A Method-of-Moments Estimation Procedure for Categorical Quality-of-Life Data with Nonignorable Missingness
针对临床试验中分类生活质量数据存在不可忽略缺失的问题，提出一种矩估计方法，推导模型可识别条件，并在乳腺癌临床试验数据中应用，模拟比较了矩估计与最大似然估计的表现。
Abstract Quality-of-life outcomes collected during clinical trials often have considerable amounts of missing data, which, if not appropriately accounted for, may lead to bias in inferences. We introduce a method-of-moments (MM) estimating procedure for a model designed to handle nonignorable missingness arising in categorical data measured on independent populations. The missingness mechanism is assumed to be the same across the populations. We derive necessary and sufficient conditions for the identifiability of the model and fit the model to quality-of-life data collected as part of a breast cancer clinical trial. We compare the MM estimator to the maximum likelihood estimator in a simulation study.