AI Article Synopsis
- Researchers worry about unmeasured variables potentially skewing results in observational epidemiology, and indirect adjustment methods, like the "Axelson adjustment," can help assess their impact on relative risk (RR) when those variables are binary.
- The study introduces formulas for indirectly adjusting observed RRs in situations with multiple categories and proposes a method to identify necessary characteristics of confounders that could fully explain the observed association.
- Examples from pediatric CT scans and studies on nuclear workers and smoking illustrate how external data can help determine if unmeasured confounding is a concern.
Article Abstract
Purpose: With observational epidemiologic studies, there is often concern that an unmeasured variable might confound an observed association. Investigators can assess the impact from such unmeasured variables on an observed relative risk (RR) by utilizing externally sourced information and applying an indirect adjustment procedure, for example, the "Axelson adjustment." Although simple and easy to use, this approach applies to exposure and confounder variables that are binary. Other approaches eschew specific values and provide only bounds on the potential bias.
Methods: For both multiplicative and additive RR models, we present formulae for indirect adjustment of observed RRs for unmeasured potential confounding variables when there are multiple categories. In addition, we suggest an alternative strategy to identify the characteristics that the confounder must have to explain fully the observed association.
Results And Conclusions: We provide examples involving studies of pediatric computer tomography scanning and leukemia and nuclear radiation workers and smoking to demonstrate that with externally sourced information, an investigator can assess whether confounding from unmeasured factors is likely to occur.
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| http://dx.doi.org/10.1016/j.annepidem.2018.09.003 | DOI Listing |
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