Joint models for predicting transplant-related mortality from quality of life data

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.