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Measurement and Causality in Medical ScienceAlex Broadbent, Olaf Dammann, Leah McClimans, Zinhle Mncube, Benjamin SmartSummaryMedical science seeks to quantify various phenomena that are hard to quantify. Among these, the quantitative measurement of “causal effects” of exposures or treatments on health outcomes is particularly interesting from a philosophical perspective, since very little philosophical work seeks to understand how causation or related phenomena could be quantifiable. Dismissing such measures out of hand as meaningless is irresponsible given their centrality in medical research. This symposium makes a start on identifying and resolving some of the conceptual difficulties the medical sciences face in devising meaningful causal measures and understanding them.These questions are pursued with particular reference to diagnostic tests, measures of attributability, heritability, and the role of measurement in evidence based medicine. The participants in the symposium include both philosophers of science and working scientists. TopicMeasurement in the medical sciences faces several interesting challenges. Many of the phenomena that medical science needs to measure are hard to quantify. In particular, quantitative measures of causal phenomena pose conceptual difficulties. Many philosophical treatments of causation consider only propositions concerning the presence or absence of causation (and typically singular causation at that), propositions characterised by the form “C causes E”. But in the medical sciences, it is common to find quantitative causal claims. These have roughly the form “C has n effect on E”. Correspondingly, much medical research is directed, not merely at finding out whether C has some effect on E, but what the value of n is.Thus, for example, in drug trials, the aim is not merely to discover whether the drug has a positive effect on the outcome, but how large that effect is. In assessing the evidence for a causal link between Zika virus and microcephaly, the question is not merely whether there is a causal link (whether Zika features in the causal history of some cases) but how “strong” or “large” the effect is. In considering the long term effects of breast-feeding on IQ, the question is not merely how strong the evidence is for a causal effect but how large the effect is. Stronger evidence for a smaller effect may be of considerably less interest for medical or public health purposes than weaker evidence for a larger effect.The idea of measuring causality is not one that fits readily into standard philosophical frameworks for thinking about causation; indeed from a philosophical perspective it is tempting to dismiss the idea as senseless. But a more constructive response would be to seek to make sense of what medical research scientists are seeking to achieve.Perhaps because of the lack of philosophical work on the topic, the medical science literature, notably in epidemiology, has proceeded to devise its own ways of thinking about and expressing quantitative measures of causality. These developments encounter difficulties that are ripe for philosophical attention. This symposium explores a collection of these difficulties concerning measurement and causation, especially as they appear in epidemiology, clinical medicine, and genetics.MeasurementIn clinical epidemiology, the validity of a diagnostic test is established in comparison to a gold standard, usually a reference method with known validity. Olaf Dammann has recently addressed the qualitative consequences of “Fletcher’s Paradox”, the case where a new test looks better than the gold standard although it is less accurate. He suggests a set of formulas to calculate the degree of congruence (co-positivity and co-negativity) of two tests (i.e., new and gold standard) in a simulated scenario where the true disease status is known. At this symposium, he will provide and discuss examples based on these congruence formulas and explore consequences for the theory of measurement in medical test validation.In addition to methodological issues (such as those addressed by Dammann), ‘measurement’ is of particular importance to a number of topics in the philosophy of medicine. In recent years, for example, many philosophers have discussed in detail the virtues and vices of evidence based medicine (EBM). These debates have primarily focused on whether EBM is, in practice, good for clinical medicine; that is, whether the move towards evidence based practice has in any way improved the wellbeing of patients. But few, if any, have discussed the connection between measurement and evidence that EBM brings to light. Smart’s contribution to this symposium is intended to go some way