The impact of missing data on estimation of health-related quality of life outcomes: an analysis of a randomized longitudinal clinical trial
The impact of missing data on estimation of health-related quality of life outcomes: an analysis of a randomized longitudinal clinical trial
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DOI：
10.1007/s10742-011-0074-6
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发表时间：
2011-12-01
影响因子：
1.5
通讯作者：
Cella, David
中科院分区：
文献类型：
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作者：
Du, Hongyan;Hahn, Elizabeth A.;Cella, David
健康相关生活质量（HRQL）结局的缺失应答在临床试验中很常见，可能会引入偏倚，因为此类数据通常不是随机缺失的。为了评价在纵向随机试验中比较两个治疗组时的缺失（脱落）效应，我们分析了新诊断的慢性粒细胞白血病患者12个月内癌症治疗试验结局指数（TOI）的功能评估变化。预计在基线和第1、2、3、4、5、6、9和12个月进行HRQL评估。我们将完成者定义为具有基线和第12个月TOI的患者，将脱落者定义为具有基线评分的所有其他患者。我们将删失时间定义为基线和计划的第12个月访视日期之间的时间间隔，将至脱落的近似时间定义为从基线到末次报告TOI日期和计划的下一次访视日期之间的中点的时间间隔。首先建立混合效应模型以评估治疗效应;然后建立模式混合模型和联合模型以解释不可解释的脱落。间歇性缺失数据被假定为随机缺失。对TOI评分进行平方根转换，以满足所有模型中每个时间点的正态性和同质性假设。混合效应模型显示，除基线外，每次访视时组间差异均具有显著性（P < 0.001）。联合模型生成的参数估计值与单独的纵向和生存子模型相似，具有显著相关参数（P = 0.039），表明TOI斜率与脱落风险之间存在负相关性，因此脱落不可预测。模式混合模型参数估计值与联合模型生成的参数估计值非常相似。当纵向研究中存在不可验证的缺失数据时，联合模型可用于量化脱落与结局之间的关系。此外，重要的是要检查基本假设，并利用多个缺失数据模型，包括模式混合模型，以评估基于模型的推理对缺失机制假设的敏感性。
Missing responses for health-related quality of life (HRQL) outcomes are common in clinical trials and may introduce bias as such data are often not missing at random. To evaluate the missingness (dropout) effect when comparing two treatment groups in a longitudinal randomized trial, we analyzed the Functional Assessment of Cancer Therapy Trial Outcome Index (TOI) change over 12 months for newly diagnosed patients with chronic myeloid leukemia. HRQL assessment was expected at baseline and months 1, 2, 3, 4, 5, 6, 9 and 12. We defined completers as those with baseline and month 12 TOI, and dropouts as all others as long as they had a baseline score. We defined censoring time as the time interval between baseline and the scheduled month 12 visit dates and approximate time-to-dropout as the time interval from baseline to the midpoint between date of the last reported TOI and the scheduled next visit date. A mixed-effects model was first built to assess treatment effect; a pattern-mixture model and a joint model were then built to account for non-ignorable dropout. Intermittent missing data were assumed to be missing at random. A square root transformation of TOI scores was taken to fulfill the normality and homogeneity assumption at each time point in all the models. The mixed-effects model revealed significant (P < 0.001) between-group differences at each visit except for baseline. The joint model generated similar parameter estimates as the separate longitudinal and survival sub-models with a significant association parameter (P = 0.039) indicating negative association between slope of TOI and hazard of dropout and thus non-ignorable dropout. The pattern-mixture model parameter estimates were fairly similar to those generated from the joint model. When non-ignorable missing data exist in longitudinal studies, a joint model is useful to quantify the relationship between dropout and outcome. In addition, it is important to examine underlying assumptions and utilize multiple missing data models including the pattern mixture model to assess sensitivity of model based inference to assumptions about missing mechanisms.