Robust quasi-randomization-based estimation with ensemble learning for missing data

Missing data analysis requires assumptions about an outcome model or a response probability model to adjust for potential bias due to nonresponse. Doubly robust (DR) estimators are consistent if at least one of the models is correctly specified. Multiply robust (MR) estimators extend DR estimators by allowing for multiple models for both the outcome and/or response probability models and are consistent if at least one of the multiple models is correctly specified. We propose a robust quasi-randomization-based model approach to bring more protection against model misspecification than the existing DR and MR estimators, where any multiple semiparametric, nonparametric or machine learning models can be used for the outcome variable. The proposed estimator achieves unbiasedness by using a subsampling Rao–Blackwell method, given cell-homogenous response, regardless of any working models for the outcome. An unbiased variance estimation formula is proposed, which does not use any replicate jackknife or bootstrap methods. A simulation study shows that our proposed method outperforms the existing multiply robust estimators.     

花生四烯酸对小鼠卵泡衰老的影响

目的:研究花生四烯酸(arachidonic acid,AA)对小鼠卵泡发育的影响,探讨AA与卵巢早衰的关系?方法:10% AA饲料喂食4周龄ICR雌鼠,4个月后观察小鼠子宫卵巢重量?卵巢组织脂肪酸分析?卵巢组织学改变?结果:10%AA组与对照组相比子宫卵巢体重下降;脂肪酸分析结果显示10%AA组卵巢n-6/n-3不饱和脂肪酸比值增大;光镜下,10%AA组可见卵巢退行性黄体增多?间质纤维化?卵巢闭锁卵泡所占比例增加?结论:喂食10%AA饲料可引起小鼠卵巢衰老?