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It is a necessary condition that an object has four sides if it is true that it is a square; conversely, the object being a square is a sufficient condition for it to be true that an object has four sides. [4] Four distinct combinations of necessity and sufficiency are possible for a relationship of two conditions. A first condition may be ...
In the field of epidemiology, the causal mechanisms responsible for diseases can be understood using the causal pie model.This conceptual model was introduced by Ken Rothman to communicate how constellations of component causes can lead to a sufficient cause to lead to a condition of interest and that reflection on these sets could improve epidemiological study design.
A sufficient cause is a complete combination of component causes necessary for the disease to manifest. Diseases result from a chain of causally related events, starting from an initial event to the clinical appearance of the disease. No single antecedent event is sufficient on its own to cause the disease; each event is a part of the ...
Necessary condition analysis follows a step-by-step approach to identify necessary conditions. The key steps involved in conducting NCA are as follows: Formulation of a necessity hypothesis: The first step in NCA is to clearly define the theoretical expectation specifying the condition(s) that may be necessary for the outcome of interest.
A condition can be both necessary and sufficient. For example, at present, "today is the Fourth of July" is a necessary and sufficient condition for "today is Independence Day in the United States". Similarly, a necessary and sufficient condition for invertibility of a matrix M is that M has a nonzero determinant.
Causes may sometimes be distinguished into two types: necessary and sufficient. [19] A third type of causation, which requires neither necessity nor sufficiency, but which contributes to the effect, is called a "contributory cause". Necessary causes If x is a necessary cause of y, then the presence of y necessarily implies the prior occurrence ...
Epidemiological (and other observational) studies typically highlight associations between exposures and outcomes, rather than causation. While some consider this a limitation of observational research, epidemiological models of causation (e.g. Bradford Hill criteria) [7] contend that an entire body of evidence is needed before determining if an association is truly causal. [8]
The bills he studied were listings of numbers and causes of deaths published weekly. Graunt's analysis of causes of death is considered the beginning of the "theory of competing risks" which according to Daley and Gani [1] is "a theory that is now well established among modern epidemiologists".