Next Article in Journal
Walkability and Greenness Do Not Walk Together: Investigating Associations between Greenness and Walkability in a Large Metropolitan City Context
Next Article in Special Issue
Association between Anxiety, Depressive Symptoms, and Quality of Life in Patients Undergoing Diagnostic Flexible Video Bronchoscopy
Previous Article in Journal
Factors Hindering Social Participation among Older Residents from Evacuation Zones after the Nuclear Power Plant Accident in Fukushima: The Fukushima Health Management Survey
Previous Article in Special Issue
Rasch Validation of the VF-14 Scale of Vision-Specific Functioning in Greek Patients
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 18
Issue 9
10.3390/ijerph18094428
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

Dimensions Used in Instruments for QALY Calculation: A Systematic Review

by
Moustapha Touré
1,2,
Christian R. C. Kouakou
1,2 and
Thomas G. Poder
2,3,*
1
Department of Economics, Business School, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
2
Centre de Recherche de l’IUSMM, CIUSSS de l’Est de L’île de Montréal, Montréal, QC H1N 3V2, Canada
3
Department of Management, Evaluation and Health Policy, School of Public Health, Université de Montréal, Montréal, QC H3N 1X9, Canada
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(9), 4428; https://doi.org/10.3390/ijerph18094428
Submission received: 16 March 2021 / Revised: 16 April 2021 / Accepted: 18 April 2021 / Published: 21 April 2021
(This article belongs to the Special Issue Health Related Quality of Life in Health Care)
Browse Figure
Review Reports Versions Notes

Abstract

:
Economic assessment is of utmost importance in the healthcare decision-making process. The quality-adjusted life-year (QALY) concept provides a rare opportunity to combine two crucial aspects of health, i.e., mortality and morbidity, into a single index to perform cost-utility comparison. Today, many tools are available to measure morbidity in terms of health-related quality of life (HRQoL) and a large literature describes how to use them. Knowing their characteristics and development process is a key point for elaborating, adapting, or selecting the most well-suited instrument for further needs. In this aim, we conducted a systematic review on instruments used for QALY calculation, and 46 studies were selected after searches in four databases: Medline EBSCO, Scopus, ScienceDirect, and PubMed. The search procedure was done to identify all relevant publications up to 18 June 2020. We mainly focused on the type of instrument developed (i.e., generic or specific), the number and the nature of dimensions and levels used, the elicitation method and the model selected to determine utility scores, and the instrument and algorithm validation methods. Results show that studies dealing with the development of specific instruments were mostly motivated by the inappropriateness of generic instruments in their field. For the dimensions’ and levels’ selection, item response theory, Rasch analysis, and literature review were mostly used. Dimensions and levels were validated by methods like the Loevinger H, the standardised response mean, or discussions with experts in the field. The time trade-off method was the most widely used elicitation method, followed by the visual analogue scale. Random effects regression models were frequently used in determining utility scores.

1. Introduction

In the face of growing demand for health services, public and private agencies are increasingly interested in knowing the relative cost-effectiveness of programs [1]. In this setting, the quality-adjusted life-year (QALY) concept has grown in popularity and is now used as a measure of benefit in the economic evaluation of health programs and technologies all around the world [2]. The principle of the QALY is to combine the duration (mortality) and quality (morbidity) of life into a single measure [3]. Such a combination allows comparisons between various intervention in health care [4]. If duration is simply estimated in a QALY calculation as the number of years lived in a given health state, quality of life is characterized by a utility value between 0 and 1, where 0 represents death and 1 represents perfect health. Many instruments to measure the Q in QALY have been developed, while some are generic, others are specific [5]. The purpose of all these instruments is to reflect respondents’ perceived health onto a utility continuum in the aim to capture the comparative effectiveness of healthcare intervention or programs [1,2,6,7].
To be usable in cost-utility studies, instruments must meet several essential criteria. The development of these instruments is done in several stages to ensure their reliability and validity. These steps, which are common to both generic and specific instruments, are generally described under three aspects: development, validation of psychometric properties, and measurement (i.e., valuation exercise to elicit health state values) [8,9]. In the aim to face further challenges and better assess programs that impact current or new fields, researchers and decision makers need adapted instruments. To develop, adapt, or select the appropriate instrument is thus necessary, and to do so, it is essential to master the different stages of the development process. There is a rich literature dealing with the development of various instruments for evaluation purposes. However, few studies describe, through a clear process, the development of preference-based instruments that can be used for cost-utility analysis [10]. The purpose of this systematic review was to analyze the different phases of the development of the instruments used in QALY determination in various countries. More specifically, it was to determine the dimensions and levels used in these instruments and to specify how these dimensions and their utility scores were obtained. To report this, the stages mentioned above were followed. By doing so, we provide a synthesis of the instruments’ development process based on existing literature that can benefit to the research community when developing or improving instruments for QALY calculation. Next sections present the methodology used for the systematic review, the results and the discussion.

2. Method

2.1. Search Strategy

The databases consulted were Medline EBSCO, Scopus, ScienceDirect (Elsevier), and PubMed. Grey literature searches were also conducted via Google Scholar and ResearchGate. The bibliographic references of the selected articles were used as a source to find other relevant studies. The keywords used in the different databases were ‘QALY’, ‘quality adjusted life year’, ‘instrument’, ‘multi-attribute’, and ‘utility’. Using Boolean operators, combinations were made to refine the results and get closer to the type of study requested. There was no restriction on the publication date and only publications in English or French were considered. Searches were conducted in English in the databases mentioned above. The search procedure was conducted up to 18 June 2020.

2.2. Selection of Studies

In accordance with our literature search protocol (i.e., an unpublished 2-page document in French to ensure consistency and reproducibility), the selection of studies was based on the following criteria: Studies published in French or English; studies describing the development of instruments for QALY calculation; and studies addressing the general population or specific patient groups.
Studies dealing with draft versions of instruments that have been subsequently modified and published, using an instrument for QALY calculation without a description of its dimensions and levels, using instruments that do not measure health utilities, and dealing with the paediatric population were not included.
The selection of studies was conducted in 2 steps. A group of 2 reviewers (M.T. and C.R.C.K.) first made the first selection after reading the titles and abstracts. The selected articles were then read in full and only those that met the inclusion criteria were selected. At each step, in case of disagreement between the 2 evaluators, the reason for this disagreement was submitted to a third evaluator (T.G.P.) who performed an arbitration. A Kappa coefficient was calculated in both steps. Data extraction was done by one evaluator (M.T.) and validated by another one (C.R.C.K. or T.G.P.).

2.3. Data Analysis

Data extraction was performed using a form structured around the instrument’s development process. Thus, the main information to be collected was related to the 3 aspects of instrument elaboration: development, validation, and measurement. Specifically, we were interested in the target population, the type of instrument developed, the number and nature of dimensions and levels, the elicitation method and model used in the determination of utility scores, and the methods used to validate the tool and the utility algorithm. As regards to the dimensions or attributes selected in the instruments, we grouped them into the three main areas of health described by the World Health Organization (WHO), i.e., physical, mental, and social. Additionally, when available, we collected information on the sociodemographic characteristics of the participants in the different stages, and the method used to recruit them. The analysis of the quality of the studies was done with the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) grid [11].

