Mapping EORTC-QLQ-C30 to EQ-5D-3L in patients with colorectal cancer

Recommendations for the management of yttrium-90 radioembolization in the treatment of patients with colorectal cancer liver metastases: a multidisciplinary review

PURPOSE

Strategies for the treatment of liver metastases from colon cancer (lmCRC) are constantly evolving. Radioembolization with yttrium 90 (Y-90 TARE) has made significant advancements in treating liver tumors and is now considered a potential option allowing for future resection. This study reviewed the scientific evidence and developed recommendations for using Y-90 TARE as a treatment strategy for patients with unresectable lmCRC.

METHODS

A multidisciplinary scientific committee, consisting of experts in medical oncology, hepatobiliary surgery, radiology, and nuclear medicine, all with extensive experience in treating patients with ImCRC with Y-90 TARE, led this project. The committee established the criteria for conducting a comprehensive literature review on Y-90 TARE in the treatment of lmCRC. The data extraction process involved addressing initial preliminary inquiries, which were consolidated into a final set of questions.

RESULTS

This review offers recommendations for treating patients with lmCRC using Y-90 TARE, addressing four areas covering ten common questions: 1) General issues (multidisciplinary tumor committee, indications for treatment, contraindications); 2) Previous process (predictive biomarkers for patient selection, preintervention tests, published evidence); 3) Procedure (standard procedure); and 4) Post-intervention follow-up (potential toxicity and its management, parameters for evaluation, quality of life).

CONCLUSIONS

Based on the insights of the multidisciplinary committee, this document offers a comprehensive overview of the technical aspects involved in the management of Y-90 TARE. It synthesizes recommendations for applying Y-90 TARE across various phases of the treatment process.

Predicting EQ-5D-5L crosswalk from the PROMIS-29 profile for the United Kingdom, France, and Germany

Background

EQ-5D health state utilities (HSU) are commonly used in health economics to compute quality-adjusted life years (QALYs). The EQ-5D, which is country-specific, can be derived directly or by mapping from self-reported health-related quality of life (HRQoL) scales such as the PROMIS-29 profile. The PROMIS-29 from the Patient Reported Outcome Measures Information System is a comprehensive assessment of self-reported health with excellent psychometric properties. We sought to find optimal models predicting the EQ-5D-5L crosswalk from the PROMIS-29 in the United Kingdom, France, and Germany and compared the prediction performances with that of a US model.

Methods

We collected EQ-5D-5L and PROMIS-29 profiles and three samples representative of the general populations in the UK (n = 1509), France (n = 1501), and Germany (n = 1502). We used stepwise regression with backward selection to find the best models to predict the EQ-5D-5L crosswalk from all seven PROMIS-29 domains. We investigated the agreement between the observed and predicted EQ-5D-5L crosswalk in all three countries using various indices for the prediction performance, including Bland–Altman plots to examine the performance along the HSU continuum.

Results

The EQ-5D-5L crosswalk was best predicted in France (nRMSE_FRA = 0.075, nMAE_FRA = 0.052), followed by the UK (nRMSE_UK = 0.076, nMAE_UK = 0.053) and Germany (nRMSE_GER = 0.079, nMAE_GER = 0.051). The Bland–Altman plots show that the inclusion of higher-order effects reduced the overprediction of low HSU scores.

Conclusions

Our models provide a valid method to predict the EQ-5D-5L crosswalk from the PROMIS-29 for the UK, France, and Germany.

Mapping EORTC-QLQ-C30 and QLQ-CR29 onto EQ-5D-5L in Colorectal Cancer Patients

PURPOSE

Patient-level utility data are needed for cost-utility analysis; in oncology, however, the data are commonly gathered using disease-specific questionnaires that are often not appropriate. Present study aimed to derive an algorithm which can map the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C30 (EORTC QLQ-C30) scales and the Colorectal Cancer-Specific Quality Of Life Questionnaire (QLQ-CR29) scales onto the EuroQoL 5-Dimension 5-Level (EQ-5D-5L) values in patients with colorectal cancer (CRC).

