Article Text

Download PDFPDF
Can’t see the wood for the trees: confounders, colliders and causal inference - a statistician’s approach
  1. Bin Huang1,2,
  2. Rhonda Szczesniak1,2
  1. 1 Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
  2. 2 Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA
  1. Correspondence to Dr Rhonda Szczesniak, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati OH 45229, USA; Rhonda.Szczesniak{at}

Statistics from

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

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 …

View Full Text


  • Contributors RS and BH conceived of the presented ideas and commentary. Both authors contributed to the content and approved the final manuscript.

  • Funding This work was supported in part by grants ME1408-19894 from the Patient Centered Outcomes Research Institute (BH), SZCZES18Y7 from the Cystic Fibrosis Foundation (BH) and NIH/NHLBI grant K25 HL125954 (RS).

  • Competing interests RS serves as a statistical editor on the Thorax Editorial Board.

  • Provenance and peer review Commissioned; externally peer reviewed.

  • Correction notice This article has been corrected since it was published. Major changes have been made to the text in the body of the article and Table 1.

  • Patient consent for publication Not required.

Linked Articles