The use of weights to account for non-response and drop-out
The use of weights to account for non-response and drop-out
复制标题
DOI：
10.1007/s00127-005-0882-5
复制
发表时间：
2005-04-01
影响因子：
4.4
通讯作者：
Wittchen, HU
中科院分区：
文献类型：
--
作者：
Höfler, M;Pfister, H;Wittchen, HU
背景精神研究和其他领域的经验研究通常显示出大量拒绝和辍学率。非参与和辍学可能会引入偏差，该偏差的幅度取决于其决定因素与各自感兴趣的参数的强烈关系。方法当丢失大多数信息时，标准方法是估计每个受访者参与的可能性，并为每个受访者分配与此概率成反比的权重。本文包含有关统计权重计算和加权数据分析的主要思想和原则的回顾。结果提供了对加权数据的简短审查，并通过EDSP（精神病理学的早期发育阶段）研究来说明统计权重的使用。结果表明，忽略不同的采样和响应概率可能会对估计的优势比产生重大影响。结论，应与加权参数估计值中的差异增加来平衡减少采样偏置的偏见的好处。
Background Empirical studies in psychiatric research and other fields often show substantially high refusal and drop-out rates. Non-participation and drop-out may introduce a bias whose magnitude depends on how strongly its determinants are related to the respective parameter of interest. Methods When most information is missing, the standard approach is to estimate each respondent's probability of participating and assign each respondent a weight that is inversely proportional to this probability. This paper contains a review of the major ideas and principles regarding the computation of statistical weights and the analysis of weighted data. Results A short software review for weighted data is provided and the use of statistical weights is illustrated through data from the EDSP (Early Developmental Stages of Psychopathology) Study. The results show that disregarding different sampling and response probabilities can have a major impact on estimated odds ratios. Conclusions The benefit of using statistical weights in reducing sampling bias should be balanced against increased variances in the weighted parameter estimates.