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Quality of Life Research

, Volume 24, Issue 1, pp 31–39 | Cite as

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

  • Norma Terrin
  • Angie Mae Rodday
  • Susan K. Parsons
Quantitative Methods Special Section

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. 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.

Results

Hazard ratios per one-half standard deviation difference in emotional, physical, and role functioning, and global HRQL were 0.61 (95 % CI 0.46–0.81; 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.

Conclusions

HRQL trajectories were predictive of TRM in pediatric HSCT, even after adjusting the survival outcome for baseline characteristics.

Keywords

Hematopoietic stem cell transplant Health-related quality of life Joint model for longitudinal and time-to-event outcomes Shared parameter model Transplant-related mortality Survival 

Notes

Acknowledgments

This work was supported by the National Center for Advancing Translational Sciences, National Institutes of Health (NIH), Grant Number UL1 TR000073 through Tufts Clinical and Translational Science Institute (CTSI), and the NIH National Cancer Institute Grant R21 CA152628 through the Institute for Clinical Research and Health Policy Studies (ICRHPS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Norma Terrin
    • 1
    • 2
    • 3
  • Angie Mae Rodday
    • 1
    • 3
  • Susan K. Parsons
    • 1
    • 3
  1. 1.Institute for Clinical Research and Health Policy StudiesTufts Medical CenterBostonUSA
  2. 2.Tufts Clinical and Translational Science InstituteTufts UniversityBostonUSA
  3. 3.Department of MedicineTufts University School of MedicineBostonUSA

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