Reader's guide to critical appraisal of cohort studies: 2. Assessing potential for confoundingBMJ 2005; 330 doi: http://dx.doi.org/10.1136/bmj.330.7497.960 (Published 21 April 2005) Cite this as: BMJ 2005;330:960
- Muhammad Mamdani, senior scientist1,
- Kathy Sykora, senior biostatistician1,
- Ping Li, analyst1,
- Sharon-Lise T Normand, professor of health care policy (biostatistics)2,
- David L Streiner, professor3,
- Peter C Austin, senior scientist1,
- Paula A Rochon, senior scientist4,
- Geoffrey M Anderson, chair in health management strategies (firstname.lastname@example.org)5
- 1Institute for Clinical Evaluative Sciences, Toronto, ON Canada
- 2Department of Health Care Policy, Harvard Medical School, Boston, USA
- 3Department of Psychiatry, University of Toronto, ON, Canada
- 4Kunin-Lunenfeld Applied Research Unit, Baycrest Centre for Geriatric Care, Toronto, ON, Canada
- 5Department of Health Policy, Management and Evaluation, Faculty of Medicine, University of Toronto, Toronto, ON Canada
- Correspondence to: G M Anderson, Institute for Clinical Evaluative Sciences, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada
- Accepted 18 February 2005
Although confounding is an important problem of cohort studies, its effects can be minimised to enable valid comparison
In cohort studies, who does or does not receive an intervention is determined by practice patterns, personal choice, or policy decisions. This raises the possibility that the intervention and comparison groups may differ in characteristics that affect the study outcome, a problem called selection bias. If these characteristics have independent effects on the observed outcome in each group, they will create differences in outcomes between the groups apart from those related to the interventions being assessed. This effect is known as confounding.1 In the first paper in the series we dealt with the design and use of cohort studies and how to identify selection bias.2 This paper focuses on the definition and assessment of confounders.
What is a confounder?
For a characteristic to be a confounder in a particular study, it must meet two criteria.1 The first is that it must be related to the outcome in terms of prognosis or susceptibility. For example, in the study of the association between antipsychotic use and hip fracture that we considered in the first paper,2 age is known to be related to risk of hip fracture and therefore has the potential to be a confounder.
The second criterion that defines a confounder is that the distribution of the characteristic is different in the groups being compared. It can differ in terms of either the mean or the degree of variation or variability in that characteristic. For example, for age to be a confounder in a cohort study, either the average age or the variation in the age in the groups being compared would have to be different. Assessing variation as well as average values is important because groups can have the same average value …
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