METHODS

Using the Ordinary Least Square (OLS) model, a cross-sectional dataset of 252 patients with CRC were gathered from three academic centers of cancer treatment in Tehran in 2017. The predicted R² (Pred R²) and adjusted R² (Adj R²) are used to evaluate model goodness of fit. Additionally, mean absolute error (MAE), root mean square error (RMSE), Spearman's correlation coefficients (ρ), and intraclass correlation (ICC) are applied to assess predictive ability of models. The tenfold cross-validation procedure was applied for validation models.

RESULTS

According to the results of our study, the model C4 from EORTC QLQ-C30 was the best predictive model (Pred R² = 66.57%, Adj R² = 67.67%, RMSE = 0.10173, MAE = 0.07840). Also, the model R4 from QLQ-CR29 performed the best for EQ-5D-5L (Adj R2 = 48.42%, Pred R² = 45.54%, MAE = 0.10051, RMSE = 0.12997).

CONCLUSIONS

The mapping algorithm successfully mapped the EORTC QLQ-C30 and QLQ-CR29 scales onto the EQ-5D-5L values; therefore, it enables policymakers to convert cancer-specific questionnaires scores to the preference-based scores.

Statistical tests for latent class in censored data due to detection limit

Measures of substance concentration in urine, serum or other biological matrices often have an assay limit of detection. When concentration levels fall below the limit, the exact measures cannot be obtained. Instead, the measures are censored as only partial information that the levels are under the limit is known. Assuming the concentration levels are from a single population with a normal distribution or follow a normal distribution after some transformation, Tobit regression models, or censored normal regression models, are the standard approach for analyzing such data. However, in practice, it is often the case that the data can exhibit more censored observations than what would be expected under the Tobit regression models. One common cause is the heterogeneity of the study population, caused by the existence of a latent group of subjects who lack the substance measured. For such subjects, the measurements will always be under the limit. If a censored normal regression model is appropriate for modeling the subjects with the substance, the whole population follows a mixture of a censored normal regression model and a degenerate distribution of the latent class. While there are some studies on such mixture models, a fundamental question about testing whether such mixture modeling is necessary, i.e. whether such a latent class exists, has not been studied yet. In this paper, three tests including Wald test, likelihood ratio test and score test are developed for testing the existence of such latent class. Simulation studies are conducted to evaluate the performance of the tests, and two real data examples are employed to illustrate the tests.

Evaluation of the performance of algorithms mapping EORTC QLQ-C30 onto the EQ-5D index in a metastatic colorectal cancer cost-effectiveness model

Background

Cost-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.

Methods

Three 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.

Results

The 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.

Conclusion

Available 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.

Mapping analysis to predict EQ-5D-5 L utility values based on the Oxford Hip Score (OHS) and Oxford Knee Score (OKS) questionnaires in the Spanish population suffering from lower limb osteoarthritis

BACKGROUND

The EQ-5D-5 L is a quality-of-life questionnaire based on individuals' preferences that is widely employed for cost-effectiveness analysis. Given the current demand for mapping algorithms to directly assign "utilities", this study aimed to generate different mapping models for predicting EQ-5D-5 L utility values based on scores of the Oxford Hip Score (OHS) and Oxford Knee Score (OKS) questionnaires provided by patients suffering from hip and knee osteoarthritis (OA), respectively, and to assess the predictive capability of these functions.

METHODS

This was a prospective, observational study. Following the criteria of the American Rheumatism Association, 361 patients with hip OA and 397 with knee OA from three regions in Spain were included. Health-related quality of life (HRQoL) was assessed through the EQ-5D-5 L general questionnaire and the OHS and OKS specifically for lower limb OA. Based on the scores on the OHS and OKS questionnaires, EQ-5D-5 L utilities were estimated using 4 models: ordinary least squares (OLS), Tobit, generalized linear model (GLM), and beta regression (Breg). The models were validated on the same patients after 6 months: the mean absolute error (MAE) and mean squared error (MSE) with their 95% confidence intervals (CI), mean values of standard errors (SE), intraclass correlation coefficients (ICC), and Bland-Altman plots were obtained.

