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

Abstract

Abstract We propose combining population-based register data with a nested clinical cohort to correct misclassification and unmeasured confounding through probabilistic quantification of bias. We have illustrated 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 whether they had osteoarthritis using data from the Skåne Healthcare Register. We studied mortality through year 2017 by estimating hazard ratios. 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 hazard ratio estimate for differential misclassification of the knee osteoarthritis diagnosis and confounding from unmeasured obesity. We included 292,000 persons in the Skåne population and 1,419 from the MOA study. The adjusted association of knee osteoarthritis with all-cause mortality in the MOA sample had a hazard ratio of 1.10 (95% confidence interval (CI): 0.80, 1.52) and was thus inconclusive. The naive association in the Skåne population had a hazard ratio of 0.95 (95% CI: 0.93, 0.98), while the bias-corrected estimate was 1.02 (95% CI: 0.59, 1.52), suggesting high uncertainty in bias correction. Combining population-based register data with clinical cohorts provides more information than using either data source separately.