Modeling lung cancer risk in case-control studies using a new dose metric of smoking
Modeling lung cancer risk in case-control studies using a new dose metric of smoking
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DOI：
10.1158/1055-9965.epi-04-0393
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发表时间：
2005-10-01
影响因子：
3.8
通讯作者：
Christiani, DC
中科院分区：
文献类型：
--
作者：
Thurston, SW;Liu, G;Christiani, DC
人们已经采取了许多方法来调整癌症风险建模中的吸烟情况。在病例对照研究中，这些指标通常是任意使用的，而不是基于研究背景下指标的属性。根据基础研究设计、假设和基础人群，不同的指标可能被认为是最合适的。我们提出了评估不同吸烟指标的方法。我们研究了一个新指标“logcig-years”的属性，该指标最初是通过使用 DNA 加合物形成的生物模型得出的。我们将该指标与其他三个吸烟指标进行比较，即包年数、平方根包年数以及吸烟持续时间和强度是单独变量的模型。我们的比较使用广义加性模型和逻辑回归来检查癌症的 Logit 概率与每个指标之间的关系，并调整其他协变量。所有模型均使用来自 1,275 例肺癌研究和 1,269 名对照者的肺癌研究数据进行拟合，该研究重点关注基因吸烟关系。在该样本中，logcig-years 与肺癌的 logit 概率之间存在非常显着的线性关系，无需根据吸烟状况进行调整。其他指标不共享这些属性。在此样本中，logcig-years 比其他指标捕获了更多关于吸烟的信息，这些信息对肺癌风险很重要。总之，我们提供了一个通用框架，用于评估吸烟是关键变量的研究中的不同吸烟指标。
Many approaches have been taken to adjust for smoking in modeling cancer risk. In case-control studies, these metrics are often used arbitrarily rather than being based on the properties of the metric in the context of the study. Depending on the underlying study design, hypotheses, and base population, different metrics may be deemed most appropriate. We present our approach to evaluating different smoking metrics. We examine the properties of a new metric, "logcig-years", that we initially derived from using a biological model of DNA adduct formation. We compare this metric to three other smoking metrics, namely pack-years, square-root pack-years, and a model in which smoking duration and intensity are separate variables. Our comparisons use generalized additive models and logistic regression to examine the relationship between the logit probability of cancer and each of the metrics, adjusting for other covariates. All models were fit using data from a lung cancer study of 1,275 cases and 1,269 controls that has focused on gene-smoking relationships. There was a very significant, linear relationship between logcig-years and the logit probability of lung cancer in this sample, without any need to adjust for smoking status. These properties together were not shared by the other metrics. In this sample, logcig-years captured more information about smoking that is important in lung cancer risk than the other metrics. In conclusion, we provide a general framework for evaluating different smoking metrics in studies where smoking is a critical variable.