towards correcting this lack.CausationCausation and causal inference are of fundamental importance to both epidemiology, and research in genetic heritability. The former, since, in essence, epidemiology is the study of the distribution and determinants of disease. The latter, since the primary goal of work in heritability is to establish the strength of the causal relationship between genotypic and phenotypic differences.One popular approach to causal inference in epidemiology is the counterfactual, or ‘potential outcomes approach’. This strategy of effect-measurement requires the comparison of the outcome of an ‘actual’ study, to that of some hypothetical scenario. The difference between the two is the inferred ‘strength’ of the causal connection between the intervention and the outcome. Some have argued, however, that the intervention must be ‘well specified’ in order for the causal inference to be justified. This requirement is in stark contrast to the way epidemiology has been practiced in the last few decades, and does not represent the methodology employed in some of its defining discoveries, including the identification of smoking as a cause of lung cancer. These recent developments have thus given rise to a current debate, with which Broadbent’s contribution to this symposium engages.For many years, given the relationship between heritability and environmental factors, philosophers of science have agreed that that heritability estimates do not indicate the causal strength of genes on phenotypic variance. However, in this symposium, Mncube will argue that heritability estimates can bear a causal interpretation when: (a) there is no statistical gene-environment interaction, (b) there is small to no gene-environment correlation, and (c) only within the domain of populations that have similar causally salient features.Papers Fletchers’ Paradox: Effects of a Not So Golden Gold Standard on Measures of Diagnostic Test ValidityOlaf DammannIn clinical epidemiology, the validity of a diagnostic test is established in comparison to a gold standard, usually a reference method with known validity. The comparison is performed by measuring the new test’s sensitivity, specificity, positive and negative predictive values, as well as its accuracy (Figure). 1270000I have previously outlined the qualitative consequences of “Fletcher’s Paradox”, the case where a new test looks better than the gold standard although it is less accurate. I have also suggested a set of formulas to calculate the degree of congruence (co-positivity and co-negativity) of two tests (i.e., new and gold standard) in a simulated scenario where the true disease status is known. In this presentation, I will provide and discuss examples based on these congruence formulas and explore consequences for the theory of measurement in medical test validation.How Evidence Based Medicine brings the Connection between Evidence and Measurement into FocusBenjamin SmartProponents of evidence based medicine (EBM) take the evidence provided by epidemiological studies to provide better justification for clinical decisions, than expert opinion/intuition or ‘mechanistic reasoning’. EBM thus dictates that whether or not an individual should be treated in a particular way, depends upon whether or not clinical trials have shown the treatment in question to be beneficial, effective, and cost-efficient, relative to the alternatives. This strategy, it is argued, limits the risk of prescribing inefficient and/or harmful treatments, improving overall mortality and morbidity rates, as well as quality-of-life.EBM is often associated with a hierarchy of evidence. At the top of the hierarchy sit randomised controlled trials (RCTs), followed by observational studies such as case-control and cohort studies. At the bottom of the evidence hierarchy are mechanistic reasoning, clinical judgement and expert opinion (Howick 2011; Cartwright 2007). Epidemiological studies such as RCTs and observational studies are grounded by statistics/quantitative data, which, of course, one can only obtain through ‘measurement’; it is unsurprising, then, that McClimans has suggested that the ‘“paradigm shift” to EBM is not so much a shift toward the reliance on evidence as it is a shift toward reliance on measurement.” (2013, 521).??Given the role of measurement in EBM, it is clear that the methods used when collecting data for epidemiological studies, and the nature of the data itself, is of fundamental importance - only when the appropriate measures are employed, and the appropriate data is collected, will the evidence used in EBM be good evidence. In this paper I examine the types of evidence EBM takes to be of primary importance, and highlight the relationship between evidence and