3. Results

3.1. Selection of Studies

Following our search strategy, a total of 4270 studies were found. At the end of the filtering processes, 50 articles were fully read and 46 met the inclusion criteria. Figure 1 shows the PRISMA flowchart describing the selection process. At the first stage of the selection, 2740 publications were excluded and a kappa coefficient equal to 0.37 was obtained. In the second stage of the selection, four studies were excluded because they did not concern instrument’s development process for QALY calculation or were related to the development process of previous versions of an instrument used in QALY determination that is no longer in use. Consequently, and as mentioned in the Method section, only updated versions of instruments were considered. A kappa coefficient of 0.65 was found at this stage and a third evaluator had to intervene to decide between disagreements related to two studies. This review thus considered 46 studies dealing with the development of 48 preference-based instruments usable in QALY calculation using their own value set.

3.2. Characteristics of the Selected Studies

Of the 46 studies that met the inclusion criteria, 12 dealt with the development of generic instruments (12 generic instruments) and the remainder (n = 34) were about the development of instruments for specific health conditions (36 specific instruments). Countries of application of these studies were United Kingdom (n = 20), United States of America (n = 6), Australia (n = 5), Holland (n = 5), Canada (n = 3), Spain (n = 1), Finland (n = 1), England (n = 1), and South Korea (n = 1). The remaining studies were carried out simultaneously in several of the above-mentioned countries (n = 3). The specific instruments developed refer to a wide variety of areas related to social care and dependency (n = 7), neurological disorders (n = 6), respiratory problems (n = 4), cancer (n = 3), diabetes (n = 3), sexuality/fertility (n = 3), bladder (n = 2), menopause/flushing (n = 2), musculoskeletal disorders (n = 2), vision/glaucoma (n = 2), digestive function (n = 1), and prostate (n = 1). All studies were published between 1998 and 2020.

3.3. Instrument Development

The development of preference-based tools provide a mean of measuring health states preferences in a field where such instruments are non-existent or to overcome the problem of unsuitability of existing instruments (e.g., sensitivity problems, missing dimension) [10,12,13]. Instruments vary in their composition, length, and in what they intend to measure. Then, the scheme (study process and design, target population, etc.) followed in the development of an instrument often depends on the motivation behind its creation [14]. A distinction will therefore be made between specific and generic instruments. Specific instruments are more oriented towards specific conditions or diseases but do not allow comparison between the quality of life of patients with different diseases, whereas generic instruments can be used by any type of patient, regardless of their health profile, and allow comparison between different patients with different diseases [14]. Thus, to allow for a better allocation of available resources, various generic and specific instruments have been developed. In addition, it should be noted that some of the preference-based tools were originally developed in that purpose while others were pre-existing instruments that were modified to be preference-based. A total of 48 instruments make up this review, 25 of which are the result of improvements to existing instruments and 23 of which were developed de novo.
Less than a quarter of the studies included in this review concerned the development of generic instruments (n = 12). These instruments are the 15-dimensional (15D), the assessment of quality of life 7-dimension (AQoL-7D), the assessment of quality of life-8 (AQoL-8D), the computerized adaptive tool 5-dimension (CAT-5D-QOL), the clinical outcomes in routine evaluation 6-dimension (CORE-6D), the EuroQol 5-dimension (EQ-5D), the health utilities index (HUI2 and HUI3), the patient reported outcomes measurement information system-29 (PROMIS-29), the quality of well being self-administered (QWB-SA), the recovering quality of life utility index (ReQoL-UI), and the second version of the short-form 6-dimension (SF-6Dv2). Half of these studies (n = 6) describe the improvement of a pre-existing tool because of limitations noted in its use. This is the case for Hawthorne (2009) [10], Seiber et al. [15], and Richardson et al. [16] who dealt with the development of parsimonious tools from AQoL and QWB, respectively, which would satisfy the axioms of utility theory and would be able to overcome the limitations observed in those instruments. To do so, they suggested switching from original versions (AQoL, AQoL-6D, and QWB) to AQoL-7D, AQol-8D, and QWB-SA, respectively. Hawthorne [10] thus retained eight items through an iterative process of entering and removing the items proposed in the AQoL model. This process was repeated until all possible combinations of items were examined. Richardson et al. [16] proposed to increase the sensitivity of AQoL to sight-related difficulties and disabilities. Vision-related quality of life (VisQol) was thus added as a dimension to AQoL-6D. Seiber et al. [15] explained the implementation of the QWB-SA, derived from the quality of well being (QWB). The QWB-SA is a tool offering the same properties as the latter, while being less time consuming and easier to use.
In the case of Herdman et al. [12] and Brazier et al. [7], the concerns was more about the sensitivity of previous versions. Consequently, they introduced the EQ-5D-5L and SF-6Dv2, respectively. These authors wished to remedy the problems of the ‘ceiling effect’ and ‘floor effect’ from which the first versions of these instruments suffered (respectively the EQ-5D-3L and the SF-6Dv1). The main changes were provided in the nature of the severity levels in different dimensions, leading to an increased number of possible combinations from 243 to 3125 for EQ-5D-5L and from 18,000 to 18,750 for SF-6Dv2. For this purpose, a literature review on the response scales and interviews with native speakers of the different target languages and experts were conducted. In addition, the exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and Rash’s analysis made it possible to retain the elements relevant to the new tools. These same techniques were used in the development of CORE-6D, PROMIS-29, and ReQoL-UI. Indeed, factor analyses are techniques that ensure a certain structural independence of the dimensions defined in the instruments by identifying the factors underlying the correlation patterns in a set of observed variables [17]. The Rasch analysis is a probabilistic method that models the probability of correct response conditional on the levels of difficulty of the question and the respondent’s ability [18]. Rasch analysis therefore allows, using measurement intervals, to evaluate different psychometric properties such as unidimensionality (the degree to which the tool measures the same aspect), targeting (the degree to which the instrument is appropriate for respondents in terms of difficulty), item severity (the order in which items are set up), and separation (the way in which items distinguish levels of functionality in different domains) [19].
Table 1 provides an overview of the dimensions and levels covered by the different generic instruments identified, while Table 2 identifies the different methods used in the different phases of the development of these generic instruments. In Table 1, the instrument that covered the most dimensions was the QWB-SA, followed by the AQoL-8D, the AQoL-7D, the PROMIS-29, and the 15D. The ReQoL-UI records the fewest dimensions, which can be misleading since it covers many subdimensions in mental health. All instruments record dimensions related to physical discomfort/pain and almost all instruments had dimensions related to mobility/ambulation. Only one instrument (CORE-6D) did not record dimensions on mobility/ambulation. Four instruments reported dimensions relative to eating/nutrition and autonomy/control/dependence. Seven instruments addressed the sadness/depression issue. Five and eight instruments had dimensions related to mental/cognitive function and anxiety/distress, respectively. Five instruments were interested in well-being/happiness/satisfaction. Fertility was only considered in HUI2 and sexual activity in 15D and QWB-SA. Dimensions related to terror/panic/fear and humiliation/shame were present in two instruments (CORE-6D and PROMIS-29). The number of levels per dimension varied between 2 (QWB-SA) and 11 (PROMIS-29). The number of possible combinations ranged from 3125 (EQ-5D-5L) to 3.55957 × 1024 (AQoL-8D).
Among the specific instruments that were developed (n = 36), their authors were mostly motivated by a problem of inadequacy of existing tools due to their lack of sensitivity or their psychometrically invalid nature in their field of interest (n = 23). Other instruments (n = 13) were simply developed because of the non-existence of a measurement tool or the fact that existing tools were not usable in economic evaluation because they were not based on individual preferences. Table 3 shows the dimensions and levels used in the various specific instruments.
Several studies (n = 18) specified that a literature review of old instruments and exchanges with professionals and/or patients helped in the selection of dimensions and levels. In addition to these resources, half (n = 18) of the studies stated that they used empirical methods such as the factor analysis, Rasch analysis, standard psychometric criteria, and differential item functioning (DIF) in the selection of the dimensions and levels shown in Table 3.

