Terrin Norma, Rodday Angie Mae, Parsons Susan K
Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 800 Washington Street, Box # 063, Boston, MA, 02111, USA,
Qual Life Res. 2015 Jan;24(1):31-9. doi: 10.1007/s11136-013-0550-2. Epub 2013 Oct 16.
To test whether longitudinally measured health-related quality of life (HRQL) predicts transplant-related mortality (TRM) in pediatric hematopoietic stem cell transplant (HSCT).
The predictors of interest were emotional functioning, physical functioning, role functioning, and global HRQL, as rated by the parent about the child up to 6 times over 12 months of follow-up and measured by the Child Health Ratings Inventories. We used joint models, specifically shared parameter models, with time to TRM as the outcome of interest and other causes of mortality as a competing risk, via the JM software package in R. Choosing shared parameter models instead of standard survival models, such as Cox models with time-dependent covariates, enabled us to address measurement error in the HRQL trajectories and appropriately handle missing data. The nonlinear trajectories for each HRQL domain were modeled by random spline functions. The survival submodels were adjusted for baseline patient, family, and transplant characteristics.
Hazard ratios per one-half standard deviation difference in emotional, physical, and role functioning, and global HRQL were 0.61 (95 % CI 0.46-0.81; p < 0.001), 0.70 (0.51-0.96; p = 0.03), 0.54 (0.34-0.85; p = 0.007), and 0.57 (0.41-0.79; p < 0.001), respectively.
HRQL trajectories were predictive of TRM in pediatric HSCT, even after adjusting the survival outcome for baseline characteristics.
检验纵向测量的健康相关生活质量（HRQL）是否能预测儿童造血干细胞移植（HSCT）中与移植相关的死亡率（TRM）。
感兴趣的预测因素包括情感功能、身体功能、角色功能和整体HRQL，由父母对孩子在长达12个月的随访中进行多达6次的评分，并通过儿童健康评级量表进行测量。我们使用联合模型，特别是共享参数模型，将TRM时间作为感兴趣的结局，将其他死亡原因作为竞争风险，通过R语言中的JM软件包进行分析。选择共享参数模型而非标准生存模型，如具有时间依存性协变量的Cox模型，使我们能够处理HRQL轨迹中的测量误差并妥善处理缺失数据。每个HRQL领域的非线性轨迹通过随机样条函数进行建模。生存子模型根据患者基线、家庭和移植特征进行了调整。
情感、身体和角色功能以及整体HRQL每相差半个标准差的风险比分别为0.61（95%CI 0.46 - 0.81；p < 0.001）、0.70（0.51 - 0.96；p = 0.03）、0.54（0.34 - 0.85；p = 0.007）和0.57（0.41 - 0.79；p < 0.001）。
即使在根据基线特征调整生存结局后，HRQL轨迹仍可预测儿童HSCT中的TRM。