RESULTS

The lowest MAEs were obtained using GLM and Breg models, with values of 0.1103 (0.0993-0.1214) and 0.1229 (0.1102-0.1335) for hip OA, and values of 0.1127 (0.1014-0.1239) and 0.1141 (0.1031-0.1251) for knee OA. MSE values were also lower using GLM and Breg. ICCs between predicted and observed values were around or over the 0.8 cut-off point. Bland-Altman plots showed an acceptable correlation, but precision was lower for subjects with worse HRQoL, which was also evident when comparing MAEs of the bottom and top halves of the utilities scale. Predictive equations for utilities based on OHS/OKS scores were proposed.

CONCLUSIONS

The OHS and OKS scores allow for estimating EQ-5D-5 L utility indexes for patients with hip and knee OA, respectively, with adequate validity and precision. GLM and Breg produce the best predictions. The predictive power of proposed equations is more consistent for subjects in better health condition.

Cost-effectiveness of the Collaborative Care to Preserve Performance in Cancer (COPE) trial tele-rehabilitation interventions for patients with advanced cancers

Purpose

The purpose of this analysis was to determine the cost‐effectiveness of a Collaborative Care Model (CCM)‐based, centralized telecare approach to delivering rehabilitation services to late‐stage cancer patients experiencing functional limitations.

Methods

Data for this analysis came from the Collaborative Care to Preserve Performance in Cancer (COPE) trial, a randomized control trial of 516 patients assigned to: (a) a control group (arm A), (b) tele‐rehabilitation (arm B), and (c) tele‐rehabilitation plus pharmacological pain management (arm C). Patient quality of life was measured using the EQ‐5D‐3L at baseline, 3‐month, and 6‐month follow‐up. Direct intervention costs were measured from the experience of the trial. Participants’ hospitalization data were obtained from their medical records, and costs associated with these encounters were estimated from unit cost data and hospital‐associated utilization information found in the literature. A secondary analysis of total utilization costs was conducted for the subset of COPE trial patients for whom comprehensive cost capture was possible.

Results

In the intervention‐only model, tele‐rehabilitation (arm B) was found to be the dominant strategy, with an incremental cost‐effectiveness ratio (ICER) of $15 494/QALY. At the $100 000 willingness‐to‐pay threshold, this tele‐rehabilitation was the cost‐effective strategy in 95.4% of simulations. It was found to be cost saving compared to enhanced usual care once the downstream hospitalization costs were taken into account. In the total cost analysis, total inpatient hospitalization costs were significantly lower in both tele‐rehabilitation (arm B) and tele‐rehabilitation plus pain management (arm C) compared to control (arm A), (P = .048).

Conclusion

The delivery of a CCM‐based, centralized tele‐rehabilitation intervention to patients with advanced stage cancer is highly cost‐effective. Clinicians and care teams working with this vulnerable population should consider incorporating such interventions into their patient care plans.

Effect of Collaborative Telerehabilitation on Functional Impairment and Pain Among Patients With Advanced-Stage Cancer: A Randomized Clinical Trial

Importance

Most patients with advanced-stage cancer develop impairment and pain-driven functional losses that jeopardize their independence.

Objective

To determine whether collaborative telerehabilitation and pharmacological pain management improve function, lessen pain, and reduce requirements for inpatient care.

Design, Setting, and Patients

The Collaborative Care to Preserve Performance in Cancer (COPE) study was a 3-arm randomized clinical trial conducted at 3 academic medical centers within 1 health care system. Patient recruitment began in March 2013 and follow-up concluded in October 2016. Participants (N = 516) were low-level community or household ambulators with stage IIIC or IV solid or hematologic cancer.

Interventions

Participants were randomly assigned to the (1) control arm, (2) telerehabilitation arm, or (3) telerehabilitation with pharmacological pain management arm. All patients underwent automated function and pain monitoring with data reporting to their care teams. Participants in arms 2 and 3 received 6 months of centralized telerehabilitation provided by a physical therapist-physician team. Those in arm 3 also received nurse-coordinated pharmacological pain management.

Main Outcomes and Measures

Blinded assessment of function using the Activity Measure for Postacute Care computer adaptive test, pain interference and average intensity using the Brief Pain Inventory, and quality of life using the EQ-5D-3L was performed at baseline and months 3 and 6. Hospitalizations and discharges to postacute care facilities were recorded.