3.4. Psychometric Validation

Following the selection of the items to make up the instrument, it was subjected to qualitative and quantitative tests to ensure its reliability, consistency, and validity (internal and external) [26,27]. Messick [28] defines validity as an integrated evaluative judgment of the degree to which empirical evidence and theoretical evidence supports the adequacy and appropriateness of interpretations and actions based on test scores or any other mode of assessment.
Among the 12 generic instruments, the method used to test the validation of dimensions and levels was provided for 6 tools (see Table 2). Hawthorne [10] tested the unidimensionality of the descriptive system and the degree of homogeneity using item response theory (IRT) and Loevinger’s H coefficient, respectively. Indeed, IRT was first proposed in the field of psychometrics and is currently widely used in the fields of health and education. Its methodology significantly improves the accuracy and reliability of measurement instruments while providing significant reductions in the time and effort required for assessments [18].
From their side, Herdman et al. [12] asked participants to assess the interpretability and plausibility of the instrument. Using subsamples, Brazier et al. [7] and Seiber et al. [15] used the DIF and the test–retest, respectively. In addition, the latter tested the impact of the questionnaire administration method on the scores obtained. The DIF examines the relationship between the response to an item and a group characteristic (e.g., gender, race, and level of education). Thus, the question answered is whether or not the response to a question in an item is due to belonging to a group [29]. As for the test–retest, it allows to measure the reliability of the instrument by observing the constancy of the scores by measuring a stable characteristic at different periods [30]. Sintonen [20] stated that for its validation, the 15D was compared to other instruments such as the Nottingham Health Profile (NHP), the 20-Item Short-Form Health Survey (SF-20), and the EQ-5D. Only three studies provided information on the samples used for the validation of the different generic instruments [7,10,12].
Regarding the validation of specific instruments, less than the third of instruments (n = 10) provided their validation method (see Table 4). The two versions of the DHP (DHP3 and DHP5) were validated by collecting the opinions of professionals in the field after presenting them with the results of the item selection. The sensitivity of OAB-5D and EORTC-8D was tested using the standardised response mean (SRM) on random samples from the initial database and on an independent sample of patients. This method was used to measure sensitivity in patients with minimal change in health status between visits. It is obtained by dividing the change in mean score by the standard deviation of that change [31]. The validity of the ASCOT was tested by comparing it with other instruments such as the EQ-5D and the general health questionnaire (GHQ-12). This was done using the Chi-square test and the analysis of variance. A comparison with other instruments was also performed for the DUI and P-PBMSI using the Cohen criterion, Spearman’s correlation, and Pearson’s correlation. A patient group test–retest was used for the validation of the CAMPHOR, the menopause specific health quality of life questionnaire, and the RSUI to assess the reliability and validity of the construction of these instruments. Finally, the IIEF was validated following confirmation of the consistency of the ordinal structure of its dimensions.

