[Is the age of causality over?].
For the assessment of diagnostic and therapeutic interventions a sound scientific base has been developed during the last twenty years. Under the headline of Evidence-based Medicine nowadays a comprehensive set of tools is offered which can be used to assess the benefit and the risk of medical interventions. The overarching rule which evolved for the grading of evidence from studies is to maximize the protection against bias. Despite this coherent approach, there is still controversy that is regularly mainly sparked by the dominant position of randomized controlled trials. Observational studies and registries are deemed to be more relevant because they provide results that are produced under "everyday conditions". These controversial discussions often show a lack of orientation, as they do without the explicit naming of scientific criteria for the evaluation and to a large extent rely on common sense. That the latter may not be a good guide for assessments in the medical field is known from numerous studies. For unbiased assessments the rigorous use of basic scientific principles is the only way. To express doubt and question these principles requires a scientific basis itself. The alternative is to move away from the established scientific foundation. The path to a "new" scientific paradigm is currently dominated by a discussion under the buzzword Big Data. Defined by the three V's of Variety, Velocity and Volume, a potential of the unlimited analysis of data is envisioned, for which there is currently no validation and whose logical foundations are extremely doubtful. The demand must be reaffirmed that instead of promises strict validation criteria be followed for the evaluation of all interventions in medicine, particularly in view of these developments.
Cochrane Deutschland, Freiburg, Deutschland. Electronic address: antes@cochrane.d.
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Antes
D015982:Bias; D019317:Evidence-Based Medicine; D005858:Germany; D006801:Humans
Evidence-based medicine; Evidenz-basierte Medizin; Good Scientific Practice; Kausalität; Risk-of-Bias; Validierung; causality; good scientific practice (GSP); risk-of-bias; scientific assessment; validation; wissenschaftliche Bewertung