measurement that EBM brings into focus.?How Much Mortality Does Obesity Cause? Measuring Causality in PopulationsAlex BroadbentIn 2008 Miguel and Hernán and Lisa Taubman argued that seeking to estimate the effect of obesity on mortality in a population was meaningless because different ways of intervening to reduce obesity would result in different changes in mortality. They argued this in a journal largely devoted to publishing estimates of this kind. Their point is part of a larger methodological movement in some parts of epidemiology, aiming to tighten up talk of causality by insisting that causal effects always be specified relative to some contemplated intervention, even where the study is observational and no actual intervention has occurred. The methodological questions raised by the Potential Outcomes Approach have sparked considerable discussion concerning causal inference ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"jZhg2DoZ","properties":{"formattedCitation":"{\\rtf (VanderWeele and Hern\\uc0\\u225{}n 2012; VanderWeele and Robinson 2014; Glymour and Glymour 2014; Broadbent 2015)}","plainCitation":"(VanderWeele and Hernán 2012; VanderWeele and Robinson 2014; Glymour and Glymour 2014; Broadbent 2015)"},"citationItems":[{"id":722,"uris":[""],"uri":[""],"itemData":{"id":722,"type":"chapter","title":"Causal effects and natural laws: Towards a conceptualization of causal counterfactulas for nonmanipulable exposures, with application to the effects of race and sex","container-title":"Causality: Statistical Perspectives and Applications","publisher":"John Wiley and Sons","publisher-place":"Chichester","page":"101-113","event-place":"Chichester","author":[{"family":"VanderWeele","given":"Tyler J."},{"family":"Hernán","given":"Miguel A."}],"editor":[{"family":"Berzuini","given":"Carlo"},{"family":"Dawid","given":"Philip"},{"family":"Bernardinelli","given":"Luisa"}],"issued":{"date-parts":[["2012"]]}}},{"id":715,"uris":[""],"uri":[""],"itemData":{"id":715,"type":"article-journal","title":"On the Causal Interpretation of Race in Regressions Adjusting for Confounding and Mediating Variables","container-title":"Epidemiology","page":"473-484","volume":"25","issue":"4","author":[{"family":"VanderWeele","given":"Tyler J"},{"family":"Robinson","given":"Whitney R"}],"issued":{"date-parts":[["2014"]]}}},{"id":723,"uris":[""],"uri":[""],"itemData":{"id":723,"type":"article-journal","title":"Race and Sex Are Causes","container-title":"Epidemiology","page":"488-490","volume":"25","issue":"4","author":[{"family":"Glymour","given":"Clark"},{"family":"Glymour","given":"Madelyn R"}],"issued":{"date-parts":[["2014"]]}}},{"id":754,"uris":[""],"uri":[""],"itemData":{"id":754,"type":"article-journal","title":"Causation and Prediction in Epidemiology: A Guide to the Methodological Revolution","container-title":"Studies in History and Philosophy of Biological and Biomedical Sciences","volume":"in press","author":[{"family":"Broadbent","given":"Alex"}],"issued":{"date-parts":[["2015"]]}}}],"schema":""} (VanderWeele and Hernán 2012; VanderWeele and Robinson 2014; Glymour and Glymour 2014; Broadbent 2015; Vandenbroucke, Broadbent and Pearce, 2016). In this paper, however, I focus on the measurement question raised so effectively by Hernán and Taubman. The question is whether measures of the proportion of an outcome that is attributable to a certain causal factor are meaningful, and if so, what they mean.A popular but inaccurate way to understand such measures is as telling us what proportion of an outcome would disappear if the exposure were absent. This is inaccurate partly because it is vague, as Hernán and Taubman point out: different replacements for the exposure would result in different outcomes. It is also inaccurate because some replacements might cause the outcome as well, yielding an underestimate of the proportion of the outcome in which the exposure is causally involved ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"KxYH4RNs","properties":{"formattedCitation":"(Greenland and Robins 1988; Greenland 2005)","plainCitation":"(Greenland and Robins 1988; Greenland 2005)"},"citationItems":[{"id":501,"uris":[""],"uri":[""],"itemData":{"id":501,"type":"article-journal","title":"Conceptual Problems in the Definition and Interpretation of Attributable Fractions","container-title":"American Journal of Epidemiology","page":"1185-1197","volume":"128","issue":"6","author":[{"family":"Greenland","given":"Sander"},{"family":"Robins","given":"James"}],"issued":{"date-parts":[["1988"]]}}},{"id":748,"uris":[""],"uri":[""],"itemData":{"id":748,"type":"article-journal","title":"Epidemiologic measures and policy formulation: lessons from potential outcomes","container-title":"Emerging Themes in