3.5. Measuring Utility Scores

The final step in the process of creating a preference-based instrument is the measurement of individual preferences. This involves assigning a utility score to the different possible health states described by each instrument. To do this, a health preference survey is filled out by a sample of individuals [1,32,33]. Different elicitation methods are used to obtain the preferences of individuals [32,33,34]. A review of elicitation methods is available in the work by Fauteux and Poder [32]. Due to the often large number of possible combinations offered by an instrument, a subset of health states is frequently chosen to be assessed directly by participants, and the utility levels or scores of the remaining health states are then modelled and estimated from the results of the sample of health states chosen at baseline [16]. In this exercise to assess the selected health states, two thirds of the instruments used the preferences of individuals from the general population (n = 33) compared to less than one third that used patient preferences (n = 9). Only five instruments were valued by both parties. More than two thirds of the elicitations of the selected health states were made by direct interviews (n = 34), eight instruments were evaluated through remote methods (online survey and postal mail), and one study used a pre-existing database. Five studies did not provide information on the mode of elicitation. In addition, 85% of the studies provided information on the number of participants, and of these, 95% provided details on the characteristics of the participants (e.g., age, level of education). However, only 41% of the studies (n = 19) stated that the sample used was representative of the target population.
There is some diversity in the elicitation methods used in studies dealing with the development of generic instruments. Time trade-off (TTO) was the most used method (n = 4), followed by the visual analogue scale (VAS) (n = 3), standard gamble (SG) (n = 1), and discrete choice experiment (with duration) (DCEtto) (n = 2). One study used a hybrid method combining VAS and SG (i.e., for HUI2 and HUI3). The utility scores obtained for the selected health states through these different elicitation methods provided scores for all other possible combinations of health states using different models. The additive regression model was used for AQoL-8D, 15D, QWB-SA, and CORE-6D; the conditional logit for SF6-Dv2 and the multiplicative model for CAT-5D-QOL, HUI2, and 3, and AQoL-7D. The random effects model was used for ReQoL-UI and the relativity model for the PROMIS-29.
The models, once estimated, were validated to ensure the reliability of the results. Different possibilities allow the validation of the models. For example, the preferred model for the AQoL-8D was the one that had the closest utility scores to the original instrument (AQoL) and the highest degree of correlation with it. For the CAT-5D-QOL, a comparison of its scores with those of the HUI3 allowed one to select the best specification. For the SF6-Dv2, heterogeneity was tested and the 15D had its preferred model selected using correlation analyses with different samples. For AQoL-7D, the analysis of its ability to discriminate between the general population and patients allowed its model to be validated. The analysis of the specification of the different models used (significance of the coefficients, mean absolute error, root mean standard error, etc.) made it possible to validate the best model for CORE-6D and ReQoL-UI. For the PROMIS-29, the ability of the model to predict pair-specific probabilities in terms of least squared error was used to determine the best model.
Table
Table 4. Methods used for the development of specific instruments.
Table 4. Methods used for the development of specific instruments.
InstrumentsMethod of Choice of Dimensions and LevelsValidation MethodElicitation MethodModel UsedReferences
Social care and dependency
Aberrant Behaviour Checklist Utility Index (ABC-UI)Factor and Rasch analyses, consultation with clinical expertsNot foundTTOMaximum likelihood with random effects[35]
Adult Social Care Outcomes Toolkit (ASCOT)Literature review on old instruments; empirical analysisComparison with other measurement toolsTTO; DCE; BWSMultinomial logit model[9]
Carer Quality of Life—7 Dimensions (CarerQol-7D)Review of existing instruments; Experts’ opinion.Not foundDCEPanel mixed multinomial parameter model including main and interaction effects (MMNL)[36]
Dependency 6 dimensions (DEP-6D)Non availableNot foundTTORandom effects regression model[37]
Impact of Weight on Quality of Life—Lite (IWQOL-Lite)Non availableNot foundDCERandom effects ordered probit[38]
Index of capability for older people (ICECAP-O)Iterative interviews until convergenceNot foundBest–worst scaling (BWS)Conditional logistic regression[39]
Older Persons Utility Scale (OPUS)Consultation with individuals drawn from local authority senior and middle managersNot foundDCERandom effects probit model[40]
Neurological disorders
Alzheimer’s disease (AD-5D)Factorial analysis; Rasch analysisNot foundTo be developedTo be developed[41]
Amyotrophic Lateral Sclerosis Utility Index (ALSUI)Non availableNot foundVAS; SGMultiplicative model[42]
Cerebral palsy-specific 6 dimensions (CP-6D)Factorial analysis, Rasch analysis.Not foundDCE with duration (DCEtto)Conditional logit, mixed logit[43]
Epilepsy-specific preference-based measure (NEWQOL-6D)Exploratory factor analysis, Rasch and psychometric analyses, DIFNot foundTTOGeneralized least squares regression[8]
Multiple Sclerosis Impact Scale 29 (MSIS-29)Rasch model, basic psychometric criteria, clinical expert opinionNot foundTTORandom effects model[44]
Prototype Preference-Based MS Index (P-PBMSI)Rasch analysis, threshold graph, WHO International Classification of Functioning, Disability and Health.Comparison with other instruments; Cohen criterion; Spearman and Pearson correlations.VASSimple linear regression[45]
Respiratory problems
Asthma Quality of Life (AQL-5D)Non availableNot foundTTOFixed-effect model[46]
Cambridge Pulmonary Hypertension Outcome Review (CAMPHOR)Percent affirmation of items; logit location in Rasch analysisTest-retestTTOOrdinary least squares; Random effects model. [47]
Chronic obstructive pulmonary disease (COPD)Non availableNot foundTTO; VASLinear mix model[48]
Rhinitis Symptom Utility Index (RSUI)Literature review, interviews with patients and experienced cliniciansTest-retest, comparison of RSUI with other indicators of disease severityVAS; SGMultiplicative model[49]
Cancer
European Organization for Research and Treatment of Cancer (EORTC-8D)Factorial analysis, Rasch analysis, expert opinionStandardised Mean Response (SRM)TTOMultivariate regression model[50]
Quality of Life Questionnaire for Cancer 30 (QLQ-C30)Rasch model, basic psychometric criteria, clinical expert opinionNot foundTTORandom effects model[44]
Quality of Life Utility Measure—Core 10 Dimensions (QLU-C10D)Experts’ opinion; Confirmatory factor analysis (CFA); Rasch analysis; DIF; Patients’ opinion.Not foundDCEConditional logistic regression[51]
Diabetes
Diabetes Health Profile 3 and 5 dimensions (DHP-3D; DHP-5D)Exploratory factor analysis; consultation with professionals in the field; Rasch analysis.Validation by professionals in the fieldTTOGeneralized least squares with random effects[52]
Diabetes Utility Index (DUI)Non availableComparison with other toolsVAS; SGSimple linear regression model[53]
Sexuality/fertility
International Index of Erectile Function (IIEF)Non availableConsistency of IIEF ordinal structureTTONon available[54]
Labour and Delivery Index (LADY-X)Interviews with patients; Experts’ opinion.Not foundDCEPanel mixed logit model (MMNL)[55]
Sexual quality of life questionnaire (SQOL-3D)Psychometric criteriaNot foundTTO; DCE; RankingOrdinary least squares and random effects model; Ordered logit[56]
Bladder
King’s Health Questionnaire (KHQ)Relevance of quality of life, percentage of items completed, face and construct validity of items, score distribution and responsiveness.Not foundSGRandom effects models[57]
Overactive Bladder 5 dimensions (OAB-5D)Factorial analysis; Rasch analysisStandardised response mean (SRM) methodTTOOrdinary least squares; random effects model “one-way error components”.[17,58]
Menopause/flushing
Flushing Symptoms Questionnaire (FSQ)Rasch analysisNot foundTTOOrdinary least square[59]
Menopause specific health quality of life questionnaireFocus group sessions with patients, literature review, expert opinion, standard psychometric criteriaTest–retest reliability, face validity, construct validity and convergent validity.TTORandom effects models[60]
Musculoskeletal disorders
Dupuytren’s contracture (DC)Non availableNot foundDCEConditional logit[61]
Health Assessment Questionnaire for arthritis (HAQ)Rasch model, basic psychometric criteria, clinical expert opinionNot foundTTORandom effects model[44]
Vision/glaucoma
Glaucoma Utility Index (GUI)Review of existing instruments on vision and glaucoma; advice from experts in the fieldNot foundDCEConditional logit regression model[62]
Visual Function Questionnaire–Utility Index (VFQ-UI)Rasch analysis, expert opinion.Not foundTTOMultivariate regression[63]
Digestive function
Short Bowel Syndrome-specific quality of life scale (SBS-QoL)Factor analysis and item performance analysis, expert opinionNot foundLT-TTORandom effects model[64]
Prostate
International prostate symptom score (IPSS)Factorial analysisNot foundTTONon available[65]
Note: VAS = Visual analogue scale; TTO = Time trade-off; SG = Standard gamble; DCE = Discrete choice experiment; DCEtto = Discrete choice experiment with duration; BWS = Best-worst scaling; LT-TTO = Lead time-time trade-off.
In terms of the elicitation methods used for specific instruments, the TTO was the most frequently used method. Indeed, about half (n = 16) of the 36 instruments concerned were valued by this method. Some studies exclusively used a DCE (n = 8), VAS (n = 1), or best worst scaling (BWS) (n = 1). A mixed method was preferred in six studies, three of which used VAS and SG, another used TTO and VAS. The last two remaining studies estimated models with preferences obtained from three elicitation methods namely TTO, DCE, and BWS on the one hand and TTO, DCE, and ranking on the other hand. Then, comparisons were made to figure out which method (model) allows a better prediction of health states scores.
In order to estimate the utility scores of the various remaining combinations, the authors used different models such as random effects models (n = 12), simple ordinary or generalized least squares (n = 6), multiplicative models (n = 2), conditional logit or maximum likelihood models (n = 9), and multivariate/multinomial models (n = 3). Most of these different models proved their validity by the consistency of the model judged through its specifications (e.g., R2, root mean square error, SRM, sign of the coefficients, significance of the coefficients, AIC, and BIC criteria) (n = 17). Six studies made comparisons either with other instruments or with scores obtained with a population other than the one used in the initial study. One study used the Hausman test and Ljung-Box statistics, and another used the likelihood ratio (LR) test. Seven studies did not provide information on how the algorithm was validated.

