scholarly journals A Method to Visualize and Adjust for Selection Bias in Prevalent Cohort Studies

2011 ◽  
Vol 174 (8) ◽  
pp. 969-976 ◽  
Author(s):  
A. Torner ◽  
P. Dickman ◽  
A.-S. Duberg ◽  
S. Kristinsson ◽  
O. Landgren ◽  
...  
Keyword(s):  
Cohort Studies ◽  
Selection Bias ◽  
Prevalent Cohort

What you don’t see can hurt you: Selection bias in cohort studies—are environmental studies at particular risk?

2013 ◽  
Vol 2013 (1) ◽  
pp. 4606
Author(s):  
Marc G Weisskopf ◽  
Howard Hu ◽  
David Sparrow ◽  
Melinda Power
Keyword(s):  
Cohort Studies ◽  
Selection Bias ◽  
Environmental Studies

scholarly journals Measurement Error for Age of Onset in Prevalent Cohort Studies

2014 ◽  
Vol 05 (11) ◽  
pp. 1672-1683 ◽  
Author(s):  
Yujie Zhong ◽  
Richard J. Cook
Keyword(s):  
Measurement Error ◽  
Cohort Studies ◽  
Age Of Onset ◽  
Prevalent Cohort

scholarly journals Collider Scope: When selection bias can substantially influence observed associations

2016 ◽  
Author(s):  
Marcus R. Munafò ◽  
Kate Tilling ◽  
Amy E. Taylor ◽  
David M. Evans ◽  
George Davey Smith
Keyword(s):  
Cohort Studies ◽  
Selection Bias ◽  
Large Scale ◽  
Sensitivity Analyses ◽  
Birth Cohort Study ◽  
Strong Impact ◽  
Cross Sectional ◽  
Polygenic Scores ◽  
Biased Estimates ◽  
Collider Bias

AbstractLarge-scale cross-sectional and cohort studies have transformed our understanding of the genetic and environmental determinants of health outcomes. However, the representativeness of these samples may be limited – either through selection into studies, or by attrition from studies over time. Here we explore the potential impact of this selection bias on results obtained from these studies, from the perspective that this amounts to conditioning on a collider (i.e., a form of collider bias). While it is acknowledged that selection bias will have a strong effect on representativeness and prevalence estimates, it is often assumed that it should not have a strong impact on estimates of associations. We argue that because selection can induce collider bias (which occurs when two variables independently influence a third variable, and that third variable is conditioned upon), selection can lead to substantially biased estimates of associations. In particular, selection related to phenotypes can bias associations with genetic variants associated with those phenotypes. In simulations, we show that even modest influences on selection into, or attrition from, a study can generate biased and potentially misleading estimates of both phenotypic and genotypic associations. Our results highlight the value of knowing which population your study sample is representative of. If the factors influencing selection and attrition are known, they can be adjusted for. For example, having DNA available on most participants in a birth cohort study offers the possibility of investigating the extent to which polygenic scores predict subsequent participation, which in turn would enable sensitivity analyses of the extent to which bias might distort estimates.Key MessagesSelection bias (including selective attrition) may limit the representativeness of large-scale cross-sectional and cohort studies.This selection bias may induce collider bias (which occurs when two variables independently influence a third variable, and that variable is conditioned upon).This may lead to substantially biased estimates of associations, including of genetic associations, even when selection / attrition is relatively modest.

scholarly journals Selection Bias Due to Loss to Follow Up in Cohort Studies

Epidemiology ◽  
2016 ◽  
Vol 27 (1) ◽  
pp. 91-97 ◽  
Author(s):  
Chanelle J. Howe ◽  
Stephen R. Cole ◽  
Bryan Lau ◽  
Sonia Napravnik ◽  
Joseph J. Eron
Keyword(s):  
Cohort Studies ◽  
Selection Bias ◽  
Loss To Follow Up
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