Results

The study included 516 participants (257 women and 259 men; mean [SD] age, 65.6 [11.1] years), with 172 randomized to 1 of 3 arms. Compared with the control group, the telerehabilitation arm 2 had improved function (difference, 1.3; 95% CI, 0.08-2.35; P = .03) and quality of life (difference, 0.04; 95% CI, 0.004-0.071; P = .01), while both telerehabilitation arms 2 and 3 had reduced pain interference (arm 2, -0.4; 95% CI, -0.78 to -0.09; P = .01 and arm 3, -0.4; 95% CI, -0.79 to -0.10; P = .01), and average intensity (arm 2, -0.4; 95% CI, -0.78 to -0.07; P = .02 and arm 3, -0.5; 95% CI, -0.84 to -0.11; P = .006). Telerehabilitation was associated with higher odds of home discharge in arms 2 (odds ratio [OR], 4.3; 95% CI, 1.3-14.3; P = .02) and 3 (OR, 3.8; 95% CI, 1.1-12.4; P = .03) and fewer days in the hospital in arm 2 (difference, -3.9 days; 95% CI, -2.4 to -4.6; P = .01).

Conclusions and Relevance

Collaborative telerehabilitation modestly improved function and pain, while decreasing hospital length of stay and the requirement for postacute care, but these outcomes were not enhanced with the addition of pharmacological pain management.

Trial Registration

ClinicalTrials.gov identifier: NCT01721343.

An Updated Systematic Review of Studies Mapping (or Cross-Walking) Measures of Health-Related Quality of Life to Generic Preference-Based Measures to Generate Utility Values

BACKGROUND

Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values (HSUVs) from data obtained from another health-related quality of life (HRQoL) measure. There have been several methodological developments in this area since a previous review up to 2007.

OBJECTIVE

To provide an updated review of all mapping studies that map from HRQoL measures to target generic preference-based measures (EQ-5D measures, SF-6D, HUI measures, QWB, AQoL measures, 15D/16D/17D, CHU-9D) published from January 2007 to October 2018.

DATA SOURCES

A systematic review of English language articles using a variety of approaches: searching electronic and utilities databases, citation searching, targeted journal and website searches.

STUDY SELECTION

Full papers of studies that mapped from one health measure to a target preference-based measure using formal statistical regression techniques.

DATA EXTRACTION

Undertaken by four authors using predefined data fields including measures, data used, econometric models and assessment of predictive ability.

RESULTS

There were 180 papers with 233 mapping functions in total. Mapping functions were generated to obtain EQ-5D-3L/EQ-5D-5L-EQ-5D-Y (n = 147), SF-6D (n = 45), AQoL-4D/AQoL-8D (n = 12), HUI2/HUI3 (n = 13), 15D (n = 8) CHU-9D (n = 4) and QWB-SA (n = 4) HSUVs. A large number of different regression methods were used with ordinary least squares (OLS) still being the most common approach (used ≥ 75% times within each preference-based measure). The majority of studies assessed the predictive ability of the mapping functions using mean absolute or root mean squared errors (n = 192, 82%), but this was lower when considering errors across different categories of severity (n = 92, 39%) and plots of predictions (n = 120, 52%).

CONCLUSIONS

The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions.

Evidence-based sizing of non-inferiority trials using decision models

Background

There are significant challenges to the successful conduct of non-inferiority trials because they require large numbers to demonstrate that an alternative intervention is “not too much worse” than the standard. In this paper, we present a novel strategy for designing non-inferiority trials using an approach for determining the appropriate non-inferiority margin (δ), which explicitly balances the benefits of interventions in the two arms of the study (e.g. lower recurrence rate or better survival) with the burden of interventions (e.g. toxicity, pain), and early and late-term morbidity.

Methods

We use a decision analytic approach to simulate a trial using a fixed value for the trial outcome of interest (e.g. cancer incidence or recurrence) under the standard intervention (p_S) and systematically varying the incidence of the outcome in the alternative intervention (p_A). The non-inferiority margin, p_A – p_S = δ, is reached when the lower event rate of the standard therapy counterbalances the higher event rate but improved morbidity burden of the alternative. We consider the appropriate non-inferiority margin as the tipping point at which the quality-adjusted life-years saved in the two arms are equal.

