Literature DB >> 26232200

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

Stavros Petrou1, Oliver Rivero-Arias2, Helen Dakin3, Louise Longworth4, Mark Oppe5, Robert Froud6,7, Alastair Gray3.   

Abstract

BACKGROUND: The process of "mapping" is increasingly being used to predict health utilities, for application within health economic evaluations, using data on other indicators or measures of health. Guidance for the reporting of mapping studies is currently lacking.
OBJECTIVE: The overall objective of this research was to develop a checklist of essential items, which authors should consider when reporting mapping studies. The MAPS (MApping onto Preference-based measures reporting Standards) statement is a checklist, which aims to promote complete and transparent reporting by researchers. This paper provides a detailed explanation and elaboration of the items contained within the MAPS statement.
METHODS: In the absence of previously published reporting checklists or reporting guidance documents, a de novo list of reporting items and accompanying explanations was created. A two-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 categorised within six sections, namely, (i) title and abstract, (ii) introduction, (iii) methods, (iv) results, (v) discussion and (vi) other. For each item, we summarise the recommendation, illustrate it using an exemplar of good reporting practice identified from the published literature, and provide a detailed explanation to accompany the recommendation.
CONCLUSIONS: It is anticipated that the MAPS statement will promote clarity, transparency and completeness of reporting of mapping studies. It is targeted at researchers developing mapping algorithms, peer reviewers and editors involved in the manuscript review process for mapping studies, and the funders of the research. The MAPS working group plans to assess the need for an update of the reporting checklist in 5 years' time.

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Year:  2015        PMID: 26232200     DOI: 10.1007/s40273-015-0312-9

Source DB:  PubMed          Journal:  Pharmacoeconomics        ISSN: 1170-7690            Impact factor:   4.981


  50 in total

1.  Predicting EuroQoL EQ-5D preference scores from the SF-12 Health Survey in a nationally representative sample.

Authors:  William F Lawrence; John A Fleishman
Journal:  Med Decis Making       Date:  2004 Mar-Apr       Impact factor: 2.583

2.  Quality of structured abstracts of original research articles in the British Medical Journal, the Canadian Medical Association Journal and the Journal of the American Medical Association: a 10-year follow-up study.

Authors:  Ho-lun Wong; Don Truong; Anisah Mahamed; Christine Davidian; Zeeshan Rana; Thomas R Einarson
Journal:  Curr Med Res Opin       Date:  2005-04       Impact factor: 2.580

3.  Evidence-based approaches for the Ayurvedic traditional herbal formulations: toward an Ayurvedic CONSORT model.

Authors:  Saravu R Narahari; Terence J Ryan; Madhur Guruprasad Aggithaya; Kuttaje S Bose; Kodimoole S Prasanna
Journal:  J Altern Complement Med       Date:  2008-07       Impact factor: 2.579

Review 4.  A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures.

Authors:  John E Brazier; Yaling Yang; Aki Tsuchiya; Donna Louise Rowen
Journal:  Eur J Health Econ       Date:  2009-07-08

5.  Predicting health utilities for children with autism spectrum disorders.

Authors:  Nalin Payakachat; J Mick Tilford; Karen A Kuhlthau; N Job van Exel; Erica Kovacs; Jayne Bellando; Jeffrey M Pyne; Werner B F Brouwer
Journal:  Autism Res       Date:  2014-09-25       Impact factor: 5.216

6.  Comparison of the underlying constructs of the EQ-5D and Oxford Hip Score: implications for mapping.

Authors:  Mark Oppe; Nancy Devlin; Nick Black
Journal:  Value Health       Date:  2011-06-25       Impact factor: 5.725

Review 7.  Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: a systematic review, statistical modelling and survey.

