is a marvellous construction, which assembles something beautiful, and perhaps useful, from colorful fragments broken off artifacts that may have been intended for other purposes. But for hauling the body of medical science up to greater heights of reliable knowledge, there is nothing to beat a purpose-built chain of necessary causeswith each link of evidence well tested, and cemented to its neighbors by bonds of strong inference. Thanks are due to Jonathan Rees, G&n
Rye, Martin
White, and Fraser Chadton.
References 1. McCormick
1. The abuse of language and logic in epidemiology.
Perspect
Biol Med 1992; 35: 186-188. 2. Skrabenek P. The epidemiology of errors. Lancet 1993; 342: 1502. 3. Skrabanek P. The emptiness of the black box. Epidemiology 1994; 5: 553-555. Little, Brown & Co., Boston, 1986. 4. Rothman K]. Modem Epidemiology. Oxford University Press, Oxford, 5. Last JM. A Dictionary of Epidemiology. 1988.
6. Stehbens
10.
WE. Causality in medical science with particular reference to heart disease and atherosclerosis. Perspect Biol Med 1992; 36: 97-l 19. Evans SA. Causation and disease: The Henle-Koch postulates revisited. Yale ] Biol Med 1976; 49: 175-195. Bradford Hill A. Bradford Hill’s Principles of Medical Statistics, 12th ed. Edward Arnold, London, 1991. MacMahon B, Pugh TF. Epidemiology: Principles and Methods. Little, Brown & Co.. Boston, 1970. Mausner JS, Kramer S. Mausner & Bahn Epidemiology, 2nd ed. W.B.
11.
Saunders,Philadelphia, Pennsylvania, 1985. SusserM.: Causal Thinking in the Health Sciences:
7. 8. 9.
gies of Epidemiology. 12.
Oxford
University
Concepts and StratePress, New York, 1973.
SusserM. Epidemiology in the United States after World War II: The
Causal Chains or Mosaics?
13. 14. 15. 16. 17.
evolution of technique. In: Epidemiology, Health and Society: Selected Papers. Oxford University Press, New York, 1987, pp. 22-49. Olsen J. Some consequences of adopting a conditional deterministic causal model in epidemiology. Eur J Public Health 1993; 3: 204209. Fujimara JH, Chou DY. Dissent in science: Styles of scientific practice and the controversy over the cause of AIDS. Social Sci Mecl 1994; 38: 1017-1036. Rose SPR. Molecules and Minds. Open University Press, Milton Keynes, 1987. Charlton BG. A critique of biological psychiatry. Psycho1 Med 1990; 20: 3-6. Barkow JH, Cosmides L, Tooby J. The Adapated Mind: Evolutionary Psychology and the Generation of Culture. Oxford University Press, New York. 1992.
107 18. Ziman JM. Reliable Knowledge-an Exploration of the Grounds for Belief in Science. Cambridge University Press, Cambridge, 1978. 19. Charlton BG. Practice guidelines and practical judgement: The role of mega-trials, meta-analysis and consensus. Br J Gen Practice 1994; 44: 290-291. 20. Charlton BG. The role of randomisation and group studies in basic science, clinical science and mega-trials: Methodological issues and implications for clinical effectiveness. J R cdl Phys London 1995; 29: 96-100. 21. Skrabanek P. The Death of Humane Medicine and the Rise of Coercive Healthism. Social Affairs Unit, London, 1994. 22. Magee B. Popper. Fontana, London, 1973. 23. Kelly MP. A suitable case for Technik: Behavioural science in the postgraduate medical curriculum. Med Educ 1990; 24: 271-279.
J Clin Epidemiol Vol. 49, No. 1, pp. 105-107, 1996 Copyright 0 1996 Elsevler Science Inc.