4. Discussion

This work addressed the main steps in the development of preference-based measurement instrument for QALY calculation. The development of new instruments or the modification of existing ones requires an understanding of the different phases involved in the development of measurement tools. These phases are generally development, validation, and measurement. In this systematic review, 46 studies were selected, tracing the development of 48 preference-based instruments for use in economic evaluations. Among these instruments, 25 corresponded to improvements in existing instruments and 23 were developed de novo. Twelve instruments were generic and 36 were specific. The number of dimensions retained per instrument varied between 2 and 15 and the number of levels between 2 and 11. All generic instruments contained one or more dimensions related to physical discomfort/pain. Almost all these instruments addressed mobility/ambulation, at the expense of other dimensions such as mental/cognitive function, well-being/happiness/satisfaction, or sadness/depression. Literature reviews, Rasch analysis, IRT, and expert opinion were mostly used to select the final dimensions and levels for the different tools.
Most of the instrument’s validation was done by test–retesting, comparisons with other instruments, and the SRM method. However, several authors did not mention the method used to validate the instruments. The conversion algorithms were mainly designed using random effects models and the most widely used elicitation method was TTO. The study of the specifications of the different models and a comparison of the results obtained with those of different instruments or subsamples mainly allowed the final model to be retained. More than three-quarters (85%) of the studies provided details on the number of participants in the elicitation phase and almost all of those (95%) provided information on the characteristics of the participants.
At the time of study selection, rigor in methodology or the amount of information available was not a criterion for inclusion. For example, during data extraction, several studies did not provide information on important aspects of the tool development process such as the sampling strategy or the method of recruiting participant samples. In view of these aspects, it seems likely that biases may remain in the measurement of the utilities or in the algorithms derived from this information. Moreover, only 41% of the studies claimed to have used a representative sample of the target population in their work. This raises the question of the external validity of the various instruments.
Therefore, additional steps could be taken to ensure the operationality of the instrument or to provide a confidence interval for the results obtained. Sensitivity analysis is one such step. It is defined as a method to identify how different sources of uncertainty in the model (algorithm) can affect the value of the result obtained [66]. It thus makes it possible to account for the degree of stability or variability of the result provided. However, of all the studies selected, few were listed as having performed a sensitivity analysis (n = 3).
Nevertheless, the average quality of the studies constituting this review is acceptable and allows a clear description of the process used. Table 5 presents the quality of the different studies with regard to the COSMIN grid, which allows an evaluation of the quality of the studies according to different criteria (e.g., content validity, consistency, and reliability of the tool). Four levels of response are allowed, ranging from “very good” to “inadequate” depending on the criteria assessed. Table 5 provides the proportion of responses for each possible level of response and for the different criteria in the grid. On average, 56% of the various criteria assessed were rated as “very good” and 37% were rated as “doubtful or undetermined”. Only 6% of the criteria were rated, on average, as “inadequate”.
High diversity has been noticed in the instruments identified in this systematic review. This is the proof of an increasing interest in the field of health economics, especially in utility measurement. This provides researchers and policy makers diversified tools to appropriately assess programs and efficiently proceed to resources allocation. However, the variety of tools may also cause some concerns in programs comparison. It has been showed that instruments used in the same field may yield to different scores due to differences in their descriptive systems (i.e., domains of health covered) or valuation techniques used [16,67]. For example, Richardson et al. [16] found that individual with visual disturbances and hearing impairments had a score of 0.14 with the HUI 3 and 0.8 with the EQ-5D. Thus, a program that would allow a full recovery for this patient (i.e., utility of 1) would record a utility gain 4.3 times greater using the HUI 3 rather than the EQ-5D. This would mean that, for this program, the use of the EQ-5D would have the same effect than an increase of 4.3 times the cost on the cost/QALY ratio. To resolve these issues, guidelines as to which instrument to use in the estimation of QALY have been elaborated in some countries [67]. Decision makers must then be aware of pros and cons of each instrument to be able to select the most adequate one for their needs. This selection is nevertheless quite difficult to enforce in practice. In order to compensate this difficulty and provide important additional information on accuracy and reliability of results, Feeny et al. [68] recommend to use two or more instruments in studies.
Most of instruments in this systematic review are specific (n = 36). This shows the recent emphasis given to the development and use of specific instruments over generic instruments. Indeed, the development of most of the specific instruments discussed in this work is justified by the lack of sensitivity or psychometrical invalidity of generic instruments in the field concerned. From this point of view, it would be important to have a specific instrument for each field requiring it for a better measurement of preferences and a better allocation of resources. The specific instruments identified in this study cover only a small part of the many areas of health care, consequently, there is a long way to go to allow more domains to have a specific instrument.
In addition, the question of the target population in the process of developing the various specific instruments arises. For institutions, such as the US Public Health, it is important to use a representative sample of the general population if the assessments obtained are “informed, unbiased and competent” [69,70]. National Institute of Care for Health and Care Excellence (NICE) also advocates the argument that in a publicly funded health care system, the purpose of economic evaluation is not to make decisions at the individual patient level but to allow policies that serve the interests of society as a whole to emerge [69,71]. However, the use of utility values from the general population becomes problematic if these values differ substantially from those of patients. For this reason, several authors believe that patients should be directly addressed in the elicitation of preference scores [69,72,73]. It is nevertheless noted that nearly two thirds of the specific instruments present in this work used the preferences of individuals from the general population only (n = 26).

5. Conclusions

This systematic review on the development of preference-based instruments identified the various stages required to develop an instrument to measure QALY. This work thus provides a better understanding of the process of developing preference-based instruments for QALY calculation. Most of the studies that have focused on the development of specific instruments have been done because of the verified inadequacy of generic instruments in some areas. A great diversity was observed in the different methods used in the different stages of the development of the instruments. Rasch analysis, TTO, and random effects models were predominantly used in instrument development and measurement. Noting the high variability among studies in the process of developing the instruments included in this review, it would be very helpful to have a standardized method for the development of preferences-based instruments like what has been done for the experiment design of DCEs [74].