Authors:  Louise Longworth; Yaling Yang; Tracey Young; Brendan Mulhern; Mónica Hernández Alava; Clara Mukuria; Donna Rowen; Jonathan Tosh; Aki Tsuchiya; Pippa Evans; Anju Devianee Keetharuth; John Brazier
Journal:  Health Technol Assess       Date:  2014-02       Impact factor: 4.014

8.  Estimating the association between SF-12 responses and EQ-5D utility values by response mapping.

Authors:  Alastair M Gray; Oliver Rivero-Arias; Philip M Clarke
Journal:  Med Decis Making       Date:  2006 Jan-Feb       Impact factor: 2.583

9.  Deriving health utilities from the MacNew Heart Disease Quality of Life Questionnaire.

Authors:  Gang Chen; John McKie; Munir A Khan; Jeff R Richardson
Journal:  Eur J Cardiovasc Nurs       Date:  2014-05-14       Impact factor: 3.908

10.  Mapping SF-36 onto the EQ-5D index: how reliable is the relationship?

Authors:  Donna Rowen; John Brazier; Jennifer Roberts
Journal:  Health Qual Life Outcomes       Date:  2009-03-31       Impact factor: 3.186

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  26 in total

1.  Mapping PROMIS Global Health Items to EuroQol (EQ-5D) Utility Scores Using Linear and Equipercentile Equating.

Authors:  Nicolas R Thompson; Brittany R Lapin; Irene L Katzan
Journal:  Pharmacoeconomics       Date:  2017-11       Impact factor: 4.981

2.  Mapping clinical outcomes to generic preference-based outcome measures: development and comparison of methods.

Authors:  Mónica Hernández Alava; Allan Wailoo; Stephen Pudney; Laura Gray; Andrea Manca
Journal:  Health Technol Assess       Date:  2020-06       Impact factor: 4.014

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

Authors:  Admassu N Lamu; Jan Abel Olsen
Journal:  Qual Life Res       Date:  2018-09-01       Impact factor: 4.147

4.  Mapping CHU9D Utility Scores from the PedsQLTM 4.0 SF-15.

Authors:  Christine Mpundu-Kaambwa; Gang Chen; Remo Russo; Katherine Stevens; Karin Dam Petersen; Julie Ratcliffe
Journal:  Pharmacoeconomics       Date:  2017-04       Impact factor: 4.981

5.  Mapping Between the Sydney Asthma Quality of Life Questionnaire (AQLQ-S) and Five Multi-Attribute Utility Instruments (MAUIs).

Authors:  Billingsley Kaambwa; Gang Chen; Julie Ratcliffe; Angelo Iezzi; Aimee Maxwell; Jeff Richardson
Journal:  Pharmacoeconomics       Date:  2017-01       Impact factor: 4.981

6.  Predicting EuroQoL 5 Dimensions 5 Levels (EQ-5D-5L) Utilities from Older People's Quality of Life Brief Questionnaire (OPQoL-Brief) Scores.

Authors:  Billingsley Kaambwa; Julie Ratcliffe
Journal:  Patient       Date:  2018-02       Impact factor: 3.883

7.  Mapping Quality of Life (EQ-5D) from DAPsA, Clinical DAPsA and HAQ in Psoriatic Arthritis.

Authors:  Tomas Mlcoch; Jan Tuzil; Liliana Sedova; Jiri Stolfa; Monika Urbanova; David Suchy; Andrea Smrzova; Jitka Jircikova; Tereza Hrnciarova; Karel Pavelka; Tomas Dolezal
Journal:  Patient       Date:  2018-06       Impact factor: 3.883

Review 8.  The Use of Mapping to Estimate Health State Utility Values.

Authors:  Roberta Ara; Donna Rowen; Clara Mukuria
Journal:  Pharmacoeconomics       Date:  2017-12       Impact factor: 4.981

9.  Mapping the Chinese Version of the EORTC QLQ-BR53 Onto the EQ-5D-5L and SF-6D Utility Scores.

Authors:  Tong Liu; Shunping Li; Min Wang; Qiang Sun; Gang Chen
Journal:  Patient       Date:  2020-10       Impact factor: 3.883

10.  Mapping the Paediatric Quality of Life Inventory (PedsQL™) Generic Core Scales onto the Child Health Utility Index-9 Dimension (CHU-9D) Score for Economic Evaluation in Children.

Authors:  Tosin Lambe; Emma Frew; Natalie J Ives; Rebecca L Woolley; Carole Cummins; Elizabeth A Brettell; Emma N Barsoum; Nicholas J A Webb
Journal:  Pharmacoeconomics       Date:  2018-04       Impact factor: 4.981

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