Fairclough D L, Peterson H F, Chang V
Center for Methodologic Research and Biometry, AMC Cancer Research Center, Denver, CO 80214, USA.
Stat Med. 1998;17(5-7):667-77. doi: 10.1002/(sici)1097-0258(19980315/15)17:5/7<667::aid-sim813>3.0.co;2-6.
Assessment of health related quality of life has become an important endpoint in many cancer clinical trials. Because the participants of these trials often experience disease and treatment related morbidity and mortality, non-random missing assessments are inevitable. Examples are presented from several such trials that illustrate the impact of missing data on the analysis of QOL in these trials. The sensitivity of different analyses depends on the proportion of assessments that are missing and the strength of the association of the underlying reasons for missing data with disease and treatment related morbidity and mortality. In the setting of clinical trials of cancer therapy, the assumption that the data are missing completely at random (MCAR) and analyses of complete cases is usually unjustified. Further, the assumption of missing at random (MAR) may also be violated in many trials and models appropriate for non-ignorable missing data should be explored. Recommendations are presented to minimize missing data, to obtain useful documentation concerning the reasons for missing data and to perform sensitivity analyses.
在许多癌症临床试验中，对健康相关生活质量的评估已成为一个重要的终点指标。由于这些试验的参与者经常经历与疾病和治疗相关的发病率和死亡率，非随机缺失评估是不可避免的。文中列举了几个此类试验的例子，说明了缺失数据对这些试验中生活质量分析的影响。不同分析方法的敏感性取决于缺失评估的比例以及缺失数据的潜在原因与疾病和治疗相关发病率和死亡率之间关联的强度。在癌症治疗临床试验的背景下，数据完全随机缺失（MCAR）的假设以及对完全病例的分析通常是不合理的。此外，随机缺失（MAR）的假设在许多试验中也可能被违反，应探索适用于不可忽略缺失数据的模型。文中还提出了一些建议，以尽量减少缺失数据，获取有关缺失数据原因的有用文档，并进行敏感性分析。
