In longitudinal studies, it is not uncommon to make multiple attempts to collect a measurement after baseline. Recording whether these attempts are successful provides useful information for the purposes of assessing missing data assumptions. This is because measurements from subjects who provide the data after numerous failed attempts may differ from those who provide the measurement after fewer attempts. Previous models for these designs were parametric and/or did not allow sensitivity analysis. For the former, there are always concerns about model misspecification and for the latter, sensitivity analysis is essential when conducting inference in the presence of missing data. Here, we propose a new approach which minimizes issues with model misspecification by using Bayesian nonparametrics for the observed data distribution. We also introduce a novel approach for identification and sensitivity analysis. We re-analyze the repeated attempts data from a clinical trial involving patients with severe mental illness and conduct simulations to better understand the properties of our approach.

翻译：在纵向研究中，基线后多次尝试收集测量值的情况并不罕见。记录这些尝试是否成功，可为评估缺失数据假设提供有用信息。这是因为经历多次失败后才提供数据的受试者，其测量值可能与尝试次数较少即提供数据的受试者存在差异。先前针对这类设计的模型多为参数模型，且/或未允许进行敏感性分析。前者始终存在模型误设的担忧，而后者在存在缺失数据时进行推断时，敏感性分析至关重要。本文提出一种新方法，通过采用贝叶斯非参数化观测数据分布，最大程度减少模型误设问题。同时引入一种新的识别与敏感性分析方法。我们重新分析了涉及严重精神疾病患者的临床试验中的重复尝试数据，并通过模拟研究进一步理解所提方法的特性。