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See companion article.1 The world cannot revolve and medicine cannot advance using randomised controlled trial (RCT) data alone. Increasing weight is, quite appropriately, being given to observational research. This is both in the context of answering important clinical questions and in the development of guidelines and policy. With that in mind, the quality of these observational studies is paramount, and quality needs to be appreciated at several levels, including methodology, interpretation and data transparency. There are plenty of examples of observational studies and RCTs failing to reach the same conclusions.2–4 Based on multifaculty input from 47 editors of the main respiratory, critical care and sleep journals, in a recent consensus statement in the Annals of the American Thoracic Society, Lederer and colleagues publish guidance on how to control for confounders and report results in observational studies.5
Investigation of the causal effect of an exposure (eg, risk factor) on a health outcome in observational studies is not straightforward. Despite a very rich epidemiological and statistical literature covering all issues and methods available to address them, this literature can be a difficult read for clinical researchers—and help from a methodologist is not always at hand. By simplifying complex statistical concepts using accessible language and simple graphical displays, articles like the review by Lederer and colleagues play an important role in making clinical researchers aware of the main …
Contributors JKQ wrote the first draft, both authors contributed to subsequent drafts.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Patient consent Not required.
Provenance and peer review Commissioned; internally peer reviewed.
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