Short-Course Radiation plus Temozolomide in Elderly Patients with Glioblastoma.

BACKGROUND: Glioblastoma is associated with a poor prognosis in the elderly. Survival has been shown to increase among patients 70 years of age or younger when temozolomide chemotherapy is added to standard radiotherapy (60 Gy over a period of 6 weeks). In elderly patients, more convenient shorter courses of radiotherapy are commonly used, but the benefit of adding temozolomide to a shorter course of radiotherapy is unknown. METHODS: We conducted a trial involving patients 65 years of age or older with newly diagnosed glioblastoma. Patients were randomly assigned to receive either radiotherapy alone (40 Gy in 15 fractions) or radiotherapy with concomitant and adjuvant temozolomide. RESULTS: A total of 562 patients underwent randomization, 281 to each group. The median age was 73 years (range, 65 to 90). The median overall survival was longer with radiotherapy plus temozolomide than with radiotherapy alone (9.3 months vs. 7.6 months; hazard ratio for death, 0.67; 95% confidence interval [CI], 0.56 to 0.80; P<0.001), as was the median progression-free survival (5.3 months vs. 3.9 months; hazard ratio for disease progression or death, 0.50; 95% CI, 0.41 to 0.60; P<0.001). Among 165 patients with methylated O6-methylguanine-DNA methyltransferase (MGMT) status, the median overall survival was 13.5 months with radiotherapy plus temozolomide and 7.7 months with radiotherapy alone (hazard ratio for death, 0.53; 95% CI, 0.38 to 0.73; P<0.001). Among 189 patients with unmethylated MGMT status, the median overall survival was 10.0 months with radiotherapy plus temozolomide and 7.9 months with radiotherapy alone (hazard ratio for death, 0.75; 95% CI, 0.56 to 1.01; P=0.055; P=0.08 for interaction). Quality of life was similar in the two trial groups. CONCLUSIONS: In elderly patients with glioblastoma, the addition of temozolomide to short-course radiotherapy resulted in longer survival than short-course radiotherapy alone. (Funded by the Canadian Cancer Society Research Institute and others; ClinicalTrials.gov number, NCT00482677 .).

A global analysis of multitrial data investigating quality of life and symptoms as prognostic factors for survival in different tumor sites.

BACKGROUND: The objective of this study was to examine the prognostic value of baseline health-related quality of life (HRQOL) for survival with regard to different cancer sites using 1 standardized and validated patient self-assessment tool. METHODS: In total, 11 different cancer sites pooled from 30 European Organization for Research and Treatment of Cancer (EORTC) randomized controlled trials were selected for this study. For each cancer site, univariate and multivariate Cox proportional hazards modeling was used to assess the prognostic value (P< .05) of 15 HRQOL parameters using the EORTC Core Quality of Life Questionnaire (QLQ-C30). Models were adjusted for age, sex, and World Health Organization performance status and were stratified by distant metastasis. RESULTS: In total, 7417 patients completed the EORTC QLQ-C30 before randomization. In brain cancer, cognitive functioning was predictive for survival; in breast cancer, physical functioning, emotional functioning, global health status, and nausea and vomiting were predictive for survival; in colorectal cancer, physical functioning, nausea and vomiting, pain, and appetite loss were predictive for survival; in esophageal cancer, physical functioning and social functioning were predictive for survival; in head and neck cancer, emotional functioning, nausea and vomiting, and dyspnea were predictive for survival; in lung cancer, physical functioning and pain were predictive for survival; in melanoma, physical functioning was predictive for survival; in ovarian cancer, nausea and vomiting were predictive for survival; in pancreatic cancer, global health status was predictive for survival; in prostate cancer, role functioning and appetite loss were predictive for survival; and, in testis cancer, role functioning was predictive for survival. CONCLUSIONS: The current results demonstrated that, for each cancer site, at least 1 HRQOL domain provided prognostic information that was additive over and above clinical and sociodemographic variables.

Corrigendum to: Impact of lymphopenia on survival for elderly patients with glioblastoma: A secondary analysis of the CCTG CE.6 (EORTC 26062-22061, TROG 08.02) randomized clinical trial.

[This corrects the article DOI: 10.1093/noajnl/vdab153.].

Minimal important differences for interpreting health-related quality of life scores from the EORTC QLQ-C30 in lung cancer patients participating in randomized controlled trials.