Author Contributions

Conceptualization, M.T. and T.G.P.; methodology, M.T., C.R.C.K. and T.G.P.; validation, T.G.P.; data curation, M.T. and C.R.C.K.; writing—original draft preparation, M.T.; writing—review and editing, M.T., C.R.C.K. and T.G.P.; supervision, T.G.P.; funding acquisition, T.G.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was founded by the Fondation de l’IUSMM and the Centre de recherche de l’IUSMM. The Centre de recherche de l’IUSMM is funded by the FRQ-S. T.G.P. is a fellow of the FRQ-S.

Data Availability Statement

Not applicable.

Acknowledgments

We acknowledge the reviewers for their important comments. We thank Jie He from the University of Sherbrooke for her support.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Brazier, J.; Usherwood, T.; Harper, R.; Thomas, K. Deriving a preference-based single index from the UK SF-36 health survey. J. Clin. Epidemiol. 1998, 51, 1115–1128. [Google Scholar] [CrossRef]
  2. Mavranezouli, I.; Brazier, J.E.; Rowen, D.; Barkham, M. Estimating a preference-based index from the Clinical Outcomes in Routine Evaluation–Outcome Measure (CORE-OM): Valuation of CORE-6D. Med Decis Mak. 2013, 33, 381–395. [Google Scholar] [CrossRef] [PubMed]
  3. Brazier, J.E.; Yang, Y.; Tsuchiya, A.; Rowen, D.L. A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. Eur. J. Health Econ. HEPAC Health Econ. Prev. Care 2010, 11, 215–225. [Google Scholar] [CrossRef]
  4. Weinstein, M.C.; Torrance, G.; McGuire, A. QALYs: The basics. Value Health 2009, 12, S5–S9. [Google Scholar] [CrossRef] [Green Version]
  5. Richardson, J.R.J.; Mckie, J.R.; Bariola, E.J. Multiattribute utility instruments and their use. Encylopedia Health Econ. 2014, 2, 341–357. [Google Scholar]
  6. Chen, G.; Ratcliffe, J. A Review of the development and application of generic multi-attribute utility instruments for paediatric populations. Pharmacoeconomics 2015, 33, 1013–1028. [Google Scholar] [CrossRef] [PubMed]
  7. Brazier, J.E.; Mulhern, B.J.; Bjorner, J.B.; Gandek, B.; Rowen, D.; Alonso, J.; Vilagut, G.; Ware, J.E. Developing a new version of the SF-6D health state classification system from the SF-36v2: SF-6Dv2. Med. Care 2020, 58, 9. [Google Scholar] [CrossRef]
  8. Mulhern, B.; Rowen, D.; Jacoby, A.; Marson, T.; Snape, D.; Hughes, D.; Latimer, N.; Baker, G.A.; Brazier, J.E. The development of a QALY measure for epilepsy: NEWQOL-6D. Epilepsy Behav. 2012, 24, 36–43. [Google Scholar] [CrossRef]
  9. Netten, A.; Burge, P.; Malley, J.; Potoglou, D.; Towers, A.-M.; Brazier, J.; Flynn, T.; Forder, J.; Wall, B. Outcomes of social care for adults: Developing a preference-weighted measure. Health Technol Assess 2012, 16. [Google Scholar] [CrossRef] [Green Version]
  10. Hawthorne, G. Assessing utility where short measures are required: Development of the short assessment of quality of life-8 (AQoL-8) instrument. Value Health 2009, 12, 948–957. [Google Scholar] [CrossRef]
  11. Mokkink, L.B.; Prinsen, C.A.; Patrick, D.L.; Alonso, J.; Bouter, L.M.; de Vet, H.C.; Terwee, C.B. COSMIN study design checklist for patient-reported outcome measurement instruments. Gut 2020, 70, 139–147. [Google Scholar] [CrossRef]
  12. Herdman, M.; Gudex, C.; Lloyd, A.; Janssen, M.; Kind, P.; Parkin, D.; Bonsel, G.; Badia, X. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual. Life Res. 2011, 20, 1727–1736. [Google Scholar] [CrossRef] [Green Version]
  13. Oppe, M.; Rand-Hendriksen, K.; Shah, K.; Ramos-Goñi, J.M.; Luo, N. EuroQol protocols for time trade-off valuation of health outcomes. Pharmacoeconomics 2016, 34, 993–1004. [Google Scholar] [CrossRef] [Green Version]
  14. Olsen, J.A.; Misajon, R. A conceptual map of health-related quality of life dimensions: Key lessons for a new instrument. Qual. Life Res. 2020, 29, 733–743. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Seiber, W.J.; Groessl, E.J.; David, K.M.; Ganiats, T.G.; Kaplan, R.M. Quality of Well Being Self-Administered (QWB-SA) Scale; Health Services Research Center, University of California: San Diego, CA, USA, 2008; p. 41. [Google Scholar]
  16. Richardson, J.; Iezzi, A.; Peacock, S.; Sinha, K.; Khan, M.; Misajon, R.; Keeffe, J. Utility weights for the vision-related Assessment of Quality of Life (AQoL)-7D instrument. Ophthalmic Epidemiol. 2012, 19, 172–182. [Google Scholar] [CrossRef] [PubMed]
  17. Young, T.; Yang, Y.; Brazier, J.E.; Tsuchiya, A.; Coyne, K. The first stage of developing preference-based measures: Constructing a health-state classification using rasch analysis. Qual. Life Res. 2009, 18, 253–265. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. An, X.; Yung, Y.-F. Item response theory: What it is and how you can use the IRT procedure to apply it. SAS Inst. Inc. 2014, 10. [Google Scholar]
  19. Duncan, P.W.; Bode, R.K.; Min Lai, S.; Perera, S. Rasch analysis of a new stroke-specific outcome scale: The stroke impact scale11no commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit upon the author(s) or upon any organization with which the author(s) is/are associated. Arch. Phys. Med. Rehabil. 2003, 84, 950–963. [Google Scholar] [CrossRef]
  20. Sintonen, H. The 15D Instrument of health-related quality of life: Properties and applications. Ann. Med. 2001, 33, 328–336. [Google Scholar] [CrossRef]
  21. Kopec, J.A.; Sayre, E.C.; Rogers, P.; Davis, A.M.; Badley, E.M.; Anis, A.H.; Abrahamowicz, M.; Russell, L.; Rahman, M.M.; Esdaile, J.M. Multiattribute health utility scoring for the computerized adaptive measure cat-5d-qol was developed and validated. J. Clin. Epidemiol. 2015, 68, 1213–1220. [Google Scholar] [CrossRef]
  22. Horsman, J.; Furlong, W.; Feeny, D.; Torrance, G. The Health Utilities Index (HUI®): Concepts, measurement properties and applications. Health Qual. Life Outcomes 2003, 1, 54. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Craig, B.M.; Reeve, B.B.; Brown, P.M.; Cella, D.; Hays, R.D.; Lipscomb, J.; Pickard, A.S.; Revicki, D.A. US valuation of health outcomes measured using the PROMIS-29. Value Health 2014, 17, 846–853. [Google Scholar] [CrossRef] [Green Version]