Results

Using the European Polyp Surveillance non-inferiority trial as an example, our decision analytic approach suggests an appropriate non-inferiority margin, defined here as the difference between the two study arms in the 10-year risk of being diagnosed with colorectal cancer, of 0.42% rather than the 0.50% used to design the trial. The size of the non-inferiority margin was smaller for higher assumed burden of colonoscopies.

Conclusions

The example demonstrates that applying our proposed method appears feasible in real-world settings and offers the benefits of more explicit and rigorous quantification of the various considerations relevant for determining a non-inferiority margin and associated trial sample size.

Mapping the EORTC-QLQ-C30 to the EQ-5D-3L: An Assessment of Existing and Newly Developed Algorithms

Objectives. To assess the external validity of mapping algorithms for predicting EQ-5D-3L utility values from EORTC QLQ-C30 responses not previously validated and to assess whether statistical models not previously applied are better suited for mapping the EORTC QLQ-C30 to the EQ-5D-3L. Methods. In total, 3866 observations for 1719 patients from a longitudinal study (Cancer 2015) were used to validate existing algorithms. Predictive accuracy was compared to previously validated algorithms using root mean squared error, mean absolute error across the EQ-5D-3L range, and for 10 tumor-type specific samples as well as using differences between estimated quality-adjusted life years. Thirteen new algorithms were estimated using a subset of the Cancer 2015 data (3203 observations for 1419 patients) applying various linear, response mapping, beta, and mixture models. Validation was performed using 2 data sets composed of patients with varying disease severity not used in the estimation and all available algorithms ranked on their performance. Results. None of the 5 existing algorithms offer an improvement in predictive accuracy over preferred algorithms from previous validation studies. Of the newly estimated algorithms, a 2-part beta model performed the best across the validation criteria and in data sets composed of patients with different levels of disease severity. Validation results did, however, vary widely between the 2 data sets, and the most accurate algorithm appears to depend on health state severity as the distribution of observed EQ-5D-3L values varies. Linear models performed better for patients in relatively good health, whereas beta, mixture, and response mapping models performed better for patients in worse health. Conclusion. The most appropriate mapping algorithm to apply in practice may depend on the disease severity of the patient sample whose utility values are being predicted.

Mapping the cancer-specific QLQ-C30 onto the generic EQ-5D-5L and SF-6D in colorectal cancer patients

INTRODUCTION

Economic evaluation of healthcare interventions usually needs accurate data on utility and health-related quality-of-life scores. The aim of this study is to map QLQ-C30 scale score onto EQ-5D-5L and SF-6D utility values in colorectal cancer (CRC) patients.

METHODS

EQ-5D-5L, SF-6D, and QLQ-C30 were completed by 252 patients with CRC who were referred to three cancer centers in Tehran between May and September 2017. Moreover, OLS, Tobit, and CLAD models were used to predict EQ-5D-5L and SF-6D values. The goodness of fit of models was evaluated using Pred R² and Adj R². In addition, their predictive performance was assessed by MAE, RMSE, ICC, MID, and Spearman's correlation coefficients between observed and predicted EQ-5D-5L and SF-6D values. Models were validated using a 10-fold cross-validation method.

RESULTS

Considering the goodness of fit and predictive ability of models, the OLS Model 2 performed best for EQ-5D-5L (Adj R² = 58.09%, Pred R² = 58.93%, MAE = 0.0932, RMSE = 0.129) and the OLS Model 3 performed best for SF-6D (Adj R² = 54.90%, Pred R² = 55.62%, MAE = 0.0485, RMSE = 0.0634).

CONCLUSION

Our results demonstrated that algorithms developed based on OLS Models 1 and 2 are the best for predicted EQ-5D-5L and SF-6D values, respectively.

Testing alternative regression models to predict utilities: mapping the QLQ-C30 onto the EQ-5D-5L and the SF-6D

PURPOSE

The purpose of the study was to compare alternative statistical techniques to find the best approach for converting QLQ-C30 scores onto EQ-5D-5L and SF-6D utilities, and to estimate the mapping algorithms that best predict these health state utilities.