ELSEVIER
SECOND
Attribution
THOUGHTS
of Causation in Epidemiology: Chain or Mosaic? Bruce G. Chrlton DEPARTMENT
UNIVERSITY
OF EPIDEMIOLOGY
OF NEWCASTLE
KEY WORDS. Epidemiology,
causation,
PUBLIC
HEALTH,
NEWCASTLE
UPON
science, philosophy,
health
UPON
AND TYNE,
In the following essay I will argue that historical trends in epidemiological reasoning have meant that ascribing causal status to an independent variable has become easier, while refuting a causal hypothesis has become harder. The result has been the development of a broadly applicable, but dubiously valid, practice of multicausal risk factor epidemiology that is used to investigate etiology in a wide range of loosely defined health states [l-3]. Epidemiology has widened its scope, but at the cost of blunting its discriminative power. This process seems to have been driven by the demands of health policy decision making, and not by methodological advances in the criteria for valid inference. I will suggest that one consequence of the adoption of more inclusive methods is that contemporary epidemiological causal ascriptions do not have the generalizability or universality normally associated with scientific knowledge. Analytical epidemiology is concerned with studying associations between variables in a population, with the aim of determining causes of health-related states (usually disease). Causes may be described as necessary or sufficient [4,5]. Necessary causes are required in order that an effect may follow-exclude the necessary cause and you have prevented the effect. Sufficient causes are adequate by themselves to produce an effect-after a sufficient cause the effect will inevitably follow. The basic sciences are built on the concept of necessary cause, and sufficient causes are (virtually) never seen in biology [6]. The most famous criteria by which an agent is defined as the necessary cause of a disease are the Henle-Koch postulates. These criteria have been extended beyond their original concern with infectious pathogens to refer to any putative causal agent [7]. To satisfy the postulates an agent should be found in all cases of the disease (positive association), should be absent from all nondiseased individuals (negative association), and the disease should be reproducible in an animal model using a pure preparation of the agent. The principles underlying the Henle-Koch postulates are therefore twofold: to establish a specific arsociation between the putative cause and effect and then to devise a tightly controlled eqeriment in which exposure of the organism to the putative cause may be manipulated to determine whether it is a necessary cause. Experiment involves contriving a situation in which the putative cause is the only significant independent variable-all other known influencing variables being excluded or held constant. For ethical reasons the deliberate induction of disease is usually permissible only in animals. Application of the Henle-Koch postulates requires definition of a disease by a pathognomonic ksion, the presence of which is both necessary and sufficient for diagnosis. The purpose of animal models (or modelling disease in cell culture or computer simulation, etc.) is to reproduce the pathognomonic lesion. When a disease is not defined by a pathognomonic lesion it may be termed a syndrome. A syndromal (Received in revised form 6 February 1995).
MEDICAL TYNE,
NE2
SCHOOL, ‘+HH,
ENGLAND
policy
diagnosis defines a condition on the basis of a cluster of several features selected from a restricted list (these features may include symptoms, signs, and laboratory tests) rather than a unitary lesion. With a syndroma1 diagnosis, the Henle-Koch postulates are inapplicable because a pathognomonic lesion is (by definition) lacking. Investigation of etiology must then be “epidemiological’‘-at the level of the population rather than the individual. As syndromes make up a substantial proportion of present-day morbidity and mortality [6], various models of epidemiological causation have been devised to investigate them. The most famous are Bradford Hill’s criteria, but several other lists have been suggested [8,9]. These criteria include a specific (probabilistic, although not necessarily oneto-one) association between putative cause and disease, a doseresponse relationship between the putative cause and disease incidence, consistency of results between types of epidemiological study, and back-up with evidence from nonepidemiological sciences suggesting probable causal mechanisms. Bradford Hill’s criteria are similar in structure to the Henle-Koch postulates in that both require evidence of a positive and negative association, followed by observations suggesting that the putative cause is the most important independent variable. But unlike the HenleKoch postulates, Bradford Hill’s criteria are probabilistic and observational; they rely on statistical associations rather than a one-to-one association, and observation of disease and plausible experimental analogies are substituted for experimental induction of the pathognomonic lesion. The imputed causal association is at the group level, and does not indicate the cause of disease in individual subjects. Furthermore, unlike the Henle-Koch postulates, Bradford Hill’s criteria are multidisciplinary; requiring both population studies and back-up from various basic sciences. Although they aim to identify a single necessary cause of a disease, Bradford Hill’s criteria cannot do this with the fullest “scientific” rigor. Instead they were meant to constitute a sensible or reasonable standard for imputing causation in practice or for policy reasons-where knowledge of a disease may be incomplete, animal models may be lacking and controlled experiments are often impossible. Because information is incomplete, causation is to that extent uncertain. Gains in the scope of causal attribution are made by paying a price in diminished rigor. More recently a wide variety of “multicausal” models have been proposed for epidemiology. These share the idea that diseases (or other health-related states) are the consequence of a network, matrix, constellation, or web made up of determinants, component or contributory causes [4,9-131. Taken individually, each of these “risk factor” variables is not required to satisfy Bradford Hill’s criteria, just to influence the chance of developing a disease or other health-related state. Different mixtures of risk factors from a potentially large list can summate in different ways to produce the same end point, but no specific risk factor is a necessary cause either at the level of the individual or the
