Ma Shujie, Ma Yanyuan, Wang Yanqing, Kravitz Eli S, Carroll Raymond J
Department of Statistics, University of California at Riverside, Riverside, CA92521.
Department of Statistics, University of South Carolina, Columbia, SC 29208.
J Am Stat Assoc. 2017;112(520):1648-1662. doi: 10.1080/01621459.2016.1222944. Epub 2017 Jul 18.
We consider a problem motivated by issues in nutritional epidemiology, across diseases and populations. In this area, it is becoming increasingly common for diseases to be modeled by a single diet score, such as the Healthy Eating Index, the Mediterranean Diet Score, etc. For each disease and for each population, a partially linear single-index model is fit. The partially linear aspect of the problem is allowed to differ in each population and disease. However, and crucially, the single-index itself, having to do with the diet score, is common to all diseases and populations, and the nonparametrically estimated functions of the single-index are the same up to a scale parameter. Using B-splines with an increasing number of knots, we develop a method to solve the problem, and display its asymptotic theory. An application to the NIH-AARP Study of Diet and Health is described, where we show the advantages of using multiple diseases and populations simultaneously rather than one at a time in understanding the effect of increased Milk consumption. Simulations illustrate the properties of the methods.
我们考虑一个受营养流行病学中跨疾病和人群问题所驱动的问题。在这个领域,用单一饮食评分(如健康饮食指数、地中海饮食评分等)对疾病进行建模变得越来越普遍。对于每种疾病和每个人群,都拟合一个部分线性单指标模型。该问题的部分线性方面在每个疾病和人群中允许有所不同。然而,至关重要的是,与饮食评分相关的单指标本身对所有疾病和人群来说是相同的,并且单指标的非参数估计函数在比例参数上是相同的。使用节点数量不断增加的B样条,我们开发了一种解决该问题的方法,并展示了其渐近理论。描述了在国立卫生研究院 - 美国退休人员协会饮食与健康研究中的应用,在那里我们展示了同时使用多种疾病和人群而非一次只使用一种疾病和人群来理解增加牛奶消费的影响的优势。模拟说明了这些方法的性质。