Epidemiology","page":"1-7","volume":"2","issue":"5","DOI":"10.1186/1742-7622-2-5","author":[{"family":"Greenland","given":"Sander"}],"issued":{"date-parts":[["2005"]]}}}],"schema":""} (Greenland and Robins 1988; Greenland 2005). Elsewhere I have provided an account of measures of attributable fraction making essential reference to the notion of explanation, which is sensitive to redundancy-type difficulties of this kind ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"NFZ4lLPL","properties":{"formattedCitation":"(Broadbent 2013)","plainCitation":"(Broadbent 2013)"},"citationItems":[{"id":466,"uris":[""],"uri":[""],"itemData":{"id":466,"type":"book","title":"Philosophy of Epidemiology","collection-title":"New Directions in the Philosophy of Science","publisher":"Palgrave Macmillan","publisher-place":"London and New York","event-place":"London and New York","author":[{"family":"Broadbent","given":"Alex"}],"issued":{"date-parts":[["2013"]]}}}],"schema":""} (Broadbent 2013). I do not, however, deal with Hernán and Taubman’s attack there, nor address the specific claim that unless a specific intervention is intended, estimates of—for example—the effect of obesity on mortality are meaningless. In this paper I seek to assess the impact of this objection, separate what is right about it from what is overstated, and identify a theoretical basis for some practical guidelines to guide the use of such measures in real-life epidemiological work.HeritabilityZinhle MncubeHeritability estimates “have been regarded as important primarily on the expectation that they would furnish valuable information about the causal strength of genetic influence on phenotypic differences” (Sesardic, 1993:399; slightly rephrased in Sesardic, 2005:22). But for over 30 years, a consensus has existed in the philosophy of science that heritability estimates do not indicate the causal strength of genes on phenotypic variance (Oftedal, 2005; Downes, 2016). Theorists provide conceptual and methodological arguments to the effect that: (i) because of gene-environment interaction, genotypes and environment continuously interact during the individual development of phenotypes such that we cannot partition the causes of variation; (ii) the existence of gene-environment correlation – cases in which two different and separate sources of phenotypic variance (genetic and environmental variance) are correlated – again highlights the idea that it is difficult to entangle genotypic and environmental effects of phenotypic variance; and (iii) heritability estimates are environment-, time- and population-dependent, therefore they cannot be generalized onto other populations. As such, most theorists are against defining heritability as a measure of causal strength of genetic variance on total phenotypic variance. Against this consensus, I propose that heritability estimates can bear a causal interpretation when: (a) there is no statistical gene-environment interaction, (b) there is small to no gene-environment correlation, and (c) only within the domain of populations that have similar causally salient features (Sesardic, 2005; Tal, 2009, 2011). When these conditions are met, it makes sense to causally interpret that heritability estimate as a measure of the causal strength of differences in genes on total phenotypic variance. Viewed in this different light, a question arises about the power of heritability to predict diseases in populations, and perhaps even in individuals.Clinical Outcomes Assessments and Epistemic RiskLeah McClimansClinical Outcomes Assessments (COAs) are commonly used measures to quantify a patients’ symptoms, overall mental state or the effects of a disease or condition on how patients function. They can be used to in the contexts of clinical trails, drug labeling claims, quality of care assessments and priority setting. But the adequacy of the psychometric and econometric methods used to develop these instruments has been a topic of much debate for over a century. Recent discussion in leading psychometric journals has focused on the ontology of psychological attributes and what measurement theories and methods befit them. While there is some consensus amongst psychometricians that a realist ontology is necessary for valid and interpretable psychological measurement, including COAs ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"r61Irlkf","properties":{"formattedCitation":"(Michell 2005; Borsboom 2006; Maul 2013)","plainCitation":"(Michell 2005; Borsboom 2006; Maul 2013)"},"citationItems":[{"id":1480,"uris":[""],"uri":[""],"itemData":{"id":1480,"type":"article-journal","title":"The logic of measurement: A realist overview","container-title":"Measurement","page":"285-94","volume":"38","author":[{"family":"Michell","given":"Joel"}],"issued":{"date-parts":[["2005"]]}}},{"id":1208,"uris":[""],"uri":[""],"itemData":{"id":1208,"type":"article-journal","title":"The