Establishing the European Norm for the health-related quality of life domains of the computer-adaptive test EORTC CAT Core.

OBJECTIVE: The computer-adaptive test (CAT) of the European Organisation for Research and Treatment of Cancer (EORTC), the EORTC CAT Core, assesses the same 15 domains as the EORTC QLQ-C30 health-related quality of life questionnaire but with increased precision, efficiency, measurement range and flexibility. CAT parameters for estimating scores have been established based on clinical data from cancer patients. This study aimed at establishing the European Norm for each CAT domain based on general population data. METHODS: We collected representative general population data across 11 European Union (EU) countries, Russia, Turkey, Canada and the United States (n ≥ 1000/country; stratified by sex and age). We selected item subsets from each CAT domain for data collection (totalling 86 items). Differential item functioning (DIF) analyses were conducted to investigate cross-cultural measurement invariance. For each domain, means and standard deviations from the EU countries (weighted by country population, sex and age) were used to establish a T-metric with a European general population mean = 50 (standard deviation = 10). RESULTS: A total of 15,386 respondents completed the online survey (n = 11,343 from EU countries). EORTC CAT Core norm scores for all 15 countries were calculated. DIF had negligible impact on scoring. Domain-specific T-scores differed significantly across countries with small to medium effect sizes. CONCLUSION: This study establishes the official European Norm for the EORTC CAT Core. The European CAT Norm can be used globally and allows for meaningful interpretation of scores. Furthermore, CAT scores can be compared with sex- and age-adjusted norm scores at a national level within each of the 15 countries.

General population normative data for the EORTC QLQ-C30 health-related quality of life questionnaire based on 15,386 persons across 13 European countries, Canada and the Unites States.

OBJECTIVE: The European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 health-related quality of life questionnaire is one of the most widely used cancer-specific health-related quality of life questionnaires worldwide. General population norm data can facilitate the interpretation of QLQ-C30 data obtained from cancer patients. This study aimed at systematically collecting norm data from the general population to develop European QLQ-C30 norm scores and to generate comparable norm data for individual countries in Europe and North America. METHODS: We collected QLQ-C30 data from the general population across 11 European Union (EU) countries, Russia, Turkey, Canada and United States (n ≥ 1000/country). Representative samples were stratified by sex and age groups (18-39, 40-49, 50-59, 60-69 and ≥ 70 years). After applying weights based on the United Nations population distribution statistics, we calculated QLQ-C30 domain scores to generate a 'European QLQ-C30 Norm' based on the EU countries. Further, we calculated QLQ-C30 norm scores for all 15 individual countries. RESULTS: A total of 15,386 respondents completed the online survey. For the EU sample, most QLQ-C30 domains showed differences by sex/age, with men scoring somewhat better health than women, while age effects varied across domains. Substantially larger differences were seen in inter-country comparisons, with Austrian and Dutch respondents reporting consistently better health compared with British and Polish respondents. CONCLUSIONS: This study is the first to systematically collect EORTC QLQ-C30 general population norm data across Europe and North America applying a consistent data collection method across 15 countries. These new norm data facilitate valid intra-country as well as inter-country comparisons and QLQ-C30 score interpretation.

QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30.

PURPOSE: To derive a health state classification system (HSCS) from the cancer-specific quality of life questionnaire, the EORTC QLQ-C30, as the basis for a multi-attribute utility instrument. METHODS: The conceptual model for the HSCS was based on the established domain structure of the QLQ-C30. Several criteria were considered to select a subset of dimensions and items for the HSCS. Expert opinion and patient input informed a priori selection of key dimensions. Psychometric criteria were assessed via secondary analysis of a pooled dataset comprising HRQOL and clinical data from 2616 patients from eight countries and a range of primary cancer sites, disease stages, and treatments. We used confirmatory factor analysis (CFA) to assess the conceptual model's robustness and generalisability. We assessed item floor effects (>75 % observations at lowest score), disordered item response thresholds, coverage of the latent variable and differential item function using Rasch analysis. We calculated effect sizes for known group comparisons based on disease stage and responsiveness to change. Seventy-nine cancer patients assessed the relative importance of items within dimensions. RESULTS: CFA supported the conceptual model and its generalisability across primary cancer sites. After considering all criteria, 12 items were selected representing 10 dimensions: physical functioning (mobility), role functioning, social functioning, emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems. CONCLUSIONS: The HSCS created from QLQ-C30 items is known as the EORTC Quality of Life Utility Measure-Core 10 dimensions (QLU-C10D). The next phase of the QLU-C10D's development involves valuation studies, currently planned or being conducted across the globe.

Using a discrete choice experiment to value the QLU-C10D: feasibility and sensitivity to presentation format.

