Kmetic Andrew, Joseph Lawrence, Berger Claudie, Tenenhouse Alan
Department of Epidemiology, Montreal General Hospital, 1650 Cedar Avenue, Montreal, Quebec H3G 1A4, Canada.
Epidemiology. 2002 Jul;13(4):437-44. doi: 10.1097/00001648-200207000-00012.
Nonresponse bias is a concern in any epidemiologic survey in which a subset of selected individuals declines to participate.
We reviewed multiple imputation, a widely applicable and easy to implement Bayesian methodology to adjust for nonresponse bias. To illustrate the method, we used data from the Canadian Multicentre Osteoporosis Study, a large cohort study of 9423 randomly selected Canadians, designed in part to estimate the prevalence of osteoporosis. Although subjects were randomly selected, only 42% of individuals who were contacted agreed to participate fully in the study. The study design included a brief questionnaire for those invitees who declined further participation in order to collect information on the major risk factors for osteoporosis. These risk factors (which included age, sex, previous fractures, family history of osteoporosis, and current smoking status) were then used to estimate the missing osteoporosis status for nonparticipants using multiple imputation. Both ignorable and nonignorable imputation models are considered.
Our results suggest that selection bias in the study is of concern, but only slightly, in very elderly (age 80+ years), both women and men.
Epidemiologists should consider using multiple imputation more often than is current practice.
无应答偏倚是任何流行病学调查中都需要关注的问题，在这类调查中，部分被选中的个体拒绝参与。
我们回顾了多重填补法，这是一种广泛适用且易于实施的贝叶斯方法，用于校正无应答偏倚。为说明该方法，我们使用了来自加拿大多中心骨质疏松症研究的数据，这是一项对9423名随机选取的加拿大人进行的大型队列研究，部分目的是估计骨质疏松症的患病率。尽管受试者是随机选取的，但只有42%被联系的个体同意全面参与研究。研究设计包括为那些拒绝进一步参与的受邀者提供一份简短问卷，以收集骨质疏松症主要危险因素的信息。然后使用这些危险因素（包括年龄、性别、既往骨折史、骨质疏松症家族史和当前吸烟状况），通过多重填补法估计未参与者缺失的骨质疏松症状态。同时考虑了可忽略和不可忽略的填补模型。
我们的结果表明，该研究中的选择偏倚值得关注，但仅在80岁及以上的老年男性和女性中存在轻微偏倚。
流行病学家应比目前的做法更频繁地考虑使用多重填补法。
