American journal of epidemiology
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Conventional confidence intervals reflect uncertainty due to random error but omit uncertainty due to biases, such as confounding, selection bias, and measurement error. Such uncertainty can be quantified, especially if the investigator has some idea of the amount of such bias. A traditional sensitivity analysis produces one or more point estimates for the exposure effect hypothetically adjusted for bias, but it does not provide a range of effect measures given the likely range of bias. ⋯ Monte Carlo sensitivity analysis, adjusting for possible confounding by smoking, led to an adjusted standardized mortality ratio of 1.43 (95% Monte Carlo limits: 1.15, 1.78). Bayesian results were similar (95% posterior limits: 1.13, 1.84). The authors believe that these types of analyses, which make explicit and quantify sources of uncertainty, should be more widely adopted by epidemiologists.