attack of the psychometricians","container-title":"Psychometrika","page":"425-440","volume":"71","issue":"3","source":"PubMed Central","abstract":"This paper analyzes the theoretical, pragmatic, and substantive factors that have hampered the integration between psychology and psychometrics. Theoretical factors include the operationalist mode of thinking which is common throughout psychology, the dominance of classical test theory, and the use of “construct validity” as a catch-all category for a range of challenging psychometric problems. Pragmatic factors include the lack of interest in mathematically precise thinking in psychology, inadequate representation of psychometric modeling in major statistics programs, and insufficient mathematical training in the psychological curriculum. Substantive factors relate to the absence of psychological theories that are sufficiently strong to motivate the structure of psychometric models. Following the identification of these problems, a number of promising recent developments are discussed, and suggestions are made to further the integration of psychology and psychometrics.","DOI":"10.1007/s11336-006-1447-6","ISSN":"0033-3123","note":"PMID: 19946599\nPMCID: PMC2779444","journalAbbreviation":"Psychometrika","author":[{"family":"Borsboom","given":"Denny"}],"issued":{"date-parts":[["2006",9]]},"PMID":"19946599","PMCID":"PMC2779444"}},{"id":1483,"uris":[""],"uri":[""],"itemData":{"id":1483,"type":"article-journal","title":"On the ontology of psychological attributes","container-title":"Theory & Psychology","page":"752-69","volume":"23","issue":"6","author":[{"family":"Maul","given":"Andrew"}],"issued":{"date-parts":[["2013"]]}}}],"schema":""} (Michell 2005; Borsboom 2006; Maul 2013), many others, e.g. psychologists, epidemiologists, etc. who continue to design and employ measuring instruments built out of theories and methods that cannot sustain a realist ontology ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"WLfB0q4f","properties":{"formattedCitation":"(Michell 1999; Borsboom 2006)","plainCitation":"(Michell 1999; Borsboom 2006)"},"citationItems":[{"id":1489,"uris":[""],"uri":[""],"itemData":{"id":1489,"type":"book","title":"Measurement in Psychology","collection-title":"Ideas in Context","publisher":"Cambridge University Press","author":[{"family":"Michell","given":"Joel"}],"issued":{"date-parts":[["1999"]]}}},{"id":1208,"uris":[""],"uri":[""],"itemData":{"id":1208,"type":"article-journal","title":"The attack of the psychometricians","container-title":"Psychometrika","page":"425-440","volume":"71","issue":"3","source":"PubMed Central","abstract":"This paper analyzes the theoretical, pragmatic, and substantive factors that have hampered the integration between psychology and psychometrics. Theoretical factors include the operationalist mode of thinking which is common throughout psychology, the dominance of classical test theory, and the use of “construct validity” as a catch-all category for a range of challenging psychometric problems. Pragmatic factors include the lack of interest in mathematically precise thinking in psychology, inadequate representation of psychometric modeling in major statistics programs, and insufficient mathematical training in the psychological curriculum. Substantive factors relate to the absence of psychological theories that are sufficiently strong to motivate the structure of psychometric models. Following the identification of these problems, a number of promising recent developments are discussed, and suggestions are made to further the integration of psychology and psychometrics.","DOI":"10.1007/s11336-006-1447-6","ISSN":"0033-3123","note":"PMID: 19946599\nPMCID: PMC2779444","journalAbbreviation":"Psychometrika","author":[{"family":"Borsboom","given":"Denny"}],"issued":{"date-parts":[["2006",9]]},"PMID":"19946599","PMCID":"PMC2779444"}}],"schema":""} (Michell 1999; Borsboom 2006). In this paper my aim is to reframe this debate from one about the appropriate ontology of psychological attributes, e.g. realist, operationalist needed to achieve measurement, to a debate about epistemic risk. Drawing on Justin Biddle and Rebecca Kukla’s recent work on phronetic risk I argue that the debate over whether a COA is qualified, i.e. that within the context of use the results of the COA can be relied upon to measure a specific concept and have a specific interpretation, can be understood as a debate over the epistemic risk and values applied in different contexts.To illustrate the debate over qualifying COAs I compare instruments designed using two different measurement theories: Classical Test Theory (CTT) and Rasch