Curran Desmond, Molenberghs Geert, Thijs Herbert, Verbeke Geert
ICON Clinical Research Ltd., Leopardstown, Dublin, Ireland.
J Biopharm Stat. 2004 Feb;14(1):125-43. doi: 10.1081/BIP-120028510.
Incomplete series of data is a common feature in quality-of-life studies, in particular in chronic diseases where attrition of patients is high. Two alternative approaches to modeling longitudinal data with incomplete measurements have frequently been proposed in the literature, selection models and pattern-mixture models. In this paper we focus on, by way of sensitivity analysis, extrapolating incomplete patterns using identifying restrictions. Perhaps the best known ones are so-called complete case missing value restrictions (CCMV), where for a given pattern, the conditional distribution of the missing data, given the observed data, is equated to its counterpart in the completers. Available case missing value (ACMV) restrictions equate this conditional density to the one calculated from the subgroup of all patterns for which all required components have been observed. Neighboring case missing value restrictions (NCMV) equate this conditional density to the one calculated from the the pattern with one additional measurement obtained. In this paper, these three identifying restriction strategies are used to multiply impute missing data in a study in metastatic prostate cancer. Multiple imputation is employed to reduce the uncertainty of single imputation. It is shown how hypothesis testing and sensitivity analyses are carried out in this setting.
数据系列不完整是生活质量研究中的一个常见特征，尤其是在慢性病患者流失率较高的情况下。文献中经常提出两种处理测量数据不完整的纵向数据建模的替代方法，即选择模型和模式混合模型。在本文中，我们通过敏感性分析，重点关注利用识别性限制来推断不完整模式。也许最著名的是所谓的完全病例缺失值限制（CCMV），即在给定模式下，缺失数据在给定观测数据条件下的条件分布等同于完整数据组中的对应分布。可用病例缺失值（ACMV）限制将此条件密度等同于从所有所需成分均已观测到的所有模式子组中计算出的密度。相邻病例缺失值限制（NCMV）将此条件密度等同于从通过额外一次测量获得的模式中计算出的密度。在本文中，这三种识别性限制策略被用于在一项转移性前列腺癌研究中对缺失数据进行多重填补。采用多重填补来降低单一填补的不确定性。展示了在这种情况下如何进行假设检验和敏感性分析。
