Braun et al. BMC Cancer 2011, 11:353 http://www.biomedcentral.com/1471-2407/11/353 Table 6 Multivariate Cox regression analysis for global QoL (Block Entry Method) QoL Domain Global Gender (male as reference) Stage at diagnosis (locoregional disease as reference) Prior treatment history (previously treated as reference) inherent in incorporating QoL measurements into busy clinical practices [5]. This will continue as long as QoL assessment is regarded as peripheral to the goal of stan- dard clinical cancer therapy. It is our contention, how- ever, that QoL is as meaningful to patients as length of life, a situation which is coming to be appreciated by patients, advocates, insurance providers, and employers. Thus, there is a strong rationale to incorporate regular QoL assessment and management into all oncologic practice settings. In the current study, we chose QLQ-C30 as a valid and reliable tool to assess patient QoL which is appro- priate for assessing large, heterogeneous patient popula- tions. It is relevant that this instrument concentrates on a patients’ ability to fulfill the activities of daily living. While this emphasis has been regarded in research stu- dies as an appropriate way to assess the effectiveness of new drugs or novel combinations of agents, cumulative experience from several decades of use of the QLQ-C30 also demonstrate the relevance of this information for clinical practitioners. Thus, results from the QLQ-C30 provide important information to guide treatment deci- sions based on balancing the need to control tumor pro- gression with the need to know how treatment affects the patient’s capacity to fulfill the activities of their daily life at work and in the home. In this study, we found that global QoL as well as physical function were significant predictors of patient survival in the entire study population. The finding of physical function predicting survival in lung cancer has been reported by previous studies [11,20,39-41]. For example, Ganz et al. reported a statistically significant relationship between initial patient-rated QoL scores and subsequent survival [40]. In another study by Hern- don et al., increased pain, appetite loss, fatigue, lung car- cinoma symptoms, poorer overall QoL, and poorer physical functioning predicted significantly poorer survi- val. However, only the pain score was retained in the final multivariate model [20]. Finally, a study conducted by Fielding and Wong found that better physical func- tioning and better appetite were predictive of longer survival after fully adjusting for sociodemographic and clinical factors in Chinese lung cancer patients [39]. The finding of global QoL predicting survival in lung cancer has also been reported by previous studies [15,20,22,25,41-43]. For example, Langendijk et al. Page 6 of 9 P-value < 0.001 0.001 < 0.001 < 0.001 HR 0.991 0.78 1.72 0.52 95% CI 0.989 to 0.994 0.68 to 0.91 1.5 to 1.9 0.45 to 0.61 examined the significance of pretreatment QoL and symptom scores in lung cancer patients treated with high dose radiotherapy and found the EORTC global QoL score as the strongest prognostic factor [22]. Mon- tazeri et al. showed that pre-diagnosis EORTC global QoL was a significant predictor of survival in lung can- cer [25]. Dharma-Wardene et al. found that baseline FACT-G total score is a statistically significant predictor of survival in patients with advanced lung cancer [15]. Brown et al. found EORTC global QoL, role functioning, fatigue, appetite loss, and constipation as prognostic indicators of survival in patients with NSCLC at 12 weeks [42]. Maione et al. found that only pretreatment EORTC global QoL and instrumental activities of daily living scores were significant predictors for survival in elderly patients with advanced non-small-cell lung can- cer treated with chemotherapy [43]. In the previously published studies mentioned above, the most commonly adjusted variables were age, gender, socioeconomic status, performance status, various cell counts, metastasis, treatment types, cancer stage, histol- ogy subtypes, sites involved, and weight loss. However, it is worth mentioning that some of these studies failed to provide adequate controls for confounding, for exam- ple, there is no information on the extent of the disease or whether the tumor size shrank in response to therapy [15,22,32]. In most of these studies, it is not possible to determine whether QoL independently predicted survi- val or simply reflected the progress of the underlying disease. For example, it has been suggested that physical functioning might be a surrogate marker for an unrec- ognized biological prognostic indicator, so a causal asso- ciation between physical functioning and survival time should not be inferred [28]. It is to be noted that in our study, several QoL func- tion and symptom scales that were predictive of survival on univariate analysis, lost their statistical significance after the basic demographic and clinical factors were adjusted for. Only physical and global function retained their statistical significance in multivariate modeling. Collectively, these results demonstrate that after the potential confounding factors are adjusted for in the multivariate analysis, the prognostic effects of QoL either disappear or are mitigated significantly. Thus, before making any claims regarding the independent prognostic effects of QoL in cancer, adequate statistical