PURPOSE: To assess the feasibility of using a discrete choice experiment (DCE) to value health states within the QLU-C10D, a utility instrument derived from the QLQ-C30, and to assess clarity, difficulty, and respondent preference between two presentation formats. METHODS: We ran a DCE valuation task in an online panel (N = 430). Respondents answered 16 choice pairs; in half of these, differences between dimensions were highlighted, and in the remainder, common dimensions were described in text and differing attributes were tabulated. To simplify the cognitive task, only four of the QLU-C10D's ten dimensions differed per choice set. We assessed difficulty and clarity of the valuation task with Likert-type scales, and respondents were asked which format they preferred. We analysed the DCE data by format with a conditional logit model and used Chi-squared tests to compare other responses by format. Semi-structured telephone interviews (N = 8) explored respondents' cognitive approaches to the valuation task. RESULTS: Four hundred and forty-nine individuals were recruited, 430 completed at least one choice set, and 422/449 (94 %) completed all 16 choice sets. Interviews revealed that respondents found ten domains difficult but manageable, many adopting simplifying heuristics. Results for clarity and difficulty were identical between formats, but the "highlight" format was preferred by 68 % of respondents. Conditional logit parameter estimates were monotonic within domains, suggesting respondents were able to complete the DCE sensibly, yielding valid results. CONCLUSION: A DCE valuation task in which only four of the QLU-C10D's ten dimensions differed in any choice set is feasible for deriving utility weights for the QLU-C10D.

Testing the measurement invariance of the EORTC QLQ-C30 across primary cancer sites using multi-group confirmatory factor analysis.

PURPOSE: The EORTC Quality of Life Questionnaire is a widely used cancer-specific quality of life instrument comprising a core set of 30 items (QLQ-C30) supplemented by cancer site-specific modules. The purpose of this paper was to examine the extent to which the conventional multi-item domain structure of the QLQ-C30 holds across patients with seven different primary cancer sites. METHODS: Multi-group confirmatory factor analysis was used to test whether a measurement model of the QLQ-C30 was invariant across cancer sites. Configural (same patterns of factor loadings), metric (equivalence of factor loadings) and scalar (equivalence of thresholds) invariance amongst the cancer site groups were assessed (N = 1,906) by comparing the fit of a model with these parameters freely estimated to a model where estimates were constrained to be equal for the corresponding items in each group. RESULTS: All groups exhibited good model fit except for the prostate group, which was excluded. Only 1 of 576 parameters was found to differ between primary sites: specifically, the first threshold of Item 1 in the breast cancer group exhibited non-invariance. In a post hoc analysis, several instances of non-invariance by treatment status (baseline, on-treatment, off-treatment) were observed. CONCLUSIONS: Given only one instance of non-invariance between cancer sites, there is a reason to be confident in the validity of conclusions drawn when comparing QLQ-C30 domain scores between different sites and when interpreting the scores of heterogeneous samples, although future research should assess the potential impact of confounding variables such as treatment and gender.

Can individual patients assess differences in quality of life between groups of patients?

This qualitative study piloted a method for eliciting patient opinion on the size of group differences in quality of life (QOL) scores from the EORTC QLQ-C30. Using scenarios from published studies, patients were asked the differences in QOL they would expect between two groups of patients or a group of patients over time. Interviews were transcribed verbatim and thematic analysis used. Eleven breast cancer patients were interviewed. The final thematic framework consisted of three major themes: (1) their ability to use published data to judge the size of differences in QOL scores, (2) their ability to gain familiarity and understanding of the QLQ-C30 questionnaire in an interview situation and (3) their ability to understand and assess differences from a group of patients rather than on an individual basis. Patients felt able to understand the questionnaire and scoring. They provided an opinion on whether differences in QOL scores were trivial, small, medium or large. Patient perspectives were often based on their own experience of the disease and treatments and their opinions were varied. In order to estimate clinically meaningful differences from published literature, a larger number of patients with varied experiences would be required and a consensus process used to align opinions where possible.

Evidence-based guidelines for interpreting change scores for the European Organisation for the Research and Treatment of Cancer Quality of Life Questionnaire Core 30.

