Minimally important differences for the EORTC QLQ-C30 in prostate cancer clinical trials.
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ABSTRACT: Background
The aim of the study was to estimate the minimally important difference (MID) for interpreting group-level change over time, both within a group and between groups, for the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) scores in patients with prostate cancer.Methods
We used data from two published EORTC trials. Clinical anchors were selected by strength of correlations with QLQ-C30 scales. In addition, clinicians' input was obtained with regard to plausibility of the selected anchors. The mean change method was applied for interpreting change over time within a group of patients and linear regression models were fitted to estimate MIDs for between-group differences in change over time. Distribution-based estimates were also evaluated.Results
Two clinical anchors were eligible for MID estimation; performance status and the CTCAE diarrhoea domain. MIDs were developed for 7 scales (physical functioning, role functioning, social functioning, pain, fatigue, global quality of life, diarrhoea) and varied by scale and direction (improvement vs deterioration). Within-group MIDs ranged from 4 to 14 points for improvement and - 13 to - 5 points for deterioration and MIDs for between-group differences in change scores ranged from 3 to 13 for improvement and - 10 to - 5 for deterioration.Conclusions
Our findings aid the meaningful interpretation of changes on a set of EORTC QLQ-C30 scale scores over time, both within and between groups, and for performing more accurate sample size calculations for clinical trials in prostate cancer.
SUBMITTER: Gamper EM
PROVIDER: S-EPMC8496068 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundMinimally important differences (MIDs) allow interpretation of the clinical relevance of health-related quality of life (HRQOL) results. This study aimed to estimate MIDs for all European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 30 (QLQ-C30) scales for interpreting group-level results in brain tumor patients.MethodsClinical and HRQOL data from three glioma trials were used. Clinical anchors were selected for each EORTC QLQ-C30 scale, based on correlation (>0.30) and clinical plausibility of association. Changes in both HRQOL and the anchors were calculated, and for each scale and time period, patients were categorized into one of the three clinical change groups: deteriorated by one anchor category, no change, or improved by one anchor category. Mean change method and linear regression were applied to estimate MIDs for interpreting within-group change and between-group differences in change over time, respectively. Distribution-based methods were applied to generate supportive evidence.ResultsA total of 1687 patients were enrolled in the three trials. The retained anchors were performance status and eight Common Terminology Criteria for Adverse Events (CTCAE) scales. MIDs for interpreting within-group change ranged from 4 to 12 points for improvement and -4 to -14 points for deterioration. MIDs for between-group difference in change ranged from 4 to 9 for improvement and -4 to -16 for deterioration. Most anchor-based MIDs were closest to the 0.3 SD distribution-based estimates (range: 3-10).ConclusionsMIDs for the EORTC QLQ-C30 scales generally ranged between 4 and 11 points for both within-group mean change and between-group mean difference in change. These results can be used to interpret QLQ-C30 results from glioma trials.
| S-EPMC8328025 | biostudies-literature
Project description:PurposeTo establish minimal important differences (MIDs) for the European Organisation for Research and Treatment for Cancer Quality of life Questionnaire core 30 (EORTC QLQ-C30) in patients with metastatic breast cancer.MethodsThe dataset was obtained from the SELECT BC-CONFIRM randomized clinical trial. Anchors obtained from patients (transition items) and clinicians (performance status) were used for anchor-based methods. Anchors obtained through 6 months after starting treatment were used for this analysis. Correlation coefficients of anchor and change in QLQ-C30 and effect size were used to qualify for estimating MIDs. Mean change method and generalized estimating equation were applied to estimate MIDs. Distribution-based methods were used for comparison.ResultsWe analyzed a dataset of 154 metastatic breast cancer patients. MIDs were estimated in 8 of 15 scales of QLQ-C30. Estimated MIDs for within-group improvement varied from 7 to 15 and those for deterioration varied from - 7 to - 17. Estimated MIDs for between-group improvement varied from 5 to 11 and those for deterioration varied from - 5 to - 8 across QLQ-C30 scales. Patient-reported anchors were more susceptible to early changes in health status than clinician-reported anchors.ConclusionWe provided the MIDs of the QLQ-C30 using both patient- and clinicians-reported anchors measured in a randomized trial of Japanese patients with metastatic breast cancer. We recommend patient-reported anchors for anchor-based estimation of MID. Our results can aid patients and clinicians, as well as researchers, in the interpretation of QLQ-C30.