  24. Keetharuth, A.D.; Rowen, D.; Bjorner, J.B.; Brazier, J. Estimating a preference-based index for mental health from the recovering quality of life measure: Valuation of recovering quality of life utility index. Value Health 2020. [Google Scholar] [CrossRef]
  25. Mulhern, B.J.; Bansback, N.; Norman, R.; Brazier, J. Valuing the SF-6Dv2 classification system in the united kingdom using a discrete-choice experiment with duration. Med. Care 2020, 58, 566–573. [Google Scholar] [CrossRef]
  26. Bédard, S.K.; Poder, T.G.; Larivière, C. Processus de validation du questionnaire IPC65: Un outil de mesure de l’interdisciplinarité en pratique clinique. St. Publique 2013, 25, 763. [Google Scholar] [CrossRef]
  27. Slocum-Gori, S.L.; Zumbo, B.D. Assessing the Unidimensionality of psychological scales: Using Multiple criteria from factor analysis. Soc. Indic. Res. 2011, 102, 443–461. [Google Scholar] [CrossRef]
  28. Messick, S. Validity of test interpretation and use. ETS Res. Rep. Ser. 1990, 1990, 1487–1495. [Google Scholar] [CrossRef]
  29. Teresi, J.A.; Fleishman, J.A. Differential item functioning and health assessment. Qual. Life Res. 2007, 16, 33–42. [Google Scholar] [CrossRef]
  30. Berchtold, A. Test–retest: Agreement or reliability? Methodol. Innov. 2016, 9. [Google Scholar] [CrossRef] [Green Version]
  31. Paterson, C.; Langan, C.E.; McKaig, G.A.; Anderson, P.M.; Maclaine, G.D.H.; Rose, L.B.; Walker, S.J.; Campbell, M.J. Assessing patient outcomes in acute exacerbations of chronic bronchitis: The Measure Your Medical Outcome Profile (MYMOP), Medical Outcomes Study 6-Item General Health Survey (MOS-6A) and EuroQol (EQ-5D). Qual. Life Res. 2000, 9, 521–527. [Google Scholar] [CrossRef]
  32. Fauteux, V.; Poder, T. État des lieux sur les méthodes d’élicitation du QALY. INT J. Health Pref. Res. 2017, 1, 2–14. [Google Scholar]
  33. Neumann, P.J.; Goldie, S.J.; Weinstein, M.C. Preference-based measures in economic evaluation in health care. Annu. Rev. Public Health 2000, 21, 587–611. [Google Scholar] [CrossRef]
  34. McDonough, C.M.; Tosteson, A.N.A. Measuring preferences for cost-utility analysis: How choice of method may influence decision-making. Pharmacoeconomics 2011, 20, 93–106. [Google Scholar] [CrossRef] [PubMed]
  35. Kerr, C.; Breheny, K.; Lloyd, A.; Brazier, J.; Bailey, D.B.; Berry-Kravis, E.; Cohen, J.; Petrillo, J. Developing a utility index for the Aberrant Behavior Checklist (ABC-C) for fragile X syndrome. Qual. Life Res. 2015, 24, 305–314. [Google Scholar] [CrossRef] [Green Version]
  36. Hoefman, R.J.; van Exel, J.; Rose, J.M.; van de Wetering, E.J.; Brouwer, W.B.F. A discrete choice experiment to obtain a tariff for valuing informal care situations measured with the carerqol instrument. Med. Decis. Mak. 2014, 34, 84–96. [Google Scholar] [CrossRef]
  37. Rodríguez-Míguez, E.; Abellán-Perpiñán, J.M.; Alvarez, X.C.; González, X.M.; Sampayo, A.R. The DEP-6D, a new preference-based measure to assess health states of dependency. Soc. Sci. Med. 2016, 153, 210–219. [Google Scholar] [CrossRef]
  38. Brett Hauber, A.; Mohamed, A.F.; Reed Johnson, F.; Oyelowo, O.; Curtis, B.H.; Coon, C. Estimating importance weights for the iwqol-lite using conjoint analysis. Qual. Life Res. 2010, 19, 701–709. [Google Scholar] [CrossRef]
  39. Coast, J.; Flynn, T.N.; Natarajan, L.; Sproston, K.; Lewis, J.; Louviere, J.J.; Peters, T.J. Valuing the ICECAP capability index for older people. Soc. Sci. Med. 2008, 67, 874–882. [Google Scholar] [CrossRef] [Green Version]
  40. Ryan, M.; Netten, A.; Skåtun, D.; Smith, P. Using discrete choice experiments to estimate a preference-based measure of outcome—An application to social care for older people. J. Health Econ. 2006, 25, 927–944. [Google Scholar] [CrossRef]
  41. Nguyen, K.-H.; Mulhern, B.; Kularatna, S.; Byrnes, J.; Moyle, W.; Comans, T. Developing a dementia-specific health state classification system for a new preference-based instrument AD-5D. Health Qual. Life Outcomes 2017, 15, 21. [Google Scholar] [CrossRef] [Green Version]
  42. Beusterien, K.; Leigh, N.; Jackson, C.; Miller, R.; Mayo, K.; Revicki, D. Integrating preferences into health status assessment for amyotrophic lateral sclerosis: The ALS Utility Index. Amyotroph. Lateral Scler. Other Mot. Neuron Disord 2005, 6, 169–176. [Google Scholar] [CrossRef]
  43. Bahrampour, M.; Norman, R.; Byrnes, J.; Downes, M.; Scuffham, P.A. Developing a cerebral palsy-specific preference-based measure for a six-dimensional classification system (CP-6D): Protocol for a valuation study. BMJ Open 2019, 9, e029325. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Versteegh, M.M.; Leunis, A.; Uyl-de Groot, C.A.; Stolk, E.A. Condition-specific preference-based measures: Benefit or burden? Value Health 2012, 15, 504–513. [Google Scholar] [CrossRef] [Green Version]
  45. Kuspinar, A.; Finch, L.; Pickard, S.; Mayo, N.E. Using existing data to identify candidate items for a health state classification system in multiple sclerosis. Qual. Life Res. 2014, 23, 1445–1457. [Google Scholar] [CrossRef]
  46. Yang, Y.; Brazier, J.E.; Tsuchiya, A.; Young, T.A. Estimating a Preference-based index for a 5-dimensional health state classification for asthma derived from the asthma quality of life questionnaire. Med. Decis. Mak. 2011, 31, 281–291. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. McKenna, S.P.; Ratcliffe, J.; Meads, D.M.; Brazier, J.E. Development and validation of a preference based measure derived from the Cambridge Pulmonary Hypertension Outcome Review (CAMPHOR) for use in cost utility analyses. Health Qual. Life Outcomes 2008, 6, 65. [Google Scholar] [CrossRef] [Green Version]