AIM: To use published literature and experts' opinion to investigate the clinical meaning and magnitude of changes in the Quality of Life (QOL) of groups of patients measured with the European Organisation for the Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30). METHODS: An innovative method combining systematic review of published studies, expert opinions and meta-analysis was used to estimate large, medium, and small mean changes over time for QLQ-C30 scores. RESULTS: Nine hundred and eleven papers were identified, leading to 118 relevant papers. One thousand two hundred and thirty two mean changes in QOL over time were combined in the meta-analysis, with timescales ranging from four days to five years. Guidelines were produced for trivial, small, and medium size classes, for each subscale and for improving and declining scores separately. Estimates for improvements were smaller than respective estimates for declines. CONCLUSIONS: These guidelines can be used to aid sample size calculations and interpretation of mean changes over time from groups of patients. Observed mean changes in the QLQ-C30 scores are generally small in most clinical situations, possibly due to response shift. Careful consideration is needed when planning studies where QOL changes over time are of primary interest; the timing of follow up, sample attrition, direction of QOL changes, and subscales of primary interest are key considerations.

Is there a genetic cause for cancer cachexia? - a clinical validation study in 1797 patients.

BACKGROUND: Cachexia has major impact on cancer patients' morbidity and mortality. Future development of cachexia treatment needs methods for early identification of patients at risk. The aim of the study was to validate nine single-nucleotide polymorphisms (SNPs) previously associated with cachexia, and to explore 182 other candidate SNPs with the potential to be involved in the pathophysiology. METHOD: A total of 1797 cancer patients, classified as either having severe cachexia, mild cachexia or no cachexia, were genotyped. RESULTS: After allowing for multiple testing, there was no statistically significant association between any of the SNPs analysed and the cachexia groups. However, consistent with prior reports, two SNPs from the acylpeptide hydrolase (APEH) gene showed suggestive statistical significance (P=0.02; OR, 0.78). CONCLUSION: This study failed to detect any significant association between any of the SNPs analysed and cachexia; although two SNPs from the APEH gene had a trend towards significance. The APEH gene encodes the enzyme APEH, postulated to be important in the endpoint of the ubiquitin system and thus the breakdown of proteins into free amino acids. In cachexia, there is an extensive breakdown of muscle proteins and an increase in the production of acute phase proteins in the liver.

Influence from genetic variability on opioid use for cancer pain: a European genetic association study of 2294 cancer pain patients.

Cancer pain patients need variable opioid doses. Preclinical and clinical studies suggest that opioid efficacy is related to genetic variability. However, the studies have small samples, findings are not replicated, and several candidate genes have not been studied. Therefore, a study of genetic variability with opioid doses in a large population using a confirmatory validation population was warranted. We recruited 2294 adult European patients using a World Health Organization (WHO) step III opioid and analyzed single nucleotide polymorphisms (SNPs) in genes with a putative influence on opioid mechanisms. The patients' mean age was 62.5 years, and the average pain intensity was 3.5. The patients' primary opioids were morphine (n=830), oxycodone (n=446), fentanyl (n=699), or other opioids (n=234). Pain intensity, time on opioids, age, gender, performance status, and bone or CNS metastases predicted opioid dose and were included as covariates. The patients were randomly divided into 1 development sample and 1 validation sample. None of 112 SNPs in the 25 candidate genes OPRM1, OPRD1, OPRK1, ARRB2, GNAZ, HINT1, Stat6, ABCB1, COMT, HRH1, ADRA2A, MC1R, TACR1, GCH1, DRD2, DRD3, HTR3A, HTR3B, HTR2A, HTR3C, HTR3D, HTR3E, HTR1, or CNR1 showed significant associations with opioid dose in both the development and the validation analyzes. These findings do not support the use of pharmacogenetic analyses for the assessed SNPs to guide opioid treatment. The study also demonstrates the importance of validating findings obtained in genetic association studies to avoid reporting spurious associations as valid findings. To elicit knowledge about new genes that influence pain and the need for opioids, strategies other than the candidate gene approach is needed.

The use of differential item functioning analyses to identify cultural differences in responses to the EORTC QLQ-C30.

INTRODUCTION: The European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 is a widely used health-related quality of life instrument. The main aim of this study is to investigate whether there are international differences in response to the questionnaire that can be explained by cultural factors. METHODS: Analyses involved a database of 106 separate studies including data from over 28,000 respondents. Differential item functioning (DIF) analyses using logistic regression were conducted for each item of the EORTC QLQ-C30 with respect to cultural/geographic group. Results were qualitatively compared with previously reported DIF analyses by translation to explore whether the source of the DIF was more likely to be linguistic or cultural in nature. RESULTS: Although most response patterns were similar, there were a number of international differences in how the questionnaire was answered. The largest variations were found in the results for Eastern Europe and East Asia. Results for the UK, the US and Australia tended to be similar. Many of the European results followed patterns that were more clearly explained when grouped by translation than when grouped by geographical region. DISCUSSION: Our results suggest that, in general, the EORTC QLQ-C30 is suitable for use in a wide variety of countries and settings. Some response variations that have the potential to affect the results of international studies were identified, but it was not always clear whether the source of the variation was primarily linguistic or cultural.