106 population. Multicausal models may be constructed using evidence considerably less conclusive or complete than is necessary for the Bradford Hill criteria, therefore the imputation of causation is correspondingly less secure. The historical shift in epidemiological reasoning from the rigor of the Henle-Koch postulates to the more relaxed criteria of a multicausal model, from the necessary cause to the probabilistic risk factor, and from the individual to the population as the unit of investigtion, can also be seen as a shift from the methods of science to the methods of the social sciences [12,14]. Throughout the development of the subject of epidemiology there has been a move away from the attempt to construct chains of necessary c~uscs, each defined with maximum rigor; toward the construction of an epidemiological mosaic made from pieces of etiological evidence, none of which, when taken individually, need be compelling [14]. Th e mosaic approach to establishing causation involves not an appeal to the minimum of clear evidence, but instead an appeal to a large mass of fragmentary evidence, cemented together by a plausible and systematic interpretation. Epidemiological practice has therefore become fundamentally both multidisciplinary and interdisciplinary-it uses multiple disciplines and works at the interface between them. While the individual items of data that constitute a mosaic may be judged to have greater or lesser scientific validity, the method by which these evidential fragments are interlinked is not a scientific process because the combination of evidence from diverse scientific disciplines cannot itself be a scientific discipline [IS, 161. Gaps in the available evidence from one discipline must be bridged or circumvented by making links to another discipline and back again [14]. Because it works between disciplines, and because data are drawn from numerous “incommensurable” (or qualitatively different) disciplines, epidemiology must rely on judgment to a much greater extent than the basic sciences. Integration between evidence from different disciplines is achieved by “common-sense” or “soft” methods such as analogy, illustrative examples, ad hoc hypothesis formation, and appeals to mutual consistency and plausibility [14,17]. Epidemiology is not, therefore, a discipline in its own right; instead it is an interdisciplinary practice; and epidemiological attribution of causation is not a science but an activity more akin to the arguing of a case in law: based on evidence but not dictated by the evidence. And because the validity of each fragment of evidence, taken one at a time, is not crucial to the case for causation, this kind of epidemiology also depends to a great extent on sheer weight of evidence to back its claims: the massed number of studies carries force rather than the quality of specific studies. It follows that epidemiological claims of causation cannot be refuted by any single, crucial contradictory item of evidence, no matter how strong or well replicated that countereviderice may be. Epidemiological hypotheses are supported by a “network” of linked evidence from numerous disciplines, and cutting any single strand may weaken a net, but does not break it. By contrast, disproving even a single item of evidence can, in principle at least, overthrow a whole scientific hypothesis, because a causal chain is only as strong as its weakest link. Contradictory findings cannot do more than alter the balance of probability of multifactorial epidemiological causation. This explains the long life, resilience, and apparent irrefutability of such epidemiological hypotheses in the face of powerful items of apparently contradictory evidence [6,14]. Science can be considered the best method yet discovered for generating reliable results 1181.This is achieved by the testing of inferences extrapolated along chains of necessary causes. A scientific hypothesis is meant to be “universal” and applicable in other situations, the
Charlton nature of which is defined. This quality of generalizability can be termed external walidity [4]. External validity refers to the extent that it is legitimate to extrapolate from a particular instance to a general law. Scientific disciplines might be defined as those areas of research where procedures for establishing external validity are well understood and where predictions tend to be highly reliable. Epidemiologists have not discovered new modes of valid causal reasoning or new ways of generating “reliable knowledge” by adopting multicausal risk factor models; such epidemiologists have merely devised less rigorous modes of reasoning that sacrifice validity in order to allow a wider range of data to be brought to bear on decision making in situations in which there is insufficient evidence for a scientifically valid answer [19,20]. The shift towards more and more relaxed criteria of causation and disproof has led epidemiology to move away from scientific status; and the subject is now better regarded as a practice for informing “evidence based” health administration [21]; and as a pre-scientific method for generating, but not testing, hypotheses [3,22]. The nonscientific status of “mosaic” reasoning may be obscured by an increased use of complex statistical techniques and modern technologies for data gathering, but at root “black box” epidemiology is usually driven by questions of policy rather than the search for analytical understanding. In summary, risk factor epidemiology cannot be regarded as a scientific discipline because it aims at concrete usefulness rather than abstract truthfulness. Properly considered, multicausal epidemiology is a pragmatic, problem-solving, evidence-based Technik [23] that uses any available and relevant data to give a “best current guess” answer to an important and urgent question of policy [3,19,20,21]. But there is an inevitable trade-off between methodological rigor and the impatient demands of health policy. Enhanced relevance and applicability of epidemiology have been attained only at the cost of diminished validity of its causal inferences. A mosaic is a marvellous construction, which assembles something beautiful, and perhaps useful, from colorful fragments broken off artifacts that may have been intended for other purposes. But for hauling the body of medical science up to greater heights of reliable knowledge, there is nothing to beat a purpose-built chain of necessary causeswith each link of evidence well tested, and cemented to its neighbors by bonds of strong inference. Thanks are due to Jonathan Rees, G&n
Rye, Martin
White, and Fraser Chadton.
