不同缺失机制并存时应答偏倚校正方法在医保学生医疗费用调查中的应用

Study on correction of data bias caused by different missing mechanisms in survey of medical expenditure among students enrolling in Urban Resident Basic Medical Insurance

在研究2012年太原市城镇居民医保参保学生（幼儿园至大学）的医疗费用及其影响因素时，发现因变量数据中同时存在随机无应答偏倚（随机缺失）和选择性偏倚（非随机缺失），为此本研究提出一个多重填补与样本选择模型相结合的两阶段策略，同时校正这两种偏倚。实例中经过两阶段抽样、问卷调查，整理获得合格数据1190例，因变量中存在2．52％非随机缺失和7．14％随机缺失。第一阶段利用完整数据对随机缺失进行多重填补，第二阶段对填补后的数据利用样本选择模型校正非随机缺失，同时建立多因素分析模型。通过1000次两阶段校正模拟研究比较4种不同多重填补方法，得出在此缺失比例组合下预测均数匹配法与样本选择模型结合的校正效果最优。最终在实例分析中得到影响太原市居民医保参保学生年度医疗费用的因素有被调查者类型、家庭年毛收入、对医疗费用水平的承受程度、慢性病、到社区卫生服务或私人诊所就诊、到医院门诊就诊、住院、是否有应住院而未住院情况、自我医疗、可接受的自付医疗费用比例。表明应用多重填补与样本选择模型相结合的两阶段校正方法，可有效校正调查数据因变量中存在的随机无应答偏倚和选择性偏倚。

The study of the medical expenditure and its influencing factors among the students enrolling in Urban Resident Basic Medical Insurance （URBMI） in Taiyuan indicated that non response bias and selection bias coexist in dependent variable of the survey data. Unlike previous studies only focused on one missing mechanism, a two-stage method to deal with two missing mechanisms simultaneously was suggested in this study, combining multiple imputation with sample selection model. A total of 1 190 questionnaires were retumed by the students （or their parents） selected in child care settings, schools and universities in Taiyuan by stratified cluster random sampling in 2012. In the retumed questionnaires, 2.52% existed not missing at random （NMAR） of dependent variable and 7.14% existed missing at random （MAR） of dependent variable. First, multiple imputation was conducted for MAR by using completed data, then sample selection model was used to correct NMAR in multiple imputation, and a multi influencing factor analysis model was established. Based on 1 000 times resampling, the best scheme of filling the random missing values is the predictive mean matching（PMM） method under the missing proportion. With this optimal scheme, a two stage survey was conducted. Finally, it was found that the influencing factors on annual medical expenditure among the students enrolling in URBMI in Taiyuan included population group, annual household gross income, affordability of medical insurance expenditure, chronic disease, seeking medical care in hospital, seeking medical care in community health center or private clinic, hospitalization, hospitalization canceled due to certain reason, self medication and acceptable proportion of self-paid medical expenditure. The two-stage method combining multiple imputation with sample selection model can deal with non response bias and selection bias effectively in dependent variable of the survey data.