The MAPS Reporting Statement for Studies Mapping onto Generic Preference-Based Outcome Measures: Explanation and Elaboration

Petrou, Stavros; Rivero‐Arias, Oliver; Dakin, Helen; Longworth, Louise; Oppe, Mark; Froud, Robert; Gray, Alastair

doi:10.1007/s40273-015-0312-9

Cited by 73 publications

(76 citation statements)

References 53 publications

(77 reference statements)

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“…In accordance with the MAPS statement, 32,33 we developed and compared the performance of several regression models mapping MOS-HIV dimension scores onto HUI3 or EQ-5D-3L utility values. The OLS model with squared terms (1b) is the preferred model with the best goodness of fit (MAE 0.0961) among the HUI3 mapping models; the finite mixture model (2f) is the preferred model among the EQ-5D-3L mapping models with a MAE of 0.0567.…”

Section: Discussionmentioning

confidence: 99%

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Mapping MOS-HIV to HUI3 and EQ-5D-3L in Patients With HIV

Joyce

Sun

Bansback

et al. 2017

MDM Policy & Practice

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Objectives: The Medical Outcomes Study HIV Health Survey (MOS-HIV) is frequently used in HIV clinical trials; however, scores generated from the MOS-HIV are not suited for cost-effectiveness analyses as they do not assign utility values to health states. Our objective was to estimate and externally validate several mapping algorithms to predict Health Utilities Index Mark 3 (HUI3) and EQ-5D-3L utility values from the MOS-HIV. Methods: We developed and validated mapping algorithms using data from two HIV clinical trials. Data from the first trial (n = 367) formed the estimation data set for the HUI3 (4,610 observations) and EQ-5D-3L (4,662 observations) mapping algorithms; data from the second trial (n = 168) formed the HUI3 (1,135 observations) and EQ-5D-3L (1,152 observations) external validation data set. We compared ordinary least squares (OLS) models of increasing complexity with the more flexible two-part, beta regression, and finite mixture models. We assessed model performance using mean absolute error (MAE) and mean squared error (MSE). Results: The OLS model that used MOS-HIV dimension scores along with squared terms gave the best HUI3 predictions (mean observed 0.84; mean predicted 0.80; MAE 0.0961); the finite mixture model gave the best EQ-5D-3L predictions (mean observed 0.90; mean predicted 0.88; MAE 0.0567). All models produced higher prediction errors at the lower end of the HUI3 and EQ-5D-3L score ranges (<0.40). Conclusions: The proposed mapping algorithms can be used to predict HUI3 and EQ-5D-3L utility values from the MOS-HIV, although greater error may pose a problem in samples where a substantial proportion of patients are in poor health. These algorithms may be useful for estimating utility values from the MOS-HIV for cost-effectiveness studies when HUI3 or EQ-5D-3L data are not available.

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Section: Discussionmentioning

confidence: 99%

“…32,33 The US Department of Veterans Affairs, the UK Medical Research Council, and the Canadian Institutes for Health Research approved the design and conduct of the OPTIMA clinical trial. The Canadian Institutes for Health Research approved the use of MAINTAIN data for the purposes of this study.…”

Section: Methodsmentioning

confidence: 99%

Mapping MOS-HIV to HUI3 and EQ-5D-3L in Patients With HIV

Joyce

Sun

Bansback

et al. 2017

MDM Policy & Practice

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“…Rather, it aims to avoid misleading conclusions being drawn by readers, and ultimately policy makers, as a result of suboptimal reporting. In keeping with other recent health research reporting guidelines, we have also produced an accompanying Explanation and Elaboration paper 16 to facilitate a deeper understanding of the 23 items contained within the MAPS reporting statement. That paper should hopefully act as a pedagogical framework for researchers reporting mapping studies.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the MAPS reporting statement, we have produced a supporting Explanation and Elaboration paper 16 modeled on those developed for other reporting guidelines. [9][10][11][12][13][14] The reporting items contained within the MAPS statement are best understood by referring to the information contained within this accompanying document.…”

Section: The Maps Explanation and Elaboration Papermentioning

confidence: 99%

Preferred Reporting Items for Studies Mapping onto Preference-Based Outcome Measures: The MAPS Statement

et al. 2015

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Background. “Mapping” onto generic preference-based outcome measures is increasingly being used as a means of generating health utilities for use within health economic evaluations. Despite the publication of technical guides for the conduct of mapping research, guidance for the reporting of mapping studies is currently lacking. The MAPS (MApping onto Preference-based measures reporting Standards) statement is a new checklist that aims to promote complete and transparent reporting of mapping studies. Methods. In the absence of previously published reporting checklists or reporting guidance documents, a de novo list of reporting items was created by a working group comprised of 6 health economists and 1 Delphi methodologist. A 2-round, modified Delphi survey with representatives from academia, consultancy, health technology assessment agencies, and the biomedical journal editorial community was used to identify a list of essential reporting items from this larger list. Results. From the initial de novo list of 29 candidate items, a set of 23 essential reporting items was developed. The items are presented numerically and categorized within 6 sections, namely: (i) title and abstract; (ii) introduction; (iii) methods; (iv) results; (v) discussion; and (vi) other. The MAPS statement is best applied in conjunction with the accompanying MAPS explanation and elaboration document. Conclusions. It is anticipated that the MAPS statement will improve the clarity, transparency, and completeness of reporting of mapping studies. To facilitate dissemination and uptake, the MAPS statement is being co-published by 7 health economics and quality-of-life journals, and broader endorsement is encouraged. The MAPS working group plans to assess the need for an update of the reporting checklist in 5 years.

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“…This approach has a desirable property in that it allows estimation of flexible mean and variance functions using the data at hand, leading to consistent and efficient estimation [23]. This study followed the recently developed checklist of minimum reporting requirement for mapping studies [24] Richardson et al [25]. Among the seven chronic disease groups included in this comprehensive international study, the current paper is based on respondents who had been diagnosed with cancer (N=772).…”

Section: Introductionmentioning

confidence: 99%

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

Lamu

Olsen

2018

Qual Life Res

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Purpose: Compare alternative statistical techniques to find the best approach for converting QLQ-C30 scores onto EQ-5D-5L and SF-6D utilities, and 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 (OLS), generalized linear model (GLM), extended estimating equations (EEE), fractional regression model (FRM), beta binomial (BB) regression, logistic quantile regression (LQR), and censored least absolute deviation (CLAD). Normalized mean absolute error (NMAE), normalized root mean square error (NRMSE), r-squared (r 2 ) 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 r 2 =72.7% in the full sample, and NRMSE=0.1037, NMAE=0.0821, CCC = 0.8345 and r 2 =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.

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The MAPS Reporting Statement for Studies Mapping onto Generic Preference-Based Outcome Measures: Explanation and Elaboration

Cited by 73 publications

References 53 publications

Mapping MOS-HIV to HUI3 and EQ-5D-3L in Patients With HIV

Mapping MOS-HIV to HUI3 and EQ-5D-3L in Patients With HIV

Preferred Reporting Items for Studies Mapping onto Preference-Based Outcome Measures: The MAPS Statement

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

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