References 1. McCormick
1. The abuse of language and logic in epidemiology.
Perspect
Biol Med 1992; 35: 186-188. 2. Skrabenek P. The epidemiology of errors. Lancet 1993; 342: 1502. 3. Skrabanek P. The emptiness of the black box. Epidemiology 1994; 5: 553-555. Little, Brown & Co., Boston, 1986. 4. Rothman K]. Modem Epidemiology. Oxford University Press, Oxford, 5. Last JM. A Dictionary of Epidemiology. 1988.
6. Stehbens
10.
WE. Causality in medical science with particular reference to heart disease and atherosclerosis. Perspect Biol Med 1992; 36: 97-l 19. Evans SA. Causation and disease: The Henle-Koch postulates revisited. Yale ] Biol Med 1976; 49: 175-195. Bradford Hill A. Bradford Hill’s Principles of Medical Statistics, 12th ed. Edward Arnold, London, 1991. MacMahon B, Pugh TF. Epidemiology: Principles and Methods. Little, Brown & Co.. Boston, 1970. Mausner JS, Kramer S. Mausner & Bahn Epidemiology, 2nd ed. W.B.
11.
Saunders,Philadelphia, Pennsylvania, 1985. SusserM.: Causal Thinking in the Health Sciences:
7. 8. 9.
gies of Epidemiology. 12.
Oxford
University
Concepts and StratePress, New York, 1973.
SusserM. Epidemiology in the United States after World War II: The
Causal Chains or Mosaics?
13. 14. 15. 16. 17.
evolution of technique. In: Epidemiology, Health and Society: Selected Papers. Oxford University Press, New York, 1987, pp. 22-49. Olsen J. Some consequences of adopting a conditional deterministic causal model in epidemiology. Eur J Public Health 1993; 3: 204209. Fujimara JH, Chou DY. Dissent in science: Styles of scientific practice and the controversy over the cause of AIDS. Social Sci Mecl 1994; 38: 1017-1036. Rose SPR. Molecules and Minds. Open University Press, Milton Keynes, 1987. Charlton BG. A critique of biological psychiatry. Psycho1 Med 1990; 20: 3-6. Barkow JH, Cosmides L, Tooby J. The Adapated Mind: Evolutionary Psychology and the Generation of Culture. Oxford University Press, New York. 1992.
107 18. Ziman JM. Reliable Knowledge-an Exploration of the Grounds for Belief in Science. Cambridge University Press, Cambridge, 1978. 19. Charlton BG. Practice guidelines and practical judgement: The role of mega-trials, meta-analysis and consensus. Br J Gen Practice 1994; 44: 290-291. 20. Charlton BG. The role of randomisation and group studies in basic science, clinical science and mega-trials: Methodological issues and implications for clinical effectiveness. J R cdl Phys London 1995; 29: 96-100. 21. Skrabanek P. The Death of Humane Medicine and the Rise of Coercive Healthism. Social Affairs Unit, London, 1994. 22. Magee B. Popper. Fontana, London, 1973. 23. Kelly MP. A suitable case for Technik: Behavioural science in the postgraduate medical curriculum. Med Educ 1990; 24: 271-279.