Minini Pascal, Chavance Michel
Laboratoire GlaxoSmithKline, Unité Méthodologie et Biostatistique, 100 route de Versailles, 78163 Marly le Roi, France.
Biostatistics. 2004 Oct;5(4):531-44. doi: 10.1093/biostatistics/kxh006.
This paper highlights the consequences of incomplete observations in the analysis of longitudinal binary data, in particular non-monotone missing data patterns. Sensitivity analysis is advocated and a method is proposed based on a log-linear model. A sensitivity parameter that represents the relationship between the response mechanism and the missing data mechanism is introduced. It is shown that although this parameter is identifiable, its estimation is highly questionable. A far better approach is to consider a range of plausible values and to estimate the parameters of interest conditionally upon each value of the sensitivity parameter. This allows us to assess the sensitivity of study's conclusion to assumptions regarding the missing data mechanism. The method is applied to a randomized clinical trial comparing the efficacy of two treatment regimens in patients with persistent asthma.
本文强调了在纵向二元数据（特别是非单调缺失数据模式）分析中不完全观测的后果。提倡进行敏感性分析，并基于对数线性模型提出了一种方法。引入了一个表示响应机制与缺失数据机制之间关系的敏感性参数。结果表明，尽管该参数是可识别的，但其估计存在很大问题。一种更好的方法是考虑一系列合理的值，并根据敏感性参数的每个值有条件地估计感兴趣的参数。这使我们能够评估研究结论对关于缺失数据机制假设的敏感性。该方法应用于一项随机临床试验，比较两种治疗方案对持续性哮喘患者的疗效。
