Kim Yongman
Food and Drug Administration, Silver Spring, Maryland, USA.
J Biopharm Stat. 2011 Mar;21(2):311-25. doi: 10.1080/10543406.2011.550112.
In chronic pain trials, proper handling of missing data due to dropout is an important issue because the dropout rate is high and the study conclusion may depend on the method chosen. The intent-to-treat (ITT) principle usually requires imputations for missing data to include the dropouts as well as completers in the statistical analysis. However, a statistical analysis with imputation might lead to a misinterpretation of clinical data. In chronic pain trials, treatment-related dropouts are clinical outcomes themselves. For example, an early dropout due to toxicity usually indicates a treatment failure, as does a dropout due to lack of efficacy. Problems with traditional methods such as last observation carried forward (LOCF) or baseline observation carried forward (BOCF) are identified especially in the chronic pain setting. Alternative methods, such as continuous responder analysis and two-part model analysis, treating dropouts as clinical events, are introduced with an example of osteoarthritis clinical trial data.
在慢性疼痛试验中，由于脱落导致的缺失数据的妥善处理是一个重要问题，因为脱落率很高，且研究结论可能取决于所选择的方法。意向性分析（ITT）原则通常要求对缺失数据进行插补，以便在统计分析中纳入脱落者和完成者。然而，进行插补的统计分析可能会导致对临床数据的错误解读。在慢性疼痛试验中，与治疗相关的脱落本身就是临床结局。例如，因毒性导致的早期脱落通常表明治疗失败，因缺乏疗效导致的脱落也是如此。尤其在慢性疼痛背景下，已发现传统方法（如末次观察结转法（LOCF）或基线观察结转法（BOCF））存在问题。本文以骨关节炎临床试验数据为例，介绍了将脱落视为临床事件的替代方法，如连续反应者分析和两部分模型分析。
