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In a recent publication, Lederer and 47 other editors, including contributors from Thorax, put forth firm guidelines to improve the rigour and reproducibility of causal inference studies using observational data in respiratory research.1 The authors highlight a continued reliance on antiquated methods and use of inappropriate procedures to account for confounding as reasons for a call to action. So, why and how must we carefully control for confounding? The journal includes an editorial offering the clinician perspective.2 We complement this editorial from a statistical standpoint.
Concern over confounding has persisted throughout the history of observational studies. In clinical effectiveness studies, unadjusted confounding can lead to ‘unfair’ comparisons. A well-known instance of confounding is referred to as Simpson’s paradox.3 Let’s take a look at a hypothetical example presented in table 1, comparing the performance of two treatments (labelled A and B) and their success rates for treating pulmonary exacerbations in cystic fibrosis patients. Here, successful treatment for each patient is defined as returning to a pre-exacerbation level of forced expiratory volume in FEV1% predicted. If you were only given the average rate of success between A and B, as shown by the overall success rates, it may lead you to believe that treatment A is doing a better job at combatting pulmonary exacerbations than treatment B. However, if you were provided the breakdown of the patient’s condition (mild or severe), it becomes quite obvious that treatment B is consistently out-performing treatment A, regardless of condition. So, what’s going on? Confounding is playing a trick on us! The underlying disease severity confounds the relationship between treatments and their success rates. Treatment B, which is believed to be more effective, has a 80% chance being prescribed to severe patients and successfully helps 62.5% of them. Meanwhile, only 20% of …
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