Methods for handling missing data in palliative care research.

Missing data is a common problem in palliative care research due to the special characteristics (deteriorating condition, fatigue and cachexia) of the population. Using data from a palliative study, we illustrate the problems that missing data can cause and show some approaches for dealing with it. Reasons for missing data and ways to deal with missing data (including complete case analysis, imputation and modelling procedures) are explored. Possible mechanisms behind the missing data are: missing completely at random, missing at random or missing not at random. In the example study, data are shown to be missing at random. Imputation of missing data is commonly used (including last value carried forward, regression procedures and simple mean). Imputation affects subsequent summary statistics and analyses, and can have a substantial impact on estimated group means and standard deviations. The choice of imputation method should be carried out with caution and the effects reported.

Comparing translations of the EORTC QLQ-C30 using differential item functioning analyses.

The European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 is one of the most widely used quality of life instruments for cancer patients. The aim of this study was to assess whether there were linguistic differences in the way an international sample answered the EORTC QLQ-C30 questionnaire. Thirteen translations of the EORTC QLQ-C30, representing 22 countries, were investigated using a database of 27,891 respondents, incorporating 103 separate studies. Differential item functioning (DIF) analyses were conducted using logistic regression to identify items which, after controlling for subscale, were answered differently by language of administration. Both uniform and non-uniform DIF were assessed. Although most languages showed similar results to English, at least one instance of statistically significant DIF was identified for each translation, and a few of these differences were large. In some cases, the patterns were supported by the results of qualitative interviews with bilingual people. Although, overall, there appeared to be good linguistic equivalence for most of the EORTC QLQ-C30 items, several scales showed strongly discrepant results for some translations. Some of these effects are large enough to impact on the results of clinical trials. Based on our experience in this study, we suggest that validation of translations of health-related quality of life instruments should include exploration of DIF.

A prospective study of health-related quality of life, fatigue, anxiety and depression 3-5 years after stem cell transplantation.

Health-related quality of life (HRQOL), fatigue and psychological distress were prospectively assessed in 248 cancer patients treated with allogeneic (SCT, N=61), or autologous (ASCT, N=69) stem cell transplantation or conventional chemotherapy (CT, N=118) of whom 128 completed the assessments after 3 years. The European Organization for Treatment and Research of Cancer Core Quality of Life Questionnaire and the Hospital Anxiety and Depression Scale were administered nine (SCT/ASCT groups) or seven times (CT group) during the first year. The Fatigue Questionnaire was added at the final assessment. The SCT group displayed greater changes from baseline scores than the ASCT group, with more symptoms in the first months post transplant. A gradual improvement was found in both groups during the following 4-6 months, before stabilizing at baseline levels. Only minor changes were observed after the first year. All groups reported more fatigue than the population values after 3 years (P<0.01). The ASCT group also reported less optimal HRQOL (P<0.01-0.0001). No differences were found in anxiety and depression. Despite a faster recovery during the first months after transplant, the ASCT patients reported poorer functioning and more fatigue compared to the SCT group after 3 years. This suggests a need for a closer follow-up of these patients with special emphasis on functional status and fatigue.

Lack of concealment may lead to selection bias in cluster randomized trials of palliative care.

Comprehensive palliative care programs are often implemented on a community level, and to evaluate such interventions, randomization by cluster (community) may be the only feasible method. In trials randomizing individual subjects, the importance of proper concealment has been stressed. In cluster randomized trials, however, concealment of individual patient allocation is often impossible. The following risk of selection bias has been given little attention. In the present study, comparing palliative care to conventional care, community health care districts were defined as clusters and randomized. The patients' treatment assignment was determined by the allocation of the cluster in which they resided, and hence predictable by their address. A biased selection based on practical considerations related to patients' diagnoses and hospital departments was suspected. To explore this, cancer diagnoses were grouped according to local tradition for sharing of treatment responsibility among hospital departments. A significant difference between trial arms in distribution of these groups was revealed and strongly supported our suspicion. The finding carries an important message to future researchers: when using cluster randomization, any evidence of selection bias should be carefully checked and reported.

Cluster-randomized trials.

Cluster-randomized trials represent an important experimental design, supplementing ordinary randomized clinical trials. They are particularly relevant when evaluating interventions at the level of clinic, hospital, district or region. They are necessary when it is not feasible to randomize individual patients, and desirable when there may be contamination between clusters. But they also carry serious design and analysis implications, and the use of clusters as the unit of randomization must be justified. Sample sizes will usually need to be greatly increased, an adequate number of clusters is essential, and the statistical analysis must allow for the cluster design. And one should rigorously guard against selection bias.