Article References & Abstracts

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1.  AUTHOR Schaffner-K-F.
INSTITUTIONGeorge Washington University, Washington, DC 20052.
TITLE Clinical trials and causation: Bayesian perspectives.
SOURCE Stat-Med 1993 Aug, VOL: 12 (15-16), P: 1477-94; discussion 1495-9, ISSN: 0277-6715.
ABSTRACT In addition to the safety, it is essential to establish the causal efficacy of extant and new treatments, and well-designed clinical trials are thought by most to be the 'gold standard' to accomplish this. Contrary to most statisticians' and regulators' views, however, I will argue that the concept of causation involved in clinical trials is not all that clear. I discuss the manipulability approach to causation, interpreted counterfactually, which seems to fit causation as it is found in such sciences as physiology, but it has unclear relations to a concept of causation proposed by a number of epidemiologists. I characterize 'epidemiological causation' as probabilistic and formulated at a population level, and dependent on certain general criteria for causation as well as study-design considerations. I then attempt to clarify the connections between these concepts of causation and Cartwright's views on complexity and causality, a 'Bayesian' framework proposed by Rubin and further elaborated by Holland, and Glymour and his colleagues' recent directed graphical causal modelling approach. Author.

2.  AUTHOR Renton-A.
INSTITUTIONAcademic Department of Public Health, St Mary's Hospital Medical School, London.
TITLEEpidemiology and causation: a realist view.
SOURCEJ-Epidemiol-Community-Health 1994 Feb, VOL: 48 (1), P: 79-85, ISSN: 0143-005X.
ABSTRACTIn this paper the controversy over how to decide whether associations between factors and diseases are causal is placed within a description of the public health and scientific relevance of epidemiology. It is argued that the rise in popularity of the Popperian view of science, together with a perception of the aims of epidemiology as being to identify appropriate public health interventions, have focussed this debate on unresolved questions of inferential logic, leaving largely unanalysed the notions of causation and of disease at the ontological level. A realist ontology of causation of disease and pathogenesis is constructed within the framework of "scientific materialism", and is shown to provide a coherent basis from which to decide causes and to deal with problems of confounding and interaction in epidemiological research. It is argued that a realist analysis identifies a richer role for epidemiology as an integral part of an ontologically unified medical science. It is this unified medical science as a whole rather than epidemiological observation or experiment which decides causes and, in turn, provides a key element to the foundations of rational public health decision making. Author.


3.  AUTHOR Goldsmith-D-F.
INSTITUTIONCalifornia Public Health Foundation, Berkeley 94704-1103, USA.
TITLE Importance of causation for interpreting occupational epidemiology research: a case study of quartz and cancer.
SOURCE Occup-Med 1996 Jul-Sep, VOL: 11 (3), P: 433-49, ISSN: 0885-114X 76 Refs.
ABSTRACTOne of the most important roles for occupational epidemiology is to provide a scientific basis for assessing causation. This chapter discusses the criteria for causation considered by the U.S. Surgeon General, the International Agency for Research on Cancer, and others to place the evidence in historical context. As a case study, the criteria for judging the evidence for potential carcinogenicity of silica dust are examined. The importance of communication with workers and management about causal concerns from workplace exposures is also discussed. Author.


4.  AUTHOR Harber-P, Shusterman-D.
INSTITUTIONDepartment of Medicine, University of California, Los Angeles 90095-7027, USA.
TITLE Medical causation analysis heuristics (see comments).
SOURCE J-Occup-Environ-Med 1996 Jun, VOL: 38 (6), P: 577-86, ISSN: 1076-2752. CM Comment in: J-Occup-Environ-Med 1997 Mar; 39(3):194.
ABSTRACTMedical causation analysis determines whether or not a specific patient's illness is the result of a work site or an environmental exposure. In the past, this has been conducted implicitly with little analysis of the process per se. Our review suggests that there are several distinct heuristics that may be utilized; these include probability-based models, application of group-based data (epidemiology) to individuals, Bayesian analysis, a priori assumptions about which conclusions are better, and others. Some methods consider only work causes, whereas others explicitly consider alternative explanations. There are considerable differences among the methods in process, outcome, and fundamental assumptions. Formal assessment of the medical causation analysis process can provide insight and may ultimately lead to its standardization and improvement. Author.


5.  AUTHOR Rizzi-D-A, Pedersen-S-A.
INSTITUTIONUnit of Medical Philosophy and Clinical Theory, University of Copenhagen, Denmark.
TITLE Causality in medicine: towards a theory and terminology.
SOURCE Theor-Med 1992 Sep, VOL: 13 (3), P: 233-54, ISSN: 0167-9902.
ABSTRACTOne of the cornerstones of modern medicine is the search for what causes diseases to develop. A conception of multifactorial disease causes has emerged over the years. Theories of disease causation, however, have not quite been developed in accordance with this view. It is the purpose of this paper to provide a fundamental explication of aspects of causation relevant for discussing causes of disease. The first part of the analysis will discuss discrimination between singular and general causality. Singular causality, as in the specific patient, is a relation between a concrete sequence of causally linked events. General causation, e.g. as in disease etiology, means various categories of causal relations between event types. The paper introduces the concept of a reference case serving as a source for causal inference, reaching beyond the concept of general causality. The second part of the analysis provides exemplification of a theory of causation suitable for discussing singular causation. The chain of events that induce a disease state can be identified as effective causal complexes, each complex composed of non-redundant components, which separately contribute to the effect of the complex, without the individual component being necessary or sufficient in itself to produce the effect. In the third part of the analysis the theory is elaborated further. Causes, defined as non-redundant components, can furthermore be differentiated according to their avoidability, according to theories about human error or by the potential of eradication. Multifactorial models of disease creates a need for systematic approaches to causal factors. The paper proposes a taxonomical terminology that serves this purpose. Author.


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