A flexible Monte Carlo quantitative bias analysis for unmeasured confounding: The qbaconfound command

Rachael Hughes, Emily Kawabata, Chin Yang Shapland, Tom Palmer, David Carslake and Kate Tilling
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Rachael Hughes: University of Bristol
Emily Kawabata: University of Bristol
Chin Yang Shapland: University of Bristol
Tom Palmer: University of Bristol
David Carslake: University of Bristol
Kate Tilling: University of Bristol

UK Stata Conference 2026 from Stata Users Group

Abstract: Unmeasured confounding is a persistent concern in observational studies. We can quantitatively assess the impact of unmeasured confounding using a quantitative bias analysis (QBA). A probabilistic QBA incorporates external information about the unmeasured confounders via prior distributions for certain parameters (known as bias parameters) that specify the relationship between the unmeasured confounders and the study data. It can be implemented as a Bayesian or Monte Carlo QBA. A Bayesian QBA combines the prior distribution with the data's likelihood function, while a Monte Carlo QBA samples the bias parameters directly from its prior distributions. Software implementations of probabilistic QBAs to unmeasured confounding are scarce and mainly limited to unadjusted analyses of binary variables. We present a new Stata command, qbaconfound, that implements our flexible Monte Carlo QBA. It is applicable to a generalized linear model or survival proportional hazards model and allows for (i) binary, continuous, or categorical exposure and measured confounders; (ii) correlation between; and (iii) one or multiple binary or continuous. To minimize the number of bias parameters, our proposed Monte Carlo QBA does not model directly but instead models the part not explained. (For more information, see Kawabata et. al preprint https://doi.org/10.1101/2025.08.12.25333217.) We illustrate qbaconfound with an example analysis from the National Health and Nutrition Examination Survey study.

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