qbaconfound: A flexible Monte Carlo probabilistic bias analysis for unmeasured confounding

BackgroundUnmeasured confounding is a persistent concern in observational studies. We can quantitatively assess the impact of unmeasured confounding using a quantitative bias analysis (QBA). A QBA specifies the relationship between the unmeasured confounder(s), U, and study data via its bias parameters. There are two broad classes of QBA methods: deterministic and probabilistic. We focus on a probabilistic QBA which incorporates external information about U via prior distribution(s) placed on these bias parameters and can be implemented as a Bayesian QBA or a Monte Carlo QBA. A Bayesian QBA combines the prior distribution(s) with the datas likelihood function whilst a Monte Carlo QBA samples the bias parameters directly from their prior distributions. Software implementations of probabilistic QBAs to unmeasured confounding are scarce and mainly limited to unadjusted analyses of a binary exposure and outcome. One exception is R package unmconf (Hebdon et al 2024, BMC Med. Res. Methodol., https://doi.org/10.1186/s12874-024-02322-2) which implements a Bayesian QBA, applicable when the analysis is a generalised linear model (GLM). However, for a study with q measured confounders and a single U, unmconf requires information on at least 3+ q bias parameters, which is burdensome when q >1 and validation data are unavailable. AimWe propose a flexible Monte Carlo QBA where the number of bias parameters is independent of the number of measured confounders. It is applicable to a GLM or survival proportional hazards model, with binary, continuous, or categorical exposure and measured confounders, and one or multiple ([≥] 2) binary or continuous unmeasured confounders. MethodsVia simulations, we evaluated our Monte Carlo QBA for different analyses (e.g., varying the regression model, type of variables for the exposure and unmeasured confounder), and different levels of dependency between the measured and unmeasured confounders. Also, using our proposed bias model, we compare a Monte Carlo implementation to a fully Bayesian implementation when the analysis is a linear or logistic regression. We repeat the simulation study for prior distributions with different levels of informativeness. ResultsIgnoring U resulted in substantially biased estimates with substantial confidence interval undercoverage (e.g., 57%). Our Monte Carlo QBA (with informative priors) resulted in unbiased (or minimally biased) point estimates and interval estimates with close to nominal coverage. For binary U, levels of bias were marginally higher when U was strongly correlated with the measured confounders. The performances of the Monte Carlo and Bayesian implementations were comparable. ConclusionWe have proposed a flexible probabilistic QBA for unmeasured confounding which is applicable for a wide range of regression-based analyses. We have minimised the burden placed on the user by limiting the number of bias parameters and avoiding the need for specialist knowledge about Bayesian inference or Bayesian software. Our proposed Monte Carlo QBA will be implemented as Stata command and R package, qbaconfound.