Andersen Elisabeth Wreford, Andersen Per Kragh
Danish Epidemiology Science Centre, Statens Serum Institut, Artillerivej 5, DK-2300 Copenhagen S, Denmark.
Stat Med. 2002 Dec 15;21(23):3595-607. doi: 10.1002/sim.1319.
This paper discusses the misclassification that occurs when relying solely on routine register data in family studies of disease clustering. A register study of familial aggregation of schizophrenia is used as an example. The familial aggregation is studied using a regression model for the disease in the child including the disease status of the parents as a risk factor. If all the information is found in the routine registers then the disease status of the parents is only known from the time when the register started and if this information is used unquestioningly the parents who have had the disease before this time are misclassified as disease-free. Two methods are presented to adjust for this misclassification: regression calibration and an EM-type algorithm. These methods are used in the schizophrenia example where the large effect of having a schizophrenic mother hardly shows any signs of bias due to misclassification. The methods are also studied in simulations showing that the misclassification problem increases with the disease frequency.
本文讨论了在疾病聚集性的家族研究中仅依靠常规登记数据时出现的错误分类问题。以一项精神分裂症家族聚集性的登记研究为例。使用一个针对儿童疾病的回归模型来研究家族聚集性，该模型将父母的疾病状况作为一个风险因素。如果所有信息都能在常规登记中找到，那么父母的疾病状况仅从登记开始时可知，并且如果不加质疑地使用这些信息，在此之前患过病的父母会被错误分类为无病。本文提出了两种方法来调整这种错误分类：回归校准和一种期望最大化（EM）类型的算法。这些方法应用于精神分裂症的例子中，结果显示有精神分裂症母亲的巨大影响几乎没有因错误分类而出现偏差的迹象。这些方法也在模拟研究中进行了探讨，结果表明错误分类问题会随着疾病频率的增加而加剧。
