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Probabilistic Quantification Of Bias to Combine the Strengths Of Population-Based Register Data and Clinical Cohorts - Studying Mortality in Osteoarthritis

Turkiewicz, Aleksandra LU ; Nilsson, Peter M LU and Kiadaliri, Ali LU orcid (2020) In American Journal of Epidemiology 189(12). p.1590-1599
Abstract

We propose to combine population-based register data, with a nested clinical cohort to correct misclassification and unmeasured confounding through probabilistic quantification of bias. We illustrate this approach by estimating the association between knee osteoarthritis and mortality. We used the Swedish Population Register to include all persons resident in the Skåne region in 2008 and assessed if they had osteoarthritis using data from the Skåne Healthcare Register. We studied mortality until year 2017 by estimating hazard ratios (HR). We used data from the Malmö Osteoarthritis Study (MOA), a small cohort study from Skåne, to derive bias parameters for probabilistic quantification of bias, to correct the HR estimate for differential... (More)

We propose to combine population-based register data, with a nested clinical cohort to correct misclassification and unmeasured confounding through probabilistic quantification of bias. We illustrate this approach by estimating the association between knee osteoarthritis and mortality. We used the Swedish Population Register to include all persons resident in the Skåne region in 2008 and assessed if they had osteoarthritis using data from the Skåne Healthcare Register. We studied mortality until year 2017 by estimating hazard ratios (HR). We used data from the Malmö Osteoarthritis Study (MOA), a small cohort study from Skåne, to derive bias parameters for probabilistic quantification of bias, to correct the HR estimate for differential misclassification of the knee osteoarthritis diagnosis and confounding from unmeasured obesity. We included 292,000 persons in the Skåne population and 1419 from the MOA study. The adjusted association of knee osteoarthritis with all-cause mortality in the MOA sample was (HR [95% confidence interval]) 1.10 (0.80,1.52) and thus inconclusive. The naive association in the Skåne population was 0.95(0.93,0.98), while the bias-corrected estimate was 1.02 (0.59,1.52), suggesting high uncertainty in bias correction. Combining population-based register data with clinical cohorts provide more information than using either data source separately.

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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
American Journal of Epidemiology
volume
189
issue
12
pages
10 pages
publisher
Oxford University Press
external identifiers
  • pmid:32639513
  • scopus:85097113631
ISSN
0002-9262
DOI
10.1093/aje/kwaa134
language
English
LU publication?
yes
additional info
© The Author(s) 2020. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.
id
1e701566-88e5-44fd-b570-7f24170dcb57
date added to LUP
2020-07-10 12:02:58
date last changed
2024-05-29 16:23:18
@article{1e701566-88e5-44fd-b570-7f24170dcb57,
  abstract     = {{<p>We propose to combine population-based register data, with a nested clinical cohort to correct misclassification and unmeasured confounding through probabilistic quantification of bias. We illustrate this approach by estimating the association between knee osteoarthritis and mortality. We used the Swedish Population Register to include all persons resident in the Skåne region in 2008 and assessed if they had osteoarthritis using data from the Skåne Healthcare Register. We studied mortality until year 2017 by estimating hazard ratios (HR). We used data from the Malmö Osteoarthritis Study (MOA), a small cohort study from Skåne, to derive bias parameters for probabilistic quantification of bias, to correct the HR estimate for differential misclassification of the knee osteoarthritis diagnosis and confounding from unmeasured obesity. We included 292,000 persons in the Skåne population and 1419 from the MOA study. The adjusted association of knee osteoarthritis with all-cause mortality in the MOA sample was (HR [95% confidence interval]) 1.10 (0.80,1.52) and thus inconclusive. The naive association in the Skåne population was 0.95(0.93,0.98), while the bias-corrected estimate was 1.02 (0.59,1.52), suggesting high uncertainty in bias correction. Combining population-based register data with clinical cohorts provide more information than using either data source separately.</p>}},
  author       = {{Turkiewicz, Aleksandra and Nilsson, Peter M and Kiadaliri, Ali}},
  issn         = {{0002-9262}},
  language     = {{eng}},
  number       = {{12}},
  pages        = {{1590--1599}},
  publisher    = {{Oxford University Press}},
  series       = {{American Journal of Epidemiology}},
  title        = {{Probabilistic Quantification Of Bias to Combine the Strengths Of Population-Based Register Data and Clinical Cohorts - Studying Mortality in Osteoarthritis}},
  url          = {{http://dx.doi.org/10.1093/aje/kwaa134}},
  doi          = {{10.1093/aje/kwaa134}},
  volume       = {{189}},
  year         = {{2020}},
}