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

doi:10.1136/bmj.330.7498.1021

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Sharon-Lise T Normand, professor of health care policy (biostatistics)1,
Kathy Sykora, senior biostatistician2,
Ping Li, analyst2,
Muhammad Mamdani, senior scientist2,
Paula A Rochon, senior scientist3,
Geoffrey M Anderson, chair in health management strategies (geoff.anderson@utoronto.ca)4

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

Correspondence to: G M Anderson, Institute for Clinical Evaluative Sciences, 2075 Bayview Avenue, Toronto, ON, Canada M4N 3M5

Accepted 18 February 2005

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

Introduction

The previous articles in this series1 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 of interest is the dependent variable, and the measures of baseline characteristics (such as age and sex) and the intervention are independent variables. The choice of method of regression analysis (linear, logistic, proportional hazards, etc) is dictated by the type of dependent variable. For example, if the outcome is binary (such as occurrence of hip fracture), a logistic regression model would be appropriate; in contrast, if the outcome is time to an event (such as time to hip fracture) a proportional hazards model is appropriate.

Stratification of the cohort helps minimise bias

Credit: SAMBA PHOTO/PHOTONICA

Regression analyses estimate the association of each independent variable with the dependent variable after adjusting for the effects of all the other variables. Because the estimated association between the intervention and outcome variables adjusts …