Thaddäus Tönnies1, Sabine Kahl, Oliver Kuss. 1. German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Institue for Biometrics and Epidemiology, Düsseldorf; German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Insitute for Clinical Diabetology, Düsseldorf; German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg; Division of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf; Centre for Health and Society, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf.
Abstract
BACKGROUND: The findings of observational studies can be distorted by a number of factors. So-called confounders are well known, but distortion by collider bias (CB) has received little attention in medical research to date. The goal of this article is to present the principle of CB, and measures that can be taken to avoid it, by way of a few illustrative examples. METHODS: The findings of a selective review of the literature on CB are explained with illustrative examples. RESULTS: The simplest case of a collider variable is one that is caused by at least two other variables. An example of CB is the observation that, among persons with diabetes, obesity is associated with lower mortality, even though it is associated with higher mortality in the general population. The false protective association between obesity and mortality arises from the restriction of the study population to persons with diabetes. CONCLUSION: CB is a distortion that arises through restriction on or stratification by a collider variable, or through statistical adjustment for a collider variable in a regression model. CB can arise in many ways. The graphic representation of causal structures helps to identify potential sources of CB. It is important to distinguish confounders from colliders, as methods that serve to correct for confounding can themselves cause bias when applied to colliders. There is no generally applicable method for correcting CB.
BACKGROUND: The findings of observational studies can be distorted by a number of factors. So-called confounders are well known, but distortion by collider bias (CB) has received little attention in medical research to date. The goal of this article is to present the principle of CB, and measures that can be taken to avoid it, by way of a few illustrative examples. METHODS: The findings of a selective review of the literature on CB are explained with illustrative examples. RESULTS: The simplest case of a collider variable is one that is caused by at least two other variables. An example of CB is the observation that, among persons with diabetes, obesity is associated with lower mortality, even though it is associated with higher mortality in the general population. The false protective association between obesity and mortality arises from the restriction of the study population to persons with diabetes. CONCLUSION: CB is a distortion that arises through restriction on or stratification by a collider variable, or through statistical adjustment for a collider variable in a regression model. CB can arise in many ways. The graphic representation of causal structures helps to identify potential sources of CB. It is important to distinguish confounders from colliders, as methods that serve to correct for confounding can themselves cause bias when applied to colliders. There is no generally applicable method for correcting CB.
Authors: Emilio A L Gianicolo; Martin Eichler; Oliver Muensterer; Konstantin Strauch; Maria Blettner Journal: Dtsch Arztebl Int Date: 2020-02-14 Impact factor: 5.594