title: Baseline quality of life as a prognostic indicator of survival: a meta-analysis of individual patient data from EORTC clinical trials.
abstract: BACKGROUND
  Although individual studies show that baseline health-related quality of life (HRQOL) is a prognostic factor for survival, contradictory results have been published and very few meta-analyses have confirmed the finding. We examined whether HRQOL scores are associated with survival when pooled across a large sample of patients with different disease sites.
  
  
  METHODS
  We selected 30 randomised controlled trials from the European Organisation for Research and Treatment of Cancer (EORTC) started between 1986 and 2004, which included survival data for 10 108 patients with 11 different cancer sites. Patients were eligible for inclusion if they had completed a baseline HRQOL assessment with the EORTC core quality of life questionnaire (QLQ-C30; n=7417). Sociodemographic variables were age (<or=60 vs >60 years) and sex (men vs women), and clinical variables were WHO performance status (0-1 vs 2-3), distant metastases (no vs yes), and cancer site. We assessed prognostic significance of sociodemographic and clinical variables and the 15 QLQ-C30 scales with Cox proportional hazard models.
  
  
  RESULTS
  In the stratified multivariate model including sociodemographic, clinical, and HRQOL data, the HRQOL parameters of physical functioning (hazard ratio 0.94, 95% CI 0.92-0.96; p<0.0001), pain (1.04, 1.02-1.06; p<0.0001), and appetite loss (1.05, 1.03-1.06; p<0.0001) provided significant prognostic information in addition to the parameters age (1.17, 1.06-1.28; p=0.0001), sex (0.74, 0.67-0.82; p<0.0001), and distant metastases (1.70, 1.49-1.93; p<0.0001), but not for WHO performance status (1.07, 0.97-1.19; p=0.11). Consideration of the three HRQOL parameters and sociodemographic and clinical data increased the predictive accuracy of prognosis of overall survival by 6% relative to sociodemographic and clinical characteristics alone (C statistic for concordance between predicted and observed overall survival 0.68 for sociodemographic and clinical variables, and 0.72 for sociodemographic, clinical, and HRQOL variables).
  
  
  CONCLUSIONS
  The results suggest that HRQOL scales provide prognostic information in addition to that of sociodemographic and clinical measures. This study shows that HRQOL data can help to predict survival in patients with cancer.
  
  
  BACKGROUND
  Merck KGaA, EORTC Charitable Trust, and National Cancer Institute.
authors.affiliation: Quality of Life Department, EORTC Headquarters, Brussels, Belgium. chantal.quinten@eortc.be
authors.forename: Chantal
authors.initials: C
authors.lastname: Quinten
authors.forename: Corneel
authors.initials: C
authors.lastname: Coens
authors.forename: Murielle
authors.initials: M
authors.lastname: Mauer
authors.forename: Sylvie
authors.initials: S
authors.lastname: Comte
authors.forename: Mirjam A G
authors.initials: MA
authors.lastname: Sprangers
authors.forename: Charles
authors.initials: C
authors.lastname: Cleeland
authors.forename: David
authors.initials: D
authors.lastname: Osoba
authors.forename: Kristin
authors.initials: K
authors.lastname: Bjordal
authors.forename: Andrew
authors.initials: A
authors.lastname: Bottomley
mesh_terms: D005060:Europe / Q000453:epidemiology; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D053208:Kaplan-Meier Estimate; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009369:Neoplasms / Q000175:diagnosis / Q000401:mortality*; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D011788:Quality of Life; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D015996:Survival Rate