Measurement Theory (RMT). CTT is usually associated with operationalist ontology and RMT is usually associated with realist ontology. Although neither theory is without criticism, it is generally claimed that RMT is more scientific than CTT. Yet CTT continues to be the dominate form of COAs and indeed other psychological measures. Some have tried to explain this discrepancy in terms of the dominance of CTT in the education of psychologists and others and the level of difficulty of RMT ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"bUB9F3Ez","properties":{"formattedCitation":"(Borsboom 2006)","plainCitation":"(Borsboom 2006)"},"citationItems":[{"id":1208,"uris":[""],"uri":[""],"itemData":{"id":1208,"type":"article-journal","title":"The attack of the psychometricians","container-title":"Psychometrika","page":"425-440","volume":"71","issue":"3","source":"PubMed Central","abstract":"This paper analyzes the theoretical, pragmatic, and substantive factors that have hampered the integration between psychology and psychometrics. Theoretical factors include the operationalist mode of thinking which is common throughout psychology, the dominance of classical test theory, and the use of “construct validity” as a catch-all category for a range of challenging psychometric problems. Pragmatic factors include the lack of interest in mathematically precise thinking in psychology, inadequate representation of psychometric modeling in major statistics programs, and insufficient mathematical training in the psychological curriculum. Substantive factors relate to the absence of psychological theories that are sufficiently strong to motivate the structure of psychometric models. Following the identification of these problems, a number of promising recent developments are discussed, and suggestions are made to further the integration of psychology and psychometrics.","DOI":"10.1007/s11336-006-1447-6","ISSN":"0033-3123","note":"PMID: 19946599\nPMCID: PMC2779444","journalAbbreviation":"Psychometrika","author":[{"family":"Borsboom","given":"Denny"}],"issued":{"date-parts":[["2006",9]]},"PMID":"19946599","PMCID":"PMC2779444"}}],"schema":""} (Borsboom 2006). Although these explanations may hold some truth, I argue that they do not provide the entire story. When we view the choice between types of measures as a choice of values and epistemic risk we see a larger, more comprehensive picture. For instance, RMT is more likely to produce false negatives and CTT is more likely to produce false positives; RMT is arguably a more precise measure, but what does precision mean in the context of the attributes and concepts COAs are often employed to measure, attributes such as depression, concepts such as physical functioning? I argue that what is at stake in different measuring contexts and how the attribute/concept is understood will affect whether precision is of overwhelming importance. It will thus contribute to the measurement methodology you seek to use.ParticipantsAlex Broadbent Professor of Philosophy and Executive Dean of Humanities, University of Johannesburg. abbroadbent@uj.ac.zaAlex Broadbent (PhD Cambridge 2007) is a philosopher of science with particular interests in philosophy of epidemiology, philosophy of medicine, and philosophy of law, connected by the philosophical themes of causation, explanation, and prediction. He is committed to finding philosophical problems in practical contexts, and to contributing something useful concerning them. He holds a P-rating from the National Research Foundation of South Africa (2013-2018) and is a member of the South African Young Academy of Sciences. He has published a number of articles in top international journals across three disciplines (philosophy, epidemiology, law). His first book, Philosophy of Epidemiology, was published by Palgrave Macmillan in 2013, and has been translated into Korean. His second book, Philosophy for Graduate Students: Metaphysics and Epistemology, was published by Routledge in 2016. He is currently working on his third book, Philosophy of Medicine, under contract with Oxford University Press.Recent representative publicationsVandenbroucke, J., Broadbent, A., and Pearce, N. Online first. Causality and Causal Inference in Epidemiology—the need for a pluralistic approach. International Journal of Epidemiology. doi:?10.1093/ije/dyv341 [open access]Broadbent, A. and Seung-Sik, H. 2016. Tobacco and Epidemiology in Korea: old tricks, new answers? Journal of Epidemiology and Community Health 70: 527-528.Broadbent, A. 2015. Causation and Prediction in Epidemiology: A Guide to the Methodological Revolution. Studies in History and Philosophy of Biological and Biomedical Sciences 54: 72-80.Broadbent, A. 2015. Risk Relativism and physical law. Journal of