The deconstruction of paradoxes | OUPblog

The deconstruction of paradoxes in epidemiology | OUPblog

The deconstruction of paradoxes and probably a "revolution" in methods of clinical and epidemiological research  http://t.co/vTxbGuDY6K

 

Eur J Epidemiol. 2015 Oct; 30 (10): 1079-1087.

The current deconstruction of paradoxes: one sign of the ongoing methodological "revolution".

Porta M^1,2,3, Vineis P^4,5, Bolúmar F^6,7,8

• ¹Hospital del Mar Institute of Medical Research (IMIM), Universitat Autònoma de Barcelona, Carrer del Dr. Aiguader, 88, 08003, Barcelona, Catalonia, Spain. mporta@imim.es.
• ²Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA. mporta@imim.es.
• ³CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain. mporta@imim.es.
• ⁴MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College, London, UK.
• ⁵Human Genetics Foundation (HuGeF), Turin, Italy.
• ⁶CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
• ⁷Faculty of Medicine, University of Alcalá, Alcalá de Henares, Madrid, Spain.
• ⁸City University of New York School of Public Health at Hunter College, New York, NY, USA.

Abstract

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.

KEYWORDS: Causal inference; Clinical research; Collider; Directed Acyclic Graphs (DAGs); Methods; Paradox

PMID: 26164615.  doi: 10.1007/s10654-015-0068-8.  Epub 2015 Jul 12.

http://www.ncbi.nlm.nih.gov/pubmed/26164615
http://link.springer.com/article/10.1007%2Fs10654-015-0068-8