Non-response models for the analysis of non-monotone ignorable missing data.
Non-response models for the analysis of non-monotone ignorable missing data.
复制标题
用于分析非单调可忽略缺失数据的无响应模型。
DOI：
10.1002/(sici)1097-0258(19970115)16:1
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发表时间：
1997
影响因子：
2
通讯作者：
Gill,RD
中科院分区：
文献类型：
--
作者：
Robins,JM;Gill,RD
本文讨论了一类新的非单调缺失数据模型--随机单调缺失（RMM）模型.我们认为，RMM模型代表了产生非单调可验证数据的最普遍的合理物理机制。我们发现，存在可验证的缺失数据的过程，不是RMM。因此，我们认为，如果统计检验拒绝了缺失数据过程是RMM可表示的假设，则在缺失机制是可解释的假设下分析非单调缺失数据可能是不合适的。我们使用RMM模型来分析辐射对乳腺癌影响的病例对照研究数据。© 1997年由John Wiley & Sons，Ltd.
We discuss a new class of ignorable non‐monotone missing data models – the randomized monotone missingness (RMM) models. We argue that the RMM models represent the most general plausible physical mechanism for generating non‐monotone ignorable data. We show that there exists ignorable missing data processes that are not RMM. We argue that it may therefore be inappropriate to analyse non‐monotone missing data under the assumption that the missingness mechanism is ignorable, if a statistical test has rejected the hypothesis that the missing data process is RMM representable. We use RMM models to analyse data from a case‐control study of the effects of radiation on breast cancer. © 1997 by John Wiley & Sons, Ltd.