| S-EPMC9098551 | biostudies-literature
Project description:PurposeWe aimed to investigate measurement invariance (MI) in the European Organisation for research and treatment of cancer quality of life questionnaire core 30 (EORTC QLQ-C30) in a heterogeneous sample of patients with cancer.MethodsData from 12 studies within the PROFILES registry were used for secondary analyses (n = 7007). We tested MI by successive restrictions on thresholds, loadings, and intercepts across subgroups based on primary cancer sites, age, sex, time since diagnosis, and life stage, using multigroup confirmatory factor analysis (MGCFA) for ordered categorical measures. We also evaluated the impact of potentially miss-specified parameter equality across groups on latent factor means by releasing threshold and loading equality constraints for each item at a time.ResultsResults showed that the highest level of MI (invariance of thresholds, loadings, and intercepts) was found across groups based on time since diagnosis and life stage and to a lesser extent across groups based on sex, age, and primary tumor site. On item level, however, changes in the item's associated factor means were relatively small and in most cases canceled each other out to some extent.ConclusionsGiven only a few instances of non-invariance in our study, there is reason to be confident that valid conclusions can be drawn from between-group comparisons of QLQ-C30 latent means as operationalized in our study. Nonetheless, further research into MI between other subgroups for the QLQ-C30 (i.e., treatment effects and ethnicity) is warranted. We stress the importance of including MI evaluations in the development and validation of measurement instruments.
| S-EPMC8921013 | biostudies-literature
Project description:OBJECTIVES:Thresholds for the minimally important difference (MID) or responder definition (RD) in health-related quality-of-life (HRQoL) scores are required to interpret the impact of an intervention or change in the trajectory of the condition which is meaningful to patients. This study aimed to establish MID and RD for the European Organisation for Research and Treatment of Cancer Quality of Life Multiple Myeloma questionnaire (EORTC QLQ-MY20). METHODS:A novel mixed-methods approach was applied by utilizing both existing clinical trial data and prospective patient interviews. Anchor-based, distribution-based, and qualitative-based estimates of meaningful change were triangulated to form recommended RDs for each scale of the EORTC QLQ-MY20. Anchor-based MIDs were summarized using weighted correlation. RESULTS:Recommended MIDs were as follows: Disease Symptoms (DS 10 points), Side Effects of Treatment (SE 10 points), Body Image (BI 13 points), and Future Perspective (FP 9 points). Recommended RDs were as follows: DS (16 improvement; 11 worsening), SE (6 improvement; 9 worsening), BI (33 improvement; 33 worsening), and FP (11 improvement; 11 worsening). CONCLUSIONS:The study generated estimates of the MID and RD for each scale of the EORTC QLQ-MY20. Published estimates will enable investigators and clinicians to adopt these as standard for interpretation and for hypothesis testing. Consequently, analyses from trials of different interventions can be more comparable.
| S-EPMC6852250 | biostudies-literature
Project description:In oncology, health-related quality of life (HRQoL) data are often collected using disease-specific patient questionnaires while generic, patient-level utility data required for health economic modeling are often not collected.We developed a mapping algorithm for multiple myeloma that relates HRQoL scores from the European Organization for Research and Treatment of Cancer (EORTC) questionnaires QLQ-C30 and QLQ-MY20 to a utility value from the European QoL-5 Dimensions (EQ-5D) questionnaire. Data were obtained from 154 multiple myeloma patients who had participated in a multicenter cohort study in the UK or Germany. All three questionnaires were administered at a single time point. Scores from all 19 domains of the QLQ-C30 and QLQ-MY20 instruments were univariately tested against EQ-5D values and retained in a multivariate regression model if statistically significant. A 10-fold cross-validation model selection method was also used as an alternative testing means. Two models were developed: one based on QLQ-C30 plus QLQ-MY20 scores and one based on QLQ-C30 scores alone. Adjusted R-squared, correlation coefficients, and plots of observed versus predicted EQ-5D values were presented for both models.Mapping revealed that Global Health Status/QoL, Physical Functioning, Pain, and Insomnia were significant predictors of EQ-5D utility values. Similar results were observed when QLQ-MY20 scores were excluded from the model, except that Emotional Functioning and became a significant predictor and Insomnia was no longer a significant predictor. Adjusted R-squared values were of similar magnitude with or without inclusion of QLQ-MY20 scores (0.70 and 0.69, respectively), suggesting that the EORTC QLQ-MY20 adds little in terms of predicting utility values in multiple myeloma.This algorithm successfully mapped EORTC HRQoL data onto EQ-5D utility in patients with multiple myeloma. Current mapping will aid in the analysis of cost-effectiveness of novel therapies for this indication.
