Figure 1.

Missing data in electronic anesthesia records lead to a trade-off between selection bias and confounding bias in research using large databases like NACOR. Inferences based on the analysis of the complete dataset or the larger enriched datasets will more likely be generalizable, but lack of control for confounders (age and gender) may lead to bias. As we control for confounding with increasingly complex models, adding more variables, the dataset becomes smaller due to missing data: We can only include records with complete data in the analysis. Any increase in validity with advanced modelling may come at the expense of generalizability due to selection bias: The few institutions uploading all variables of interest may not represent typical anesthesia practice.