Robins J M, Gill R D
Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
Stat Med. 1997;16(1-3):39-56. doi: 10.1002/(sici)1097-0258(19970115)16:1<39::aid-sim535>3.0.co;2-d.
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.
我们讨论了一类新的可忽略非单调缺失数据模型——随机单调缺失（RMM）模型。我们认为，RMM模型代表了生成非单调可忽略数据最普遍合理的物理机制。我们表明存在不是RMM的可忽略缺失数据过程。我们认为，如果统计检验拒绝了缺失数据过程可由RMM表示的假设，那么在缺失机制可忽略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模型来分析一项关于辐射对乳腺癌影响的病例对照研究的数据。
