Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, U.S.A.
Stat Med. 2011 Jul 10;30(15):1837-51. doi: 10.1002/sim.4240. Epub 2011 Apr 15.
In matched case-crossover studies, it is generally accepted that covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression model because any stratum effect is removed by the conditioning on the fixed number of sets of a case and controls in the stratum. Hence, the conditional logistic regression model is not able to detect any effects associated with the matching covariates by stratum. In addition, the matching covariates may be effect modification and the methods for assessing and characterizing effect modification by matching covariates are quite limited. In this article, we propose a unified approach in its ability to detect both parametric and nonparametric relationships between the predictor and the relative risk of disease or binary outcome, as well as potential effect modifications by matching covariates. Two methods are developed using two semiparametric models: (1) the regression spline varying coefficients model and (2) the regression spline interaction model. Simulation results show that the two approaches are comparable. These methods can be used in any matched case-control study and extend to multilevel effect modification studies. We demonstrate the advantage of our approach using an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis associated with drinking water turbidity.
在匹配病例对照研究中，人们普遍认为，病例和相关对照所匹配的协变量不会对条件逻辑回归模型中包含的独立预测因素产生混杂影响，因为在该条件下，任何层效应都被去除了。因此，条件逻辑回归模型无法检测到与分层匹配协变量相关的任何效果。此外，匹配协变量可能是效应修饰，并且评估和描述匹配协变量效应修饰的方法非常有限。在本文中，我们提出了一种统一的方法，能够检测预测因素与疾病或二项结果的相对风险之间的参数和非参数关系，以及匹配协变量的潜在效应修饰。使用两个半参数模型开发了两种方法：（1）回归样条变系数模型和（2）回归样条交互模型。模拟结果表明，这两种方法具有可比性。这些方法可用于任何匹配病例对照研究，并扩展到多层次的效应修饰研究。我们通过一个与饮用水浑浊度有关的儿童无菌性脑膜炎的 1-4 双向病例交叉研究的流行病学示例展示了我们方法的优势。