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Project description:BackgroundThe types of outcomes measured collected in clinical studies and those required for cost-effectiveness analysis often differ. Decision makers routinely use quality adjusted life years (QALYs) to compare the benefits and costs of treatments across different diseases and treatments using a common metric. QALYs can be calculated using preference-based measures (PBMs) such as EQ-5D-3L, but clinical studies often focus on objective clinician or laboratory measured outcomes and non-preference-based patient outcomes, such as QLQ-C30. We model the relationship between the generic, preference-based EQ-5D-3L and the cancer specific quality of life questionnaire, QLQ-C30 in patients with breast cancer. This will result in a mapping that allows users to convert QLQ-C30 scores into EQ-5D-3L scores for the purposes of cost-effectiveness analysis or economic evaluation.MethodsWe use data from a randomized trial of 602 patients with HER2-positive advanced breast cancer provided 3766 EQ-5D-3L observations. Direct mapping using adjusted, limited dependent variable mixture models (ALDVMM) is compared to a random effects linear regression and indirect mapping using seemingly unrelated ordered probit models. EQ-5D-3L was estimated as a function of the summary scales of the QLQ-C30 and other patient characteristics.ResultsA four component mixture model outperformed other models in terms of summary fit statistics. A close fit to the observed data was observed across the range of disease severity. Simulated data from the model closely aligned to the original data and showed that mapping did not significantly underestimate uncertainty. In the simulated data, 22.15% were equal to 1 compared to 21.93% in the original data. Variance was 0.0628 in the simulated data versus 0.0693 in the original data. The preferred mapping is provided in Excel and Stata files for the ease of users.ConclusionA four component adjusted mixture model provides reliable, non-biased estimates of EQ-5D-3L from the QLQ-C30, to link clinical studies to economic evaluation of health technologies for breast cancer. This work adds to a growing body of literature demonstrating the appropriateness of mixture model based approaches in mapping.
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Project description:BACKGROUND:The European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 is a widely used cancer-specific questionnaire assessing 15 domains of health-related quality of life (HRQoL). Our aim was to facilitate the interpretation of scores on this questionnaire by providing Austrian normative data based on a general population sample. METHODS:The calculation of normative data was based on the EORTC QLQ-C30 data collected from an Austrian general population sample that was part of an international online panel study on the development of European normative data. Data reported herein were stratified and weighted by age and sex. Normative data were calculated for all 15 HRQoL domains of the EORTC QLQ-C30. For precise predictions of EORTC QLQ-C30 scores, a regression model based on sex, age and the presence of health conditions was built. RESULTS:The Austrian sample comprised 1002 Austrian participants (50.1% female, 51.4% when weighted by age and sex based on United Nation statistics). The mean age was 53.7?years (weighted: 47.7?years) and 53.6% (weighted: 47.4%) reported at least one health condition. Men reported better physical (Cohen's d?=?0.17) and emotional (Cohen's d?=?0.17) functioning as well as less fatigue (Cohen's d?=?0.18) and insomnia (Cohen's d?=?0.25) compared with women. Younger individuals (<?40?years) reported less dyspnea (Cohen's d?=?0.61) and pain (Cohen's d?=?0.51), whereas older individuals (?60?years) reported better emotional functioning (Cohen's d?=?0.55). CONCLUSIONS:We present Austrian normative data for the EORTC QLQ-C30. Differences by age and sex are mostly in line with the findings of other European normative studies. The Austrian population sample shows higher HRQoL and lower morbidity compared with other European countries. The normative data in this study will facilitate the interpretation of EORTC QLQ-C30 scores in oncological practice and research at a national and international level (including cross-cultural comparisons).
