The current deconstruction of paradoxes: one sign of the ongoing methodological “revolution”

Miquel Porta, Paolo Vineis, Francisco Bolúmar

Research output: Contribution to journalArticleResearchpeer-review

20 Citations (Scopus)

Abstract

© 2015, Springer Science+Business Media Dordrecht. The current deconstruction of paradoxes is one among several signs that a profound renewal of methods for clinical and epidemiological research is taking place; perhaps for some basic life sciences as well. The new methodological approaches have already deconstructed and explained long puzzling apparent paradoxes, including the (non-existent) benefits of obesity in diabetics, or of smoking in low birth weight. Achievements of the new methods also comprise the elucidation of the causal structure of long-disputed and highly complex questions, as Berkson’s bias and Simpson’s paradox, and clarifying reasons for deep controversies, as those on estrogens and endometrial cancer, or on adverse effects of hormone replacement therapy. These are signs that the new methods can go deeper and beyond the methods in current use. A major example of a highly relevant idea is: when we condition on a common effect of a pair of variables, then a spurious association between such pair is likely. The implications of these ideas are potentially vast. A substantial number of apparent paradoxes may simply be the result of collider biases, a source of selection bias that is common not just in epidemiologic research, but in many types of research in the health, life, and social sciences. The new approaches develop a new framework of concepts and methods, as collider, instrumental variables, d-separation, backdoor path and, notably, Directed Acyclic Graphs (DAGs). The current theoretical and methodological renewal—or, perhaps, “revolution”—may be changing deeply how clinical and epidemiological research is conceived and performed, how we assess the validity and relevance of findings, and how causal inferences are made. Clinical and basic researchers, among others, should get acquainted with DAGs and related concepts.
Original languageEnglish
Pages (from-to)1079-1087
JournalEuropean Journal of Epidemiology
Volume30
Issue number10
DOIs
Publication statusPublished - 1 Oct 2015

Keywords

  • Causal inference
  • Clinical research
  • Collider
  • Directed Acyclic Graphs (DAGs)
  • Methods
  • Paradox

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