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

Abstract

Purpose To test whether longitudinally measured health-related quality of life (HRQL) predicts transplant-related mortality (TRM) in pediatric hematopoietic stem cell transplant (HSCT). Methods 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 (CHRIs). 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 non-linear trajectories for each HRQL domain were modeled by random spline functions. The survival submodels were adjusted for baseline patient, family, and transplant characteristics. Results Hazard ratios per one-half standard deviation difference in emotional, physical, and role functioning and global HRQL were 0.61 (95% CI: 0.46 to 0.81; p<0.001), 0.70 (0.51 to 0.96; p=0.03), 0.54 (0.34 to 0.85; p=0.007), and 0.57 (0.41 to 0.79; p<0.001), respectively. Conclusions HRQL trajectories were predictive of TRM in pediatric HSCT, even after adjusting the survival outcome for baseline characteristics.

Everyday life following hematopoietic stem cell transplantation: decline in physical symptoms within the first month and change-related predictors

“…Adverse physical symptoms are common indicators of the health-related quality of life (HRQOL) physical domain [ 12 ]. Earlier studies suggest that poorer physical condition is associated with diminished well-being of patients [ 13 ], a lower psychological domain of HRQOL [ 14 ], higher distress, anxiety and depression [ 13 , 15 – 18 ], and poorer health prognosis and survival rates [ 19 – 21 ]. Physical functioning of patients after HSCT is frequently the focus of studies.…”

Predicting Health Care Utilization for Children With Respiratory Insufficiency Using Parent-Proxy Ratings of Children's Health-Related Quality of Life

“…In addition to its usefulness as a patient-reported outcome, HRQL assessments provide can serve as predictors of future outcomes, such as prognosis and survival (Coates et al, 2000; Gotay et al, 2008; Hsu, Speers, Kennecke, & Cheung, 2017; Kaplan et al, 2007; Khouli et al, 2011; Terrin et al, 2015). However, evidence linking HRQL to future healthcare utilization, particularly in pediatric populations, has been limited (Kephart & Asada, 2009; Lima & Kopec, 2005; Parkerson et al, 2001; Ryan et al, 2013; Seid et al, 2004; Wherry, et al, 2014).…”

“…In addition, prior studies have demonstrated HRQL can serve as a predictor of future outcomes, such as prognosis and survival (Coates et al, 2000; Eton et al, 2003; Gotay, Kawamoto, Bottomley, & Efficace, 2008; Kaplan et al, 2007; Kephart & Asada, 2009; Lima & Kopec, 2005; Parkerson Jr, Harrell Jr, Hammond, & Wang, 2001; Ryan et al, 2013; Seid, Varni, Segall, & Kurtin, 2004; Terrin, Rodday, & Parsons, 2015; Wherry, Burns, & Leininger, 2014). Limited studies have shown that HRQL scores also predict healthcare utilization (Kephart & Asada, 2009; Lima & Kopec, 2005; Parkerson et al, 2001; Ryan et al, 2013; Seid et al, 2004; Wherry, et al, 2014); even fewer studies have focused on pediatric populations.…”

Assessing model fit in joint models of longitudinal and survival data with applications to cancer clinical trials

“…The TM in joint modeling for cancer vaccine trials in malignant melanoma has been considered in [9][10][11][12]. The TM models have been also used in quality-of-life studies [13][14][15][16], and in AIDS studies [17][18][19][20][21][22][23]. The SPM models have been used in other types of biomedical applications [24,25].…”