Pattern Mixture Models for the Analysis of Repeated Attempt Designs
Pattern Mixture Models for the Analysis of Repeated Attempt Designs
复制标题
DOI：
10.1111/biom.12353
复制
发表时间：
2015-12-01
期刊：
影响因子：
1.9
通讯作者：
White, Ian R.
中科院分区：
文献类型：
--
作者：
Daniels, Michael J.;Jackson, Dan;White, Ian R.
在随访研究中，多次尝试收集基线后的测量值并不罕见。记录这些尝试是否成功为评估随机缺失（MAR）假设和促进非随机缺失（MNAR）建模提供了有用的信息。这是因为在多次尝试失败后提供该数据的受试者的测量结果可能与在较少尝试后提供测量结果的受试者不同。迄今为止，这种类型的提供测量的“连续抵抗”已经在选择模型框架中建模，其中结果数据与给定这些结果的尝试的成功或失败联合建模。在这里，我们提出了一种模式混合的方法来建模这种类型的数据。我们重新分析了重复尝试的数据，从以前使用选择模型的方法分析的试验。我们的模式混合模型是更灵活的，是更透明的参数可识别性比以前被用来模拟重复尝试数据的模型，并允许敏感性分析。我们的结论是，我们的方法来建模这种类型的数据提供了一个完全可行的替代更成熟的选择模型。
It is not uncommon in follow-up studies to make multiple attempts to collect a measurement after baseline. Recording whether these attempts are successful or not provides useful information for the purposes of assessing the missing at random (MAR) assumption and facilitating missing not at random (MNAR) modeling. This is because measurements from subjects who provide this data after multiple failed attempts may differ from those who provide the measurement after fewer attempts. This type of "continuum of resistance" to providing a measurement has hitherto been modeled in a selection model framework, where the outcome data is modeled jointly with the success or failure of the attempts given these outcomes. Here, we present a pattern mixture approach to model this type of data. We re-analyze the repeated attempt data from a trial that was previously analyzed using a selection model approach. Our pattern mixture model is more flexible and is more transparent in terms of parameter identifiability than the models that have previously been used to model repeated attempt data and allows for sensitivity analysis. We conclude that our approach to modeling this type of data provides a fully viable alternative to the more established selection model.