Epidemiology and Community Health 69: 92-94.Broadbent, A. 2014. Disease as a Theoretical Concept: the Case of HPV-itis. Studies in History and Philosophy of Biological and Biomedical Sciences 48: 250-257.Broadbent, A. 2013. Philosophy of Epidemiology. London: Palgrave Macmillan.Olaf DammannProfessor of Public Health and Community Medicine, Pediatrics, and Ophthalmology at Tufts University School of Medicine. Olaf.Dammann@tufts.eduOlaf Dammann, M.D. (U Hamburg, ’90), SM Epidemiology (Harvard, ’97) is Professor of Public Health and Community Medicine, Pediatrics, and Ophthalmology at Tufts University School of Medicine in Boston, USA. His research interests include the elucidation of risk factors for brain damage and retinopathy in preterm newborns, the theory of risk and causation in public health research, and the development of computational population models of disease occurrence. His current grant support is from the National Eye Institute and Tufts University School of Medicine Chairs’ Initiative. His bibliography lists 180 publications.Recent representative publicationsDammann O. Causality, mosaics, and the health sciences. Theor Med Bioeth. 2016 Apr;37(2):161-8. Escobar E, Durgham R, Dammann O, Stopka TJ. Agent-based computational model of the prevalence of gonococcal infections after the implementation of HIV pre-exposure prophylaxis guidelines. Online J Public Health Inform. 2015 Dec 30;7(3):e224. Fiorentino AR, Dammann O. Evidence, illness, and causation: an epidemiological perspective on the Russo-Williamson Thesis. Stud Hist Philos Biol Biomed Sci. 2015 Dec;54:1-9. Leviton A, Gressens P, Wolkenhauer O, Dammann O. Systems approach to the study of brain damage in the very preterm newborn. Front Syst Neurosci. 2015 Apr 14;9:58. Dammann O, Gray P, Gressens P, Wolkenhauer O, Leviton A. Systems Epidemiology: What's in a Name? Online J Public Health Inform. 2014 Dec 15;6(3):e198.Leah McClimans (Symposium Chair)Associate Professor, Department of Philosophy, University of South Carolina. MCCLIMAN@mailbox.sc.eduLeah McClimans (PhD LSE, 2007) is Associate Professor of Philosophy at the University of South Carolina. Her research interests focus on measurement in medical contexts, including the measurement of quality of life and the role of measurement in evidence based medicine. She is also interested in methodology of health-related quality of life measures, the art of questioning and the use of empirical outcomes in bioethical decisions. She is currently editing a collection on measurement in medicine.Recent representative publicationsLeah McClimans?&?Anne Slowther?(2016).?‘Moral Expertise in the Clinic: Lessons Learned From Medicine and Science.’?Journal of Medicine and Philosophy?41 (4):401-415.L. McClimans?(2013).?The Role of Measurement in Establishing Evidence.?Journal of Medicine and Philosophy?38 (5):520-538.Leah McClimans?&?John P. Browne?(2012).?Quality of Life is a Process Not an Outcome.?Theoretical Medicine and Bioethics?33 (4):279-292.Zinhle MncubeLecturer, Department of Philosophy, University of Johannesburg. zinhlem@uj.ac.za ?Zinhle’s research interests lie broadly in the philosophy of science, the philosophy of biology, and the philosophy of race. Her Honour's research was on the biological basis of race and her Master's dissertation was on heritability and genetic causation. Zinhle lectures undergraduate courses in metaphysics and epistemology. She is also an Iris Marion Young Scholar and a Cornelius Golightly fellow.?Recent representative publicationsMncube, Z. (2015). Are human races cladistic subspecies??South African Journal of Philosophy?34 (2): 163-174?.Benjamin SmartSenior Lecturer, Department of Philosophy, University of Johannesburg. bsmart@uj.ac.zaBenjamin Smart (Ph. D. University of Nottingham, 2012) is a philosopher of science and metaphysician, specialising in the philosophy of disease, epidemiology, causation, and laws of nature. He has recently published a monograph with Palgrave Macmillan entitled Concepts and Causes in the Philosophy of Disease, in which he demonstrates that a variety of analyses of causation, and numerous concepts of disease, are employed in the medical sciences. Recent representative publicationsSmart, B. The Philosophy of Disease, 2016, Basingstoke: Palgrave MacmillanSmart, B. and Thebault, K. 2015. ‘The Principle of Least Action Revisited’ Analysis 75(2): 386-395Smart, B. 2014. ‘On the Classification of Diseases’. Theoretical Medicine and Bioethics 35(40): 251-269Smart, B. 2013. ‘Is the Humean Defeated by Induction?’Philosophical Studies 162(2): 319-332Barker, S. and Smart, B. 2012. ‘The Ultimate Argument Against the Dispositional Monist Accounts of Laws’ Analysis 72(4): 714-723 ................
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