BACKGROUND: The aim of this study was to determine the smallest changes in health-related quality of life (HRQOL) scores in a subset of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire core 30 (EORTC QLQ-C30) scales, which could be considered as clinically meaningful in patients with non-small-cell lung cancer (NSCLC). METHODS: WHO performance status (PS) and weight change were used as clinical anchors to determine minimal important differences (MIDs) in HRQOL change scores (range, 0-100) in the EORTC QLQ-C30 scales. Selected distribution-based methods were used for comparison. FINDINGS: In a pooled dataset of 812 NSCLC patients undergoing treatment, the values determined to represent the MID depended on whether patients were improving or deteriorating. MID estimates for improvement (based on a one-category change in PS, 5 - <20% weight gain) were physical functioning (9, 5); role functioning (14, 7); social functioning (5, 7); global health status (9, 4); fatigue (14, 5); and pain (16, 2). The respective MID estimates for deterioration (based on PS, weight loss) were physical (4, 6); role (5, 5); social (7, 9); global health status (4, 4); fatigue (6, 11); and pain (3, 7). INTERPRETATION: Based on the selected QLQ-C30 scales, the MID may depend upon whether the patients' PS is improving or worsening, but our results are not definitive. The MID estimates for the specified scales can help clinicians and researchers evaluate the significance of changes in HRQOL and assess the value of a health care intervention or compare treatments. The estimates also can be useful in determining sample sizes in the design of future clinical trials.

Impact of lymphopenia on survival for elderly patients with glioblastoma: A secondary analysis of the CCTG CE.6 (EORTC 26062-22061, TROG03.01) randomized clinical trial.

BACKGROUND: Lymphopenia may lead to worse outcomes for glioblastoma patients. This study is a secondary analysis of the CCTG CE.6 trial evaluating the impact of chemotherapy and radiation on lymphopenia, and effects of lymphopenia on overall survival (OS). METHODS: CCTG CE.6 randomized elderly glioblastoma patients (≥ 65 years) to short-course radiation alone (RT) or short-course radiation with temozolomide (RT + TMZ). Lymphopenia (mild-moderate: grade 1-2; severe: grade 3-4) was defined per CTCAE v3.0, and measured at baseline, 1 week and 4 weeks post-RT. Preselected key factors for analysis included age, sex, ECOG, resection extent, MGMT methylation, Mini-Mental State Examination, and steroid use. Multinomial logistic regression and multivariable Cox regression models were used to identify lymphopenia-associated factors and association with survival. RESULTS: Five hundred and sixty-two patients were analyzed (281 RT vs 281 RT+TMZ). At baseline, both arms had similar rates of mild-moderate (21.4% vs 21.4%) and severe (3.2% vs 2.9%) lymphopenia. However, at 4 weeks post-RT, RT+TMZ was more likely to develop lymphopenia (mild-moderate: 27.9% vs 18.2%; severe: 9.3% vs 1.8%; p<0.001). Developing any lymphopenia post-RT was associated with baseline lymphopenia (P < .001). Baseline lymphopenia (hazard ratio [HR] 1.3) was associated with worse OS (HR: 1.30, 95% confidence interval [CI] 1.05-1.62; P = .02), regardless of MGMT status. CONCLUSIONS: Development of post-RT lymphopenia is associated with addition of TMZ and baseline lymphopenia and not with RT alone in patients treated with short-course radiation. However, regardless of MGMT status, only baseline lymphopenia is associated with worse OS, which may be considered as a prognostic biomarker for elderly glioblastoma patients.

Baseline quality of life as a prognostic indicator of survival: a meta-analysis of individual patient data from EORTC clinical trials.

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 (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. FINDINGS: 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). INTERPRETATION: 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. FUNDING: Merck KGaA, EORTC Charitable Trust, and National Cancer Institute.

Translating the science of patient-reported outcomes assessment into clinical practice.

Patient-reported outcomes (PROs) are based on direct reporting by patients without the intervention of an observer. They include the self-assessment of functional status, symptoms, and other concerns such as needs and satisfaction with care. Health-related quality of life (HRQOL) assessment is a form of PRO and often includes both functional status and symptoms. The science underlying the assessment of HRQOL in clinical practice requires an understanding of the relationships between symptoms, functional status, and HRQOL, as well as instrument selection, and analysis and interpretation of the data. A modification of the Wilson and Cleary model is proposed to show the likelihood of bidirectional relationships between symptoms, functions, and HRQOL. Instrument selection should be based on the measurement properties of the instruments and patient populations in which they will be used. Analyses of data that allow a calculation of the proportion of patients who benefit from an intervention are preferred to analyses that show only the mean change in scores from baseline. HRQOL assessment in clinical practice has been shown to lead to a better understanding of patients' concerns with improvement in counseling and referral for required services. Potentially, HRQOL assessment should also be used to monitor the progress of a patient's disease and benefit from treatment.

