Ortega-Villa Ana Maria, Kim Inyoung, Kim H
Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA, U.S.A.
Department of Public Health Science, Seoul National University, Seoul, Korea.
Stat Med. 2017 Mar 15;36(6):998-1013. doi: 10.1002/sim.7194. Epub 2016 Dec 15.
In matched case-crossover studies, it is generally accepted that the 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. This is because any stratum effect is removed by the conditioning on the fixed number of sets of the 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. However, some matching covariates such as time often play an important role as an effect modification leading to incorrect statistical estimation and prediction. Therefore, we propose three approaches to evaluate effect modification by time. The first is a parametric approach, the second is a semiparametric penalized approach, and the third is a semiparametric Bayesian approach. Our parametric approach is a two-stage method, which uses conditional logistic regression in the first stage and then estimates polynomial regression in the second stage. Our semiparametric penalized and Bayesian approaches are one-stage approaches developed by using regression splines. Our semiparametric one stage approach allows us to not only detect the parametric relationship between the predictor and binary outcomes, but also evaluate nonparametric relationships between the predictor and time. We demonstrate the advantage of our semiparametric one-stage approaches using both a simulation study and an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis with drinking water turbidity. We also provide statistical inference for the semiparametric Bayesian approach using Bayes Factors. Copyright © 2016 John Wiley & Sons, Ltd.
在匹配的病例交叉研究中，人们普遍认为，病例与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变量产生混杂效应。这是因为通过对层内固定数量的病例和对照集进行条件设定，任何层效应都会被消除。因此，条件逻辑回归模型无法检测到与按层匹配的协变量相关的任何效应。然而，一些匹配协变量，如时间，往往作为效应修饰因素发挥重要作用，从而导致错误的统计估计和预测。因此，我们提出了三种方法来评估时间的效应修饰作用。第一种是参数方法，第二种是半参数惩罚方法，第三种是半参数贝叶斯方法。我们的参数方法是一种两阶段方法，第一阶段使用条件逻辑回归，然后在第二阶段估计多项式回归。我们的半参数惩罚和贝叶斯方法是通过使用回归样条开发的单阶段方法。我们的半参数单阶段方法不仅能检测预测变量与二元结局之间的参数关系，还能评估预测变量与时间之间的非参数关系。我们通过模拟研究和儿童无菌性脑膜炎与饮用水浊度的1 - 4双向病例交叉研究的流行病学实例，展示了我们半参数单阶段方法的优势。我们还使用贝叶斯因子对半参数贝叶斯方法进行了统计推断。版权所有© 2016约翰·威利父子有限公司。
