摘要
目的构建多分类倾向性评分匹配方法,并将其应用到分组变量为三分类的流行病学调查数据中。方法数据来源于前期的流行病学调查数据,从五个城市中随机抽取3600名受访者,收集他们的人口学信息。让受访者自己评价其健康情况,分析自评健康为好、一般和差的受访者其慢性病发病率是否相同,并探讨慢性病发病与否同时还受其他混杂因素的影响。结果在不同倾向性评分匹配方法中,只有马氏距离匹配可消除不同组间协变量的不均衡,设定匹配时的平均马氏距离为0.12。结论马氏距离匹配适用于本研究的数据类型,可有效控制三组间的混杂因素。
Objective To establish propensity score matching to multiple data and apply it in the epidemiological data with three categories. Methods Data were obtained from previous epidemiological surveys, in which 3600 subjects from 5 cities were randomly selected. Demographic information was collected and self-rated health was obtained. We analyzed whether the incidence of chronic disease was different among the subjects whose self-rated health were good, fair, or bad, respectively. Also, we explored whether the incidence of chronic disease was also affected by other confounding factors. Results In different propensity score matching, only Mahalanobis metric matching could eliminate the confounding factors among different groups, with caliper of 0.12. Conclusion Mahalanobis metric matching is suitable for our data, and can effectively control the confounding factors among three groups.
出处
《中国卫生信息管理杂志》
2013年第5期448-451,共4页
Chinese Journal of Health Informatics and Management
关键词
倾向性评分
匹配方法
自评健康
慢性病
Propensity score, Matching, Self-rated health, Chronic disease
