Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Epidemiology. 2010 Mar;21(2):181-6. doi: 10.1097/EDE.0b013e3181c6f7d9.
Occupational cohort mortality studies rarely include information on smoking history. Consequently, smoking is often an unmeasured potential confounder in analyses of associations between occupational exposures and lung cancer. Several authors have recommended sensitivity analyses to assess confounding by smoking, which require speculation about the prevalence of smokers in different occupational exposure groups.
A method of adjustment for confounding by smoking is proposed in which the target parameter of interest (the log hazard ratio for lung cancer contrasting exposed to unexposed workers, adjusted for smoking) is approximated by the difference between the crude exposure-disease associations for lung cancer and chronic obstructive pulmonary disease. A polytomous logistic regression method is used to derive appropriate confidence intervals. The performance of this adjustment approach is assessed via direct calculations.
Under the scenarios considered, 90% or more of the bias due to confounding by smoking was removed via this adjustment in the absence of smoking data.
This approach to adjustment for confounding by smoking can be employed without explicitly positing the distribution of smoking with respect to occupational exposure. The approach is easily implemented in analyses of occupational cohort data and should facilitate quantitative assessments of bias due to unmeasured confounding by smoking in occupational studies of lung cancer.
职业队列死亡率研究很少包含吸烟史信息。因此，在分析职业暴露与肺癌之间的关联时，吸烟通常是一个未被测量的潜在混杂因素。几位作者建议进行敏感性分析以评估吸烟引起的混杂作用，这需要推测不同职业暴露组中吸烟者的比例。
提出了一种通过吸烟进行调整的方法，其中感兴趣的目标参数（暴露与未暴露工人相比肺癌的对数危险比，调整吸烟因素）通过肺癌和慢性阻塞性肺疾病的粗暴露-疾病关联之间的差异来近似。使用多分类逻辑回归方法得出适当的置信区间。通过直接计算评估这种调整方法的性能。
在所考虑的情况下，在没有吸烟数据的情况下，通过这种调整可以消除 90%或更多由于吸烟引起的混杂偏倚。
这种通过吸烟进行调整的方法可以在不明确假设吸烟与职业暴露关系的情况下使用。该方法易于在职业队列数据的分析中实施，并有助于在肺癌的职业研究中定量评估由于吸烟引起的未被测量的混杂偏倚。
