Journal
HUMAN REPRODUCTION
Volume 34, Issue 11, Pages 2093-2098Publisher
OXFORD UNIV PRESS
DOI: 10.1093/humrep/dez199
Keywords
infertility; RCT; IVF; statistics; evidence-based medicine; reproductive medicine; machine learning; artificial intelligence; epidemiology; add-on treatments
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Funding
- Wellcome Institutional Strategic Support Fund [204796/Z/16/Z]
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The conclusion of the Human Fertilisation and Embryology Authority that 'add-on' therapies in IVF are not supported by high-quality evidence has prompted new questions regarding the role of the randomized controlled trial (RCT) in evaluating infertility treatments. Critics argue that trials are cumbersome tools that provide irrelevant answers. Instead, they argue that greater emphasis should be placed on large observational databases, which can be analysed using powerful algorithms to determine which treatments work and for whom. Although the validity of these arguments rests upon the sciences of statistics and epidemiology, the discussion to date has largely been conducted without reference to these fields. We aim to remedy this omission, by evaluating the arguments against RCTs in IVF from a primarily methodological perspective. We suggest that, while criticism of the status quo is warranted, a retreat from RCTs is more likely to make things worse for patients and clinicians.
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