Monte Carlo sensitivity analysis and Bayesian analysis of smoking as an unmeasured confounder in a study of silica and lung cancer.

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Causal Inference through a Witness Protection Program (2014, background): “For instance, Steenland and Greenland (2004) discuss how empirical priors on smoking habits among blue-collar workers were used in their epidemiological question: the causal effect of the occupational harzard of silica exposure on lung cancer incidence among industrial sand workers.”
Multiple‐bias modelling for analysis of observational data (2005, methodology): “Yet conventional analyses of observational data in the health sciences (as reviewed, for example, in Rothman and Greenland (1998), chapters 12–17) can be characterized by a two-step process that quantifies only random error— (a) employ frequentist statistical methods based on the following…”
Good practices for quantitative bias analysis. (2014, methodology): “Several have provided code so that future researchers could implement their analyses as well.(15,27,68,71) When possible, we encourage authors to adopt code that has been previously developed, because it should help to identify and reduce coding errors.”