Multiply imputing missing values arising by design in transplant survival data

对器官移植接受者进行移植后的跟踪，记录他们的存活时间以及各种解释变量。由于不同中心的数据收集程序或随着时间的推移而有所不同，可能只为某些接收者记录了特定的解释变量（或一组变量），这导致数据中大量记录缺少这一变量。该变量也可能是生存的重要预测因素，因此适当处理这种设计缺失问题非常重要。Pankhurst等人（2018）已经表明，多重插补优于其他常用方法来处理这个问题。然而，可能有不止一种方法可以实现估算过程，这为我们的工作带来了一个有趣的途径。例如，BMI等变量是从身高和体重推导出来的，因此，我们可以直接估算用于构建衍生变量的变量（这里是身高和体重），而不是直接估算衍生变量（这里是BMI），然后根据估算的数据构建衍生变量。这将是有趣的探索，在失踪的设计背景。

Recipients of organ transplants are followed up from transplantation and their survival times recorded, together with various explanatory variables. Due to differences in data collection procedures in different centres or over time, a particular explanatory variable (or set of variables) may only be recorded for certain recipients, which results in this variable being missing for a substantial number of records in the data. The variable may also turn out to be an important predictor of survival and so it is important to handle this missing-by-design problem appropriately.Pankhurst et al. (2018) have shown that multiple imputation outperforms other methods commonly in use to handle this issue. However, there may be more than one way the imputation process could be implemented, which leads to an interesting avenue for our work. For example, a variable such as BMI is derived from height and weight, so rather than imputing the derived variable, BMI here, directly, we could instead impute the variables used to construct the derived variable, here height and weight, and then construct the derived variable based on the imputed data. It will be interesting to explore this in the missing by design context.