Abstract
Summary Two views exist of medical science: one emphasises discovery and explanation, the other emphasises evaluation of interventions. This essay analyses in what respects these views differ, and how they lead to opposite research hierarchies, with randomisation on top for evaluation and at bottom for discovery and explanation. The two views also differ strongly in their thinking about the role of prior specification of a research hypothesis. Hence, the essay explores the controversies surrounding subgroup analyses and multiplicity of analyses in observational research. This exploration leads to a rethinking of the universally accepted hierarchy of strength of study designs, which has the randomised trial on top: this hierarchy may be confounded by the prior odds of the research hypothesis. Finally, the strong opinions that are sometimes displayed in pitting the two types of medical science against each other may be explained by a difference in “loss function”: the difference in penalty for being wrong. A longer, more detailed version of this paper is found in supplementary Text S1.
Highlights
After the mutation was established, we looked at the data again
We found a few homozygotes for the mutation among the patients
Randomised controlled trials are rarely used for research to detect or to establish causes of disease, mainly because randomisation is most of the time impossible, but quite randomisation is most of the time not needed
Summary
Two views exist of medical science: one emphasises discovery and explanation, the other emphasises evaluation of interventions. From the perspective of the evaluative researcher, this method of discovery and explanation is dangerously biased: clinicians present case series out of the blue, epidemiologists do multiple analyses on existing data collected for completely different purposes, basic scientists repeat lab experiments with endless new variations, changing the hypothesis as well as the experiment continuously—until something fits. All these researchers always dream up perfect explanations. The preferred designs of researchers are case-control studies, or
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