Evaluating health-related quality of life in cancer clinical trials: the National Cancer Institute of Canada Clinical Trials Group experience.

INTRODUCTION: The National Cancer Institute of Canada (NCIC) Clinical Trials Group (CTG) Quality of Life (QOL) Committee was initiated in 1986. PURPOSE: The purpose of this review is to describe the evolution of the Committee's work and to highlight key developments such as the formulation of a policy regarding health-related quality-of-life (HRQOL) assessment, the provision of guidelines to ensure completion of HRQOL data within the protocol requirements, the rationale behind the choice of HRQOL instruments, the timing of assessments and the development of data analytic methods. These developments are illustrated with examples from CTG studies. RECOMMENDATIONS: There is a lack of concordance between conventional toxicity data and HRQOL data and comparative studies designed to elucidate these differences are to be encouraged. Also, more studies are required to compare different analytic strategies and to determine how much missing data is acceptable, particularly in oncology studies where attrition is inevitable.

Effects on quality of life of combined trastuzumab and chemotherapy in women with metastatic breast cancer.

PURPOSE: The study was designed to compare the effects of treatment with a combination of trastuzumab (Herceptin; Genentech, Inc, South San Francisco, CA) and chemotherapy versus chemotherapy alone on health-related quality of life (HRQL) in patients with HER-2/neu overexpressing, metastatic breast cancer. PATIENTS AND METHODS: A sample of 400 patients, not previously treated for metastatic disease and randomized to receive either trastuzumab plus chemotherapy (208 patients) or chemotherapy alone (192 patients), completed the European Organization for Research and Treatment Care Quality of Life Questionnaire at baseline and on at least one subsequent occasion at 8, 20, 32, 44, and 56 weeks. HRQL improvement or worsening was defined as a >or= 10-point change (range, 0 to 100 points) in the scores of six preselected domains (global quality of life [QOL], physical, role, social, and emotional functioning, and fatigue). Stable HRQL was defined as a change of less than 10. A Bonferroni correction was applied for multiple testing. RESULTS: After completion of chemotherapy, patients treated with trastuzumab and chemotherapy reported significant improvement in fatigue (P <.05) as compared with their baseline scores. Higher proportions of patients receiving the combined therapy achieved improvement in global QOL (P <.05) than did patients treated with chemotherapy alone. Higher proportions of the combined therapy group also achieved improvement in physical and role functioning and in fatigue as compared with the chemotherapy group, but the differences were not statistically significant. There were no differences in the proportions of patients in the two groups that reported worsening. CONCLUSION: Statistically significantly higher proportions of patients treated with a combination of trastuzumab and chemotherapy reported improved global QOL than did patients treated by chemotherapy alone.

Beyond the development of health-related quality-of-life (HRQOL) measures: a checklist for evaluating HRQOL outcomes in cancer clinical trials--does HRQOL evaluation in prostate cancer research inform clinical decision making?

PURPOSE: The aim of this study was to evaluate whether the inclusion of health-related quality of life (HRQOL), as a part of the trial design in a randomized controlled trial (RCT) setting, has supported clinical decision making for the planning of future medical treatments in prostate cancer. MATERIALS AND METHODS: A minimum standard checklist for evaluating HRQOL outcomes in cancer clinical trials was devised to assess the quality of the HRQOL reporting and to classify the studies on the grounds of their robustness. It comprises 11 key HRQOL issues grouped into four broader sections: conceptual, measurement, methodology, and interpretation. Relevant studies were identified in a number of databases, including MEDLINE and the Cochrane Controlled Trials Register. Both their HRQOL and traditional clinical reported outcomes were systematically analyzed to evaluate their consistency and their relevance for supporting clinical decision making. RESULTS: Although 54% of the identified studies did not show any differences in traditional clinical end points between treatment arms and 17% showed a difference in overall survival, 74% of the studies showed some difference in terms of HRQOL outcomes. One third of the RCTs provided a comprehensive picture of the whole treatment including HRQOL outcomes to support their conclusions. CONCLUSION: A minimum set of criteria for assessing the reported outcomes in cancer clinical trials is necessary to make informed decisions in clinical practice. Using a checklist developed for this study, it was found that HRQOL is a valuable source of information in RCTs of treatment in metastatic prostate cancer.

