1 Department of Biostatistics, Georgetown University, Washington, DC, USA.
2 Department of Biomedical Statistics and Bioinformatics, Kyoto University, Japan.
Stat Methods Med Res. 2019 Jan;28(1):70-83. doi: 10.1177/0962280217715051. Epub 2017 Jun 19.
Analyzing longitudinal dyadic data is a challenging task due to the complicated correlations from repeated measurements and within-dyad interdependence, as well as potentially informative (or non-ignorable) missing data. We propose a dyadic shared-parameter model to analyze longitudinal dyadic data with ordinal outcomes and informative intermittent missing data and dropouts. We model the longitudinal measurement process using a proportional odds model, which accommodates the within-dyad interdependence using the concept of the actor-partner interdependence effects, as well as dyad-specific random effects. We model informative dropouts and intermittent missing data using a transition model, which shares the same set of random effects as the longitudinal measurement model. We evaluate the performance of the proposed method through extensive simulation studies. As our approach relies on some untestable assumptions on the missing data mechanism, we perform sensitivity analyses to evaluate how the analysis results change when the missing data mechanism is misspecified. We demonstrate our method using a longitudinal dyadic study of metastatic breast cancer.
分析纵向对偶数据是一项具有挑战性的任务，因为重复测量和对偶内相关性会产生复杂的相关性，并且可能存在信息（或不可忽略）缺失数据。我们提出了一种对偶共享参数模型，用于分析具有有序结果和信息性间歇性缺失数据和辍学的纵向对偶数据。我们使用比例优势模型来模拟纵向测量过程，该模型使用主体-伙伴相互依存效应的概念来适应对偶内的相互依存关系，以及对偶特定的随机效应。我们使用转移模型来模拟信息性辍学和间歇性缺失数据，该模型与纵向测量模型共享相同的随机效应集。我们通过广泛的模拟研究来评估所提出方法的性能。由于我们的方法依赖于对缺失数据机制的一些未经检验的假设，因此我们进行敏感性分析，以评估当缺失数据机制被错误指定时分析结果如何变化。我们使用转移性乳腺癌的纵向对偶研究来演示我们的方法。
