Commentary: Epidemiologists have debated representativeness for more than 40 years—has the time come to move on?
Ellen A. Nohr,Jørn Olsen +1 more
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This article is published in International Journal of Epidemiology.The article was published on 2013-08-01 and is currently open access. It has received 63 citations till now. The article focuses on the topics: Imputation (statistics).read more
Citations
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Collider scope: when selection bias can substantially influence observed associations
TL;DR: In simulations, it is shown 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.
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Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis
G. David Batty,G. David Batty,Catharine R. Gale,Catharine R. Gale,Mika Kivimäki,Ian J. Deary,Steven Bell,Steven Bell,Steven Bell +8 more
TL;DR: Risk factor levels and mortality rates were typically more favourable in UK Biobank participants relative to the HSE-SHS consortium, and close agreement was seen between studies.
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Representativeness of the LifeLines Cohort Study
TL;DR: The results suggest that the LifeLines adult study population is broadly representative for the adult population of the north of the Netherlands, and indicate that the risk of selection bias is low and that risk estimates in LifeL Lines can be generalized to the general population.
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An integrated national mortality surveillance system for death registration and mortality surveillance, China.
Shiwei Liu,Xiaoling Wu,Alan D. Lopez,Lijun Wang,Yue Cai,Andrew Page,Peng Yin,Yunning Liu,Yichong Li,Jiangmei Liu,Jinling You,Maigeng Zhou +11 more
TL;DR: The development and operation of the new national mortality surveillance system is described, which is expected to yield representative provincial estimates of mortality in China for the first time.
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How to investigate and adjust for selection bias in cohort studies.
TL;DR: Methods to quantify selection bias are introduced together with analytical strategies to adjust for the bias including controlling for covariates associated with selection, inverse probability weighting and bias analysis and can be applied to other study designs such as case–control studies and surveys.
References
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Journal Article
The cement of the universe : a study of causation
TL;DR: In this paper, the authors study causality both as a concept and as it is 'in the objects' and offer new accounts of the logic of singular causal statements, the form of causal regularities, the detection of causal relationships, the asymmetry of cause and effect, and necessary connection, and relate causation to functional and statistical laws and to teleology.
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Why representativeness should be avoided
TL;DR: The essence of knowledge is generalisation; that rubbing wood in a certain way can produce fire is a knowledge derived by generalisation from individual experiences.
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Does Low Participation in Cohort Studies Induce Bias
TL;DR: The results are reassuring for studies based on the Danish cohort and similar cohorts of pregnant women and the methodology used to compute confidence intervals for the relative odds ratios performed well in the scenarios considered.
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Loss to follow-up in cohort studies: bias in estimates of socioeconomic inequalities.
TL;DR: Considerable attrition from cohort studies may result in biased estimates of socioeconomic inequalities, and the degree of bias may worsen as participation rates decrease, but qualitative conclusions about the direction and approximate magnitude of inequalities did not change among most of the authors' examples.
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Estimating bias from loss to follow-up in the danish national birth cohort
TL;DR: The presence and magnitude of bias due to loss to follow-up depended on the nature of the factors or outcomes examined, with the most pronounced contribution in this study coming from maternal smoking.
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