2010 年 31 巻 2 号 p. 63-76
Epidemiologic findings by conventional statistical methods reflect uncertainty due to random error but omit uncertainty due to biases, such as unmeasured confounding, selection bias, and misclassification error. One approach for addressing this problem is to perform sensitivity analyses. We used MCSA (Monte Carlo sensitivity analysis) to analyze data from a large population-based cohort study, Japan Arteriosclerosis Longitudinal Study-Existing Cohorts Combine. The effects of the blood pressure on arteriosclerotic disease were examined among 21,949 subjects accounting for both misclassification of exposure and unmeasured confounding. We used a Poisson regression model to estimate the gender-specific incidence rate ratio (IRR) of each blood pressure category adjusted for several measured risk factors. The prior information on the misclassified blood pressure and the unmeasured diabetes mellitus history was obtained from sub-cohort members. Sequential correction of two biases by the MCSA led to large decrease of IRR among pre-hypertensive men (IRR = 1.79 [95% limits = 0.22−3.78]) and women (1.15 [0.28−2.25]), and large increase of IRR among stage 2 hypertensive men (7.24 [3.50−11.2]) and women (4.12 [2.14−6.89]). Our expanded MCSA provides valuable approach for bias analysis, which makes explicit and quantifies sources of uncertainty.