BMJ  2005;330:1021-1023 (30 April), doi:10.1136/bmj.330.7498.1021

Education and debate

Readers guide to critical appraisal of cohort studies: 3. Analytical strategies to reduce confounding

¹ Department of Health Care Policy, Harvard Medical School, Boston, MA, USA, ² Institute for Clinical Evaluative Sciences, Toronto, ON, Canada, ³ Kunin-Lunenfeld Applied Research Unit, Baycrest Centre for Geriatric Care, Toronto, ⁴ Department of Health Policy, Management and Evaluation, Faculty of Medicine, University of Toronto, Toronto

Analytical strategies can help deal with potential confounding but readers need to know which strategy is appropriate

The first 150 words of the full text of this article appear below.

Introduction

The previous articles in this series^{1 2} argued that cohort studies are exposed to selection bias and confounding, and that critical appraisal requires a careful assessment of the study design and the identification of potential confounders. This article describes two analytical strategies—regression and stratification—that can be used to assess and reduce confounding. Some cohort studies match individual participants in the intervention and comparison groups on the basis of confounders, but because matching may be viewed as a special case of stratification we have not discussed it specifically and details are available elsewhere.^{3 4} Neither of these techniques can eliminate bias related to unmeasured or unknown confounders. Furthermore, both have their own assumptions, advantages, and limitations.

Regression

Regression uses the data to estimate how confounders are related to the outcome and produces an adjusted estimate of the intervention effect. It is the most commonly used method for reducing confounding in cohort studies. The outcome . . . [Full text of this article]

-->

Stratification

Assessing analytical strategies

Concluding remarks

CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    StumbleUpon    Technorati    What's this?

Relevant Articles

Reader's guide to critical appraisal of cohort studies: 2. Assessing potential for confounding

Muhammad Mamdani, Kathy Sykora, Ping Li, Sharon-Lise T Normand, David L Streiner, Peter C Austin, Paula A Rochon, and Geoffrey M Anderson
BMJ 2005 330: 960-962. [Extract] [Full Text] [PDF]

Reader's guide to critical appraisal of cohort studies: 1. Role and design

Paula A Rochon, Jerry H Gurwitz, Kathy Sykora, Muhammad Mamdani, David L Streiner, Susan Garfinkel, Sharon-Lise T Normand, and Geoffrey M Anderson
BMJ 2005 330: 895-897. [Extract] [Full Text] [PDF]

This article has been cited by other articles:

• Levine, M. N., Julian, J. A. (2008). Registries That Show Efficacy: Good, but Not Good Enough. JCO 26: 5316-5319 [Full text]  
• Normand, S.-L. T. (2008). Some Old and Some New Statistical Tools for Outcomes Research. Circulation 118: 872-884 [Full text]  
• Trojano, M. (2007). Is it time to use observational data to estimate treatment effectiveness in multiple sclerosis?. Neurology 69: 1478-1479 [Full text]  
• Kelly, C., Arnold, R., Galloway, Y., O'Hallahan, J. (2007). A Prospective Study of the Effectiveness of the New Zealand Meningococcal B Vaccine. Am J Epidemiol 166: 817-823 [Abstract] [Full text]  
• Gill, S. S., Bronskill, S. E., Normand, S.-L. T., Anderson, G. M., Sykora, K., Lam, K., Bell, C. M., Lee, P. E., Fischer, H. D., Herrmann, N., Gurwitz, J. H., Rochon, P. A. (2007). Antipsychotic Drug Use and Mortality in Older Adults with Dementia. ANN INTERN MED 146: 775-786 [Abstract] [Full text]  

Online poll

Find out more >>