Quality of life in ovarian cancer patients: comparison of paclitaxel plus cisplatin, with cyclophosphamide plus cisplatin in a randomized study.

PURPOSE: Formal quality-of-life (QOL) assessments may contribute important information on patient symptoms. Despite many trials of systemic chemotherapy in ovarian cancer, reports of its effect on QOL are few. PATIENTS AND METHODS: QOL was assessed in an Intergroup randomized trial comparing paclitaxel plus cisplatin to cyclophosphamide plus cisplatin in women with advanced ovarian cancer. One hundred fifty-two eligible patients accrued in Canada completed the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C30 and a trial-specific checklist at baseline (after surgical debulking) and at regular intervals during and after chemotherapy. Mean change scores over time in the two arms were calculated. RESULTS: Compliance with QOL questionnaire completion was excellent (81% to 93%). In general, deterioration was seen in the QOL domains immediately after chemotherapy (day 8 of cycle 1), followed by clinically meaningful improvements compared with baseline (change scores > or = 10) in both arms during the treatment period in a number of domains and items, including global QOL, emotional function, social function, fatigue, pain, sleep, constipation, appetite, abdominal swelling, and abdominal cramps. Improvements in global QOL persisted for the duration of follow-up. More neurosensory effects and myalgia were documented in the paclitaxel arm; however, this did not adversely affect global or other domains of QOL and improved once chemotherapy was completed. CONCLUSION: Improvement from baseline in QOL measures was seen in both treatment arms. The greater neurologic and muscle toxicity of paclitaxel did not adversely influence QOL. QOL data can contribute useful information on the experience of symptoms and their time course, which may assist patients and physicians in their discussion about the anticipated effects of therapy.

Analysis and interpretation of health-related quality-of-life data from clinical trials: basic approach of The National Cancer Institute of Canada Clinical Trials Group.

Clinicians are being confronted with increasing amounts of health-related quality of life (HRQOL) data. Thus, there is a need for a greater understanding of the analysis and interpretation of HRQOL so that the data can be reported in an appropriate manner. The approach of the Clinical Trials Group of the National Cancer Institute of Canada emphasises the clinical meaning of the results, while avoiding complex statistical modelling. It consists of four steps: calculating the questionnaire completion rates, calculating the baseline scores, determining the individual change in scores over time for the domains specified in the trial hypothesis, and culminates in determining the proportions of patients who have reported clinically meaningful changes in scores since baseline. A rationale supporting each step is given. This approach is presented as a simple and practical aid to the analysis, interpretation and reporting of HRQOL results.

Use of liposomal anthracyclines in Kaposi's sarcoma.

Conventional chemotherapy regimens for the treatment of advanced Kaposi's sarcoma (KS) show limited efficacy and considerable toxicity. Liposomal anthracyclines with potential utility in KS include pegylated liposomal doxorubicin (Doxil/Caelyx [PLD]), daunorubicin citrate liposome (DaunoXome [DNX]), and nonpegylated liposomal doxorubicin (Myocet [NPLD]). Preclinical data showed that pegylated liposomes accumulate preferentially in highly vascularized KS lesions. In randomized clinical trials, PLD induced higher response rates than did the conventional combination chemotherapy regimens, bleomycin + vincristine (BV) and BV + conventional doxorubicin (ABV); DNX produced a response rate comparable to that of ABV. NPLD has not been compared with conventional chemotherapy for KS. PLD and DNX were associated with less toxicity compared with BV or ABV, including less alopecia and fewer gastrointestinal and neurologic side effects. Grade 3/4 myelosuppression was common with both PLD and DNX; stomatitis and infusion reactions occurred with PLD treatment, but hand-foot syndrome was relatively infrequent in the dose schedules used for KS. Health-related quality of life was improved in several domains in patients treated with PLD or DNX compared with ABV.

Methods to explain the clinical significance of health status measures.