METHODS

772 cancer patients described their health along the cancer-specific instrument (QLQ-C30) and two generic preference-based instruments (EQ-5D-5L and SF-6D). Seven alternative regression models were applied: ordinary least squares, generalized linear model, extended estimating equations (EEE), fractional regression model, beta binomial (BB) regression, logistic quantile regression and censored least absolute deviation. Normalized mean absolute error (NMAE), normalized root mean square error (NRMSE), r-squared (r²) and concordance correlation coefficient (CCC) were used as model performance criteria. Cross-validation was conducted by randomly splitting internal dataset into two equally sized groups to test the generalizability of each model.

RESULTS

In predicting EQ-5D-5L utilities, the BB regression performed best. It gave better predictive accuracy in terms of all criteria in the full sample, as well as in the validation sample. In predicting SF-6D, the EEE performed best. It outperformed in all criteria: NRMSE = 0.1004, NMAE = 0.0798, CCC = 0.842 and r² = 72.7% in the full sample, and NRMSE = 0.1037, NMAE = 0.0821, CCC = 0.8345 and r² = 71.4% in cross-validation.

CONCLUSIONS

When only QLQ-C30 data are available, mapping provides an alternative approach to obtain health state utility data for use in cost-effectiveness analyses. Among seven alternative regression models, the BB and the EEE gave the most accurate predictions for EQ-5D-5L and SF-6D, respectively.

Review and critical appraisal of studies mapping from quality of life or clinical measures to EQ-5D: an online database and application of the MAPS statement

Background

The Health Economics Research Centre (HERC) Database of Mapping Studies was established in 2013, based on a systematic review of studies developing mapping algorithms predicting EQ-5D. The Mapping onto Preference-based measures reporting Standards (MAPS) statement was published in 2015 to improve reporting of mapping studies. We aimed to update the systematic review and assess the extent to which recently-published studies mapping condition-specific quality of life or clinical measures to the EQ-5D follow the guidelines published in the MAPS Reporting Statement.

Methods

A published systematic review was updated using the original inclusion criteria to include studies published by December 2016. We included studies reporting novel algorithms mapping from any clinical measure or patient-reported quality of life measure to either the EQ-5D-3L or EQ-5D-5L. Titles and abstracts of all identified studies and the full text of papers published in 2016 were assessed against the MAPS checklist.

Results

The systematic review identified 144 mapping studies reporting 190 algorithms mapping from 110 different source instruments to EQ-5D. Of the 17 studies published in 2016, nine (53%) had titles that followed the MAPS statement guidance, although only two (12%) had abstracts that fully addressed all MAPS items. When the full text of these papers was assessed against the complete MAPS checklist, only two studies (12%) were found to fulfil or partly fulfil all criteria. Of the 141 papers (across all years) that included abstracts, the items on the MAPS statement checklist that were fulfilled by the largest number of studies comprised having a structured abstract (95%) and describing target instruments (91%) and source instruments (88%).

Conclusions

The number of published mapping studies continues to increase. Our updated database provides a convenient way to identify mapping studies for use in cost-utility analysis. Most recent studies do not fully address all items on the MAPS checklist.

Electronic supplementary material

The online version of this article (10.1186/s12955-018-0857-3) contains supplementary material, which is available to authorized users.

Quality of Life in Palliative Care

Introduction

A main goals of palliative care is to improve the health-related quality of life (QOL) of patients with advanced illnesses. The objective of this narrative review is to provide an updated synopsis on the use of QOL questionnaires in the palliative care setting.

Areas covers

Focusing on the palliative cares setting, we will define QOL, discuss how QOL instruments can be used clinically and in research, review approaches to validate these questionnaires, and how they can be used in utility analyses.

Expert opinion/commentary

Several QOL questionnaires, such as EORTC-QLQ-C30, McGill QOL questionnaire and EQ-5D have been validated in the palliative care setting. However, significant gaps impede their application, including lack of determination of their responsiveness to change and minimal clinically important differences, the need to conduct more psychometric validation on QOL questionnaires among patients at various stages of disease trajectory, and the paucity of studies examining utility and cost-effectiveness. Further research is needed to address these knowledge gaps so QOL questionnaires can be better used to inform clinical practice and research.