Dataset Information

Mapping EORTC QLQ-C30 and FACT-G onto EQ-5D-5L index for patients with cancer.

ABSTRACT: BACKGROUND:To 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. METHODS:We 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. RESULTS:Based 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). CONCLUSIONS:The 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.

SUBMITTER: Hagiwara Y

PROVIDER: S-EPMC7641825 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Mapping EORTC QLQ-C30 and FACT-G onto EQ-5D-5L index for patients with cancer.

Hagiwara Yasuhiro Y Shiroiwa Takeru T Taira Naruto N Kawahara Takuya T Konomura Keiko K Noto Shinichi S Fukuda Takashi T Shimozuma Kojiro K

Health and quality of life outcomes 20201103 1

<h4>Background</h4>To 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.<h4>Methods</h4>We 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 ...[more]

PMID: 33143687

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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.

Project description:BackgroundCost-effectiveness models require quality of life utilities calculated from generic preference-based questionnaires, such as EQ-5D. We evaluated the performance of available algorithms for QLQ-C30 conversion into EQ-5D-3L based utilities in a metastatic colorectal cancer (mCRC) patient population and subsequently developed a mCRC specific algorithm. Influence of mapping on cost-effectiveness was evaluated.MethodsThree available algorithms were compared with observed utilities from the CAIRO3 study. Six models were developed using 5-fold cross-validation: predicting EQ-5D-3L tariffs from QLQ-C30 functional scale scores, continuous QLQ-C30 scores or dummy levels with a random effects model (RE), a most likely probability method on EQ-5D-3L functional scale scores, a beta regression model on QLQ-C30 functional scale scores and a separate equations subgroup approach on QLQ-C30 functional scale scores. Performance was assessed, and algorithms were tested on incomplete QLQ-C30 questionnaires. Influence of utility mapping on incremental cost/QALY gained (ICER) was evaluated in an existing Dutch mCRC cost-effectiveness model.ResultsThe available algorithms yielded mean utilities of 1: 0.87?±?sd:0.14,2: 0.81?±?0.15 (both Dutch tariff) and 3: 0.81?±?sd:0.19. Algorithm 1 and 3 were significantly different from the mean observed utility (0.83?±?0.17 with Dutch tariff, 0.80?±?0.20 with U.K. tariff). All new models yielded predicted utilities drawing close to observed utilities; differences were not statistically significant. The existing algorithms resulted in an ICER difference of €10,140 less and €1765 more compared to the observed EQ-5D-3L based ICER (€168,048). The preferred newly developed algorithm was €5094 higher than the observed EQ-5D-3L based ICER. Disparity was explained by minimal diffences in incremental QALYs between models.ConclusionAvailable mapping algorithms sufficiently accurately predict utilities. With the commonly used statistical methods, we did not succeed in developping an improved mapping algorithm. Importantly, cost-effectiveness outcomes in this study were comparable to the original model outcomes between different mapping algorithms. Therefore, mapping can be an adequate solution for cost-effectiveness studies using either a previously designed and validated algorithm or an algorithm developed in this study.

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.

Project description:BACKGROUND:Several mapping or cross-walking algorithms for deriving utilities from the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire for Cancer (EORTC QLQ-C30) scores have been published in recent years. However, the large majority used ordinary least squares (OLS) regression, which proved to be not very accurate because of the specifics of the quality-of-life measures. OBJECTIVE:Our objective was to compare regression methods that have been used to map EuroQol 5 Dimensions 3 Levels (EQ-5D-3L) utility values from the general EORTC QLQ-C30 using OLS as a benchmark while fixing the number of explanatory variables and to explore an alternative three-part model. METHODS:We conducted a regression analysis of predicted EQ-5D-3L utilities generated using data from an observational study in ambulatory patients with non-small-cell lung cancer in a Toronto hospital. Six alternative regression methods were compared with a simple OLS regression as benchmark. The six alternative regression models were Tobit, censored least absolute deviation, normal mixture, beta, zero-one inflated beta and a mix of piecewise OLS and logistic regression. RESULTS:The best predictive fit was obtained by a mix of OLS regression(s) for utilities lower than 1 with a cut-off point of 0.50 and a separate binary logistic regression for utilities equal to one. Zero-one inflated beta regression was also promising. However, OLS regression proved to be the most accurate for the mean. The prediction of utilities equal to one was poor in all regression approaches. CONCLUSIONS:Three-part regression methods that separately target low, medium and high (<0.50, 0.51-0.99 or 1) utilities seem to have better prediction power than OLS with EQ-5D-3L data, although OLS also seems quite robust. Exploration of three-part approaches compared with single (OLS) regression should be further tested in other similar datasets or using individual pooled data from various clinical or observational studies. The use of alternative goodness-of-fit measures for mapping studies and their influence on the choice of the best performing methods should also be investigated.

Dataset Information

Mapping EORTC QLQ-C30 and FACT-G onto EQ-5D-5L index for patients with cancer.

Publications

Mapping EORTC QLQ-C30 and FACT-G onto EQ-5D-5L index for patients with cancer.

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