One can classify ways to establish the interpretability of quality-of-life measures as anchor based or distribution based. Anchor-based measures require an independent standard or anchor that is itself interpretable and at least moderately correlated with the instrument being explored. One can further classify anchor-based approaches into population-focused and individual-focused measures. Population-focused approaches are analogous to construct validation and rely on multiple anchors that frame an individual's response in terms of the entire population (eg, a group of patients with a score of 40 has a mortality of 20%). Anchors for population-based approaches include status on a single item, diagnosis, symptoms, disease severity, and response to treatment. Individual-focused approaches are analogous to criterion validation. These methods, which rely on a single anchor and establish a minimum important difference in change in score, require 2 steps. The first step establishes the smallest change in score that patients consider, on average, to be important (the minimum important difference). The second step estimates the proportion of patients who have achieved that minimum important difference. Anchors for the individual-focused approach include global ratings of change within patients and global ratings of differences between patients. Distribution-based methods rely on expressing an effect in terms of the underlying distribution of results. Investigators may express effects in terms of between-person standard deviation units, within-person standard deviation units, and the standard error of measurement. No single approach to interpretability is perfect. Use of multiple strategies is likely to enhance the interpretability of any particular instrument.

Health-related quality of life and cancer clinical trials.

The measurement of patient-reported outcomes, including health-related quality of life, is a new initiative which has emerged and grown over the past four decades. Following the development of reliable and valid self-report questionnaires, health-related quality of life has been assessed in tens of thousands of patients and a wide variety of cancers. This review is based on a selection of data published in the last decade and is intended primarily for healthcare professionals. The assessments in clinical trials have been particularly useful for elucidating the effects of various cancers and their treatments on patients' lives and have provided additional information that enhances the usual clinical endpoints used for determining the benefits and toxicity of treatment. With growing experience the quality of the health-related quality of studies has improved and, in general, recent studies are more likely to be methodologically robust than those that were performed in earlier decades. Health-related quality of life has become a more accurate predictor of survival than some other clinical parameters, such as performance status. The overall outlook for the routine assessment of patient-reported outcomes in clinical trials is assured and, eventually, it is likely to become a standard part of clinical practice. However, there is still a need for a clear method for determining the clinical meaningfulness of changes in scores. The answer will probably come from the greater use of patient-reported outcomes and the consequent growth of experience that is necessary to make such judgements.

Patient self-reports of symptoms and clinician ratings as predictors of overall cancer survival.

BACKGROUND: The National Cancer Institute's Common Terminology Criteria for Adverse Events (NCI-CTCAE) reporting system is widely used by clinicians to measure patient symptoms in clinical trials. The European Organization for Research and Treatment of Cancer's Quality of Life core questionnaire (EORTC QLQ-C30) enables cancer patients to rate their symptoms related to their quality of life. We examined the extent to which patient and clinician symptom scoring and their agreement could contribute to the estimation of overall survival among cancer patients. METHODS: We analyzed baseline data regarding six cancer symptoms (pain, fatigue, vomiting, nausea, diarrhea, and constipation) from a total of 2279 cancer patients from 14 closed EORTC randomized controlled trials. In each trial that was selected for retrospective pooled analysis, both clinician and patient symptom scoring were reported simultaneously at study entry. We assessed the extent of agreement between clinician vs patient symptom scoring using the Spearman and kappa correlation statistics. After adjusting for age, sex, performance status, cancer severity, and cancer site, we used Harrell concordance index (C-index) to compare the potential for clinician-reported and/or patient-reported symptom scores to improve the accuracy of Cox models to predict overall survival. All P values are from two-sided tests. RESULTS: Patient-reported scores for some symptoms, particularly fatigue, did differ from clinician-reported scores. For each of the six symptoms that we assessed at baseline, both clinician and patient scorings contributed independently and positively to the predictive accuracy of survival prognostication. Cox models of overall survival that considered both patient and clinician scores gained more predictive accuracy than models that considered clinician scores alone for each of four symptoms: fatigue (C-index = .67 with both patient and clinician data vs C-index = .63 with clinician data only; P <.001), vomiting (C-index = .64 vs .62; P = .01), nausea (C-index = .65 vs .62; P < .001), and constipation (C-index = .62 vs .61; P = .01). CONCLUSION: Patients provide a subjective measure of symptom severity that complements clinician scoring in predicting overall survival.

Examining the relationships among health-related quality-of-life indicators in cancer patients participating in clinical trials: a pooled study of baseline EORTC QLQ-C30 data.