| S-EPMC7425034 | biostudies-literature
Project description:PurposeTo obtain reference norms of EORTC QLQ-C30, EORTC QLQ-BR23, and EQ-5D-5L, based on a population of Spanish non-metastatic breast cancer patients at diagnosis and 2 years after, according to relevant demographic and clinical characteristics.MethodsMulticentric prospective cohort study including consecutive women aged ≥ 18 years with a diagnosis of incident non-metastatic breast cancer from April 2013 to May 2015. Health-related quality of life (HRQoL) questionnaires were administered between diagnosis and beginning the therapy, and 2 years after. HRQoL differences according to age, comorbidity and stage were tested with ANOVA or Chi Square test and multivariate linear regression models.Results1276 patients were included, with a mean age of 58 years. Multivariate models of EORTC QLQ-C30 summary score and EQ-5D-5L index at diagnosis and at 2-year follow-up show the independent association of comorbidity and tumor stage with HRQoL. The standardized multivariate regression coefficient of EORTC QLQ-C30 summary score was lower (poorer HRQoL) for women with stage II and III than for those with stage 0 at diagnosis (- 0.11 and - 0.07, p < 0.05) and follow-up (- 0.15 and - 0.10, p < 0.01). The EQ-5D-5L index indicated poorer HRQoL for women with Charlson comorbidity index ≥ 2 than comorbidity 0 both at diagnosis (- 0.13, p < 0.001) and follow-up (- 0.18, p < 0.001). Therefore, we provided the reference norms at diagnosis and at the 2-year follow-up, stratified by age, comorbidity index, and tumor stage.ConclusionThese HRQoL reference norms can be useful to interpret the scores of women with non-metastatic breast cancer, comparing them with country-specific reference values for this population.
| S-EPMC10063520 | biostudies-literature
Project description:Objective. The EORTC QLQ-C30 is widely used for assessing quality of life in cancer. However, QLQ-C30 responses cannot be incorporated in cost-utility analysis because they are not based on general population's preferences, or utilities. To overcome this limitation, the QLU-C10D, a cancer-specific utility algorithm, was derived from the QLQ-C30. The aim of this study was to obtain Canadian population utility weights for the QLU-C10D. Methods. Respondents from a Canadian research panel expressed their preferences for 16 choice sets in an online discrete choice experiment. Each choice set consisted of two health states described by the 10 QLU-C10D domains plus an attribute representing duration of survival. Using a conditional logit model, responses were converted into utility decrements by evaluating the marginal rate of substitution between each QLU-C10D domain level with respect to duration. Results. A total of 3,363 individuals were recruited. A total of 2,345 completed at least one choice set and 2,271 completed all choice sets. The largest utility decrements were associated with the worse levels of Physical Functioning (-0.24), Pain (-0.18), Role Functioning (-0.15), Emotional Functioning (-0.12), and Nausea (-0.12). The remaining domains and levels had decrements of -0.05 to -0.09. The utility of the worst possible health state was -0.15. Conclusion. Respondents from the general population were most concerned with generic health domains, but Nausea and Bowel Problems also had an impact on the individual's utility. It is unclear as to whether cancer-specific domains will affect cost-utility analysis when evaluating cancer treatments; this will be tested in the next phase of the study.
| S-EPMC6580722 | biostudies-literature
Project description:BackgroundTo develop direct and indirect (response) mapping algorithms from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) and the Functional Assessment of Cancer Therapy General (FACT-G) onto the EQ-5D-5L index.MethodsWe conducted the QOL-MAC study where EQ-5D-5L, EORTC QLQ-C30, and FACT-G were cross-sectionally evaluated in patients receiving drug treatment for solid tumors in Japan. We developed direct and indirect mapping algorithms using 7 regression methods. Direct mapping was based on the Japanese value set. We evaluated the predictive performances based on root mean squared error (RMSE), mean absolute error, and correlation between the observed and predicted EQ-5D-5L indexes.ResultsBased on data from 903 and 908 patients for EORTC QLQ-C30 and FACT-G, respectively, we recommend two-part beta regression for direct mapping and ordinal logistic regression for indirect mapping for both EORTC QLQ-C30 and FACT-G. Cross-validated RMSE were 0.101 in the two methods for EORTC QLQ-C30, whereas they were 0.121 in two-part beta regression and 0.120 in ordinal logistic regression for FACT-G. The mean EQ-5D-5L index and cumulative distribution function simulated from the recommended mapping algorithms generally matched with the observed ones except for very good health (both source measures) and poor health (only FACT-G).ConclusionsThe developed mapping algorithms can be used to generate the EQ-5D-5L index from EORTC QLQ-C30 or FACT-G in cost-effectiveness analyses, whose predictive performance would be similar to or better than those of previous algorithms.
| S-EPMC7641825 | biostudies-literature