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Systematically missing confounders in individual participant data meta-analysis of observational cohort studies.

Fibrinogen Studies, Collaboration (author)
Rosengren, Annika, 1951 (author)
Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för akut och kardiovaskulär medicin,Institute of Medicine, Department of Emergeny and Cardiovascular Medicine
Wilhelmsen, Lars, 1932 (author)
Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för akut och kardiovaskulär medicin,Institute of Medicine, Department of Emergeny and Cardiovascular Medicine
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Lappas, Georg, 1962 (author)
Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för akut och kardiovaskulär medicin,Institute of Medicine, Department of Emergeny and Cardiovascular Medicine
Eriksson, Henry, 1946 (author)
Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för akut och kardiovaskulär medicin,Institute of Medicine, Department of Emergeny and Cardiovascular Medicine
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 (creator_code:org_t)
Wiley, 2009
2009
English.
In: Statistics in medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 28:8, s. 1218-37
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • One difficulty in performing meta-analyses of observational cohort studies is that the availability of confounders may vary between cohorts, so that some cohorts provide fully adjusted analyses while others only provide partially adjusted analyses. Commonly, analyses of the association between an exposure and disease either are restricted to cohorts with full confounder information, or use all cohorts but do not fully adjust for confounding. We propose using a bivariate random-effects meta-analysis model to use information from all available cohorts while still adjusting for all the potential confounders. Our method uses both the fully adjusted and the partially adjusted estimated effects in the cohorts with full confounder information, together with an estimate of their within-cohort correlation. The method is applied to estimate the association between fibrinogen level and coronary heart disease incidence using data from 154,012 participants in 31 cohorts

Keyword

Cohort Studies
Computer Simulation
Coronary Disease
metabolism
Data Interpretation
Statistical
Female
Fibrinogen
analysis
Humans
Male
Meta-Analysis as Topic
Models
Statistical

Publication and Content Type

ref (subject category)
art (subject category)

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