Utilization of Positive and Negative Controls to Examine Comorbid Associations in Observational Database Studies

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Abstract

Background:

Opportunities to leverage observational data for precision medicine research are hampered by underlying sources of bias and paucity of methods to handle resulting uncertainty. We outline an approach to account for bias in identifying comorbid associations between 2 rare genetic disorders and type 2 diabetes (T2D) by applying a positive and negative control disease paradigm.

Research Design:

Association between 10 common and 2 rare genetic disorders [Hereditary Fructose Intolerance (HFI) and α-1 antitrypsin deficiency] and T2D was compared with the association between T2D and 7 negative control diseases with no established relationship with T2D in 4 observational databases. Negative controls were used to estimate how much bias and variance existed in datasets when no effect should be observed.

Results:

Unadjusted association for common and rare genetic disorders and T2D was positive and variable in magnitude and distribution in all 4 databases. However, association between negative controls and T2D was 200% greater than expected indicating the magnitude and confidence intervals for comorbid associations are sensitive to systematic bias. A meta-analysis using this method demonstrated a significant association between HFI and T2D but not for α-1 antitrypsin deficiency.

Conclusions:

For observational studies, when covariate data are limited or ambiguous, positive and negative controls provide a method to account for the broadest level of systematic bias, heterogeneity, and uncertainty. This provides greater confidence in assessing associations between diseases and comorbidities. Using this approach we were able to demonstrate an association between HFI and T2D. Leveraging real-world databases is a promising approach to identify and corroborate potential targets for precision medicine therapies.

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