AIMS: Cancer patients experience multiple and concurrent health-related problems and symptoms due to their illness and therapies. The first objective of this analysis was to identify how health-related quality-of-life (HRQoL) indicators cluster among cancer patients and how possible clusters change across patients with different sociodemographic and clinical characteristics. The second objective of this study was to identify which HRQoL indicators are linked to patients' perception of overall quality of life. METHODS: Retrospective pooling of 30 closed randomized European Organisation for Research and Treatment of Cancer (EORTC) clinical trials yielded baseline EORTC Quality of Life Core Questionnaire (QLQ-C30) HRQoL data for a total of 7417 patients. A cluster analysis was performed to determine how the 15 HRQoL indicators obtained with the QLQ-C30 cluster overall and by patient characteristics. RESULTS: Three main clusters emerged from the overall dataset: a physical cluster, a psychological cluster and a gastrointestinal cluster. The same clusters were found in subgroups defined according to sociodemographic and clinical characteristics, while some differences emerged among cancer sites. The Global Health scale was found to be part of the physical cluster in the overall dataset. This result was consistent across different levels of disease severity, while divergent results were seen across some cancer sites. CONCLUSION: Our findings suggest that HRQoL indicators are interrelated. Understanding these relationships may aid clinicians in managing the symptom burden experienced by patients, as well as policy-makers, in defining psychosocial support plans.

Meta-analysis provides evidence-based effect sizes for a cancer-specific quality-of-life questionnaire, the FACT-G.

OBJECTIVE: To compare Cohen's guidelines for small (0.2), medium (0.5), and large (0.8) effect sizes with empirical estimates for a cancer-specific health-related quality-of-life questionnaire (HRQOL), the Functional Assessment of Cancer Therapy - General (FACT-G). METHODS: Seventy-one papers satisfied inclusion criteria for meta-analysis. Blinded to the HRQOL results, three "experts" (with expertise in interpreting the FACT-G questionnaire and managing cancer patients), predicted the relative magnitude of HRQOL mean differences. Size classes (small, medium, large) were defined in terms of relevance to clinical decision making. The experts worked independently and based their predictions on patient characteristics and clinical circumstances. Their judgments were linked with FACT-G results and inverse-variance-weighted mean effect sizes calculated for each size class. RESULTS: At least two experts were perfectly concordant and up to one was discordant by at most one size category for 833 of the mean differences; for these, weighted kappas were generally in the "substantial" range (0.60-0.79). Of these mean differences, 617 were cross-sectional; small, medium, and large mean effect sizes were physical well-being 0.42, 0.87, 1.6; functional well-being 0.37, 0.71, 1.6; emotional well-being 0.32, 0.40, no large differences; and social well-being 0.14, 0.23, no large differences. Two hundred and sixteen longitudinal mean differences yielded small and medium effect sizes: physical well-being 0.26, 0.34; functional well-being 0.14, 0.28; emotional well-being 0.27, 0.23; and social well-being 0.08, 0.01. There was virtually no evidence for large longitudinal effects. CONCLUSION: These results provide specific, evidence-based alternatives to Cohen's generic guidelines, for use in sample-size calculations for the FACT-G and interpretation of the clinical significance of effects measured with FACT-G.

Meta-analysis provides evidence-based interpretation guidelines for the clinical significance of mean differences for the FACT-G, a cancer-specific quality of life questionnaire.

Our aim was to develop evidence-based interpretation guidelines for the Functional Assessment of Cancer Therapy-General (FACT-G), a cancer-specific health-related quality of life (HRQOL) instrument, from a range of clinically relevant anchors, incorporating expert judgment about clinical significance. Three clinicians with many years' experience managing cancer patients and using HRQOL outcomes in clinical research reviewed 71 papers. Blinded to the FACT-G results, they considered the clinical anchors associated with each FACT-G mean difference, predicted which dimensions of HRQOL would be affected, and whether the effects would be trivial, small, moderate, or large. These size classes were defined in terms of clinical relevance. The experts' judgments were then linked with FACT-G mean differences, and inverse-variance weighted mean differences were calculated for each size class. Small, medium, and large differences (95% confidence interval) from 1,118 cross-sectional comparisons were as follows: physical well-being 1.9 (0.6-3.2), 4.1 (2.7-5.5), 8.7 (5.2-12); functional well-being 2.0 (0.5-3.5), 3.8 (2.0-5.5), 8.8 (4.3-13); emotional well-being 1.0 (0.1-2.6), 1.9 (0.3-3.5), no large differences; social well-being 0.7 (-0.7 to 2.1), 0.8 (-2.9 to 4.5), no large differences. Results from 436 longitudinal comparisons tended to be smaller than the corresponding cross-sectional results. These results augment other interpretation guidelines for FACT-G with information on sample size, power calculations, and interpretation of cancer clinical trials that use FACT-G.