  48. Cho, S.; Kim, H.; Kim, S.-H.; Ock, M.; Oh, Y.-M.; Jo, M.-W. Utility estimation of hypothetical chronic obstructive pulmonary disease health states by the general population and health professionals. Health Qual. Life Outcomes 2015, 13, 1–7. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Revicki, D.A.; Leidy, N.K.; Brennan-Diemer, F.; Thompson, C.; Toglas, A.; Togias, A. Development and preliminary validation of the multiattribute Rhinitis Symptom Utility Index. Qual. Life Res. 1998, 7, 693–702. [Google Scholar] [CrossRef] [PubMed]
  50. Rowen, D.; Brazier, J.; Young, T.; Gaugris, S.; Craig, B.M.; King, M.T.; Velikova, G. Deriving a preference-based measure for cancer using the EORTC QLQ-C30. Value Health 2011, 14, 721–731. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. King, M.T.; Viney, R.; Simon Pickard, A.; Rowen, D.; Aaronson, N.K.; Brazier, J.E.; Cella, D.; Costa, D.S.J.; Fayers, P.M.; Kemmler, G.; et al. Australian utility weights for the EORTC QLU-C10D, a multi-attribute utility instrument derived from the cancer-specific quality of life questionnaire, EORTC QLQ-C30. Pharmacoeconomics 2018, 36, 225–238. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Mulhern, B.; Labeit, A.; Rowen, D.; Knowles, E.; Meadows, K.; Elliott, J.; Brazier, J. Developing preference-based measures for diabetes: DHP-3D and DHP-5D. Diabet. Med. 2017, 34, 1264–1275. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Sundaram, M.; Smith, M.J.; Revicki, D.A.; Miller, L.-A.; Madhavan, S.; Hobbs, G. Estimation of a valuation function for a diabetes mellitus-specific preference-based measure of health: The Diabetes Utility Index®. Pharmacoeconomics 2010, 28, 201–216. [Google Scholar] [CrossRef]
  54. Stolk, E.A.; Busschbach, J.J.V. Validity and feasibility of the use of condition-specific outcome measures in economic evaluation. Qual. Life Res. 2003, 12, 363–371. [Google Scholar] [CrossRef]
  55. Gärtner, F.R.; de Bekker-Grob, E.W.; Stiggelbout, A.M.; Rijnders, M.E.; Freeman, L.M.; Middeldorp, J.M.; Bloemenkamp, K.W.M.; de Miranda, E.; van den Akker-van Marle, M.E. Calculating preference weights for the labor and delivery index: A discrete choice experiment on women’s birth experiences. Value Health 2015, 18, 856–864. [Google Scholar] [CrossRef] [Green Version]
  56. Ratcliffe, J.; Brazier, J.; Tsuchiya, A.; Symonds, T.; Brown, M. Using DCE and ranking data to estimate cardinal values for health states for deriving a preference-based single index from the sexual quality of life questionnaire. Health Econ. 2009, 18, 1261–1276. [Google Scholar] [CrossRef] [PubMed]
  57. Brazier, J.; Czoski-Murray, C.; Roberts, J.; Brown, M.; Symonds, T.; Kelleher, C. Estimation of a preference-based index from a condition-specific measure: The king’s health questionnaire. Med. Decis. Mak. 2008, 28, 113–126. [Google Scholar] [CrossRef] [PubMed]
  58. Yang, Y.; Brazier, J.; Tsuchiya, A.; Coyne, K. Estimating a preference-based single index from the overactive bladder questionnaire. Value Health 2009, 12, 159–166. [Google Scholar] [CrossRef] [Green Version]
  59. Young, T.A.; Rowen, D.; Norquist, J.; Brazier, J.E. Developing preference-based health measures: Using rasch analysis to generate health state values. Qual. Life Res. 2010, 19, 907–917. [Google Scholar] [CrossRef]
  60. Brazier, J.E.; Roberts, J.; Platts, M.; Zoellner, Y.F. Estimating a preference-based index for a menopause specific health quality of life questionnaire. Health Qual. Life Outcomes 2005, 3, 13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Gu, N.Y.; Botteman, M.F.; Gerber, R.A.; Ji, X.; Postema, R.; Wan, Y.; Sianos, G.; Anthony, I.; Cappelleri, J.C.; Szczypa, P.; et al. Eliciting health state utilities for dupuytren’s contracture using a discrete choice experiment. Acta. Orthop. 2013, 84, 571–578. [Google Scholar] [CrossRef] [Green Version]
  62. Burr, J.M.; Kilonzo, M.; Vale, L.; Ryan, M. Developing a preference-based glaucoma utility index using a discrete choice experiment. Optom. Vis. Sci. 2007, 84, 13. [Google Scholar] [CrossRef] [Green Version]
  63. Rentz, A.M.; Kowalski, J.W.; Walt, J.G.; Hays, R.D.; Brazier, J.E.; Yu, R.; Lee, P.; Bressler, N.; Revicki, D.A. Development of a preference-based index from the national eye institute visual function questionnaire–25. JAMA Ophthalmol. 2014, 132, 310. [Google Scholar] [CrossRef] [Green Version]
  64. Lloyd, A.; Kerr, C.; Breheny, K.; Brazier, J.; Ortiz, A.; Borg, E. Economic evaluation in Short Bowel Syndrome (SBS): An algorithm to estimate utility scores for a patient-reported SBS-Specific Quality of Life Scale (SBS-QoLTM). Qual. Life Res. 2014, 23, 449–458. [Google Scholar] [CrossRef]
  65. Kok, E.T.; McDonnell, J.; Stolk, E.A.; Stoevelaar, H.J.; Busschbach, J.J.V. The valuation of the International Prostate Symptom Score (IPSS) for use in economic evaluations. Eur. Urol. 2002, 42, 491–497. [Google Scholar] [CrossRef]
  66. Saltelli, A. Sensitivity analysis for importance assessment. Risk Anal. 2002, 22, 579–590. [Google Scholar] [CrossRef]
  67. Wisløff, T.; Hagen, G.; Hamidi, V.; Movik, E.; Klemp, M.; Olsen, J.A. Estimating QALY gains in applied studies: A review of cost-utility analyses published in 2010. Pharmacoeconomics 2014, 32, 367–375. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Feeny, D.; Furlong, W.; Torrance, G.W. Commentary. in praise of studies that use more than one generic preference-based measure. Int. J. Technol. Assess. Health Care 2019, 35, 257–262. [Google Scholar] [CrossRef] [PubMed]
  69. Garau, M.; Shah, K.K.; Mason, A.R.; Wang, Q.; Towse, A.; Drummond, M.F. Using QALYs in cancer: A review of the methodological limitations. Pharmacoeconomics 2011, 29, 673–685. [Google Scholar] [CrossRef] [PubMed]
  70. Gold, M.R.; Siegel, J.E.; Russell, L.B.; Russell, P.; Weinstein, M.C. Cost-Effectiveness in Health and Medicine; Oxford University Press: New York, NY, USA, 1996; ISBN 978-0-19-510824-8. [Google Scholar]
  71. Earnshaw, J.; Lewis, G. NICE guide to the methods of technology appraisal. Pharmacoeconomics 2008, 26, 725–727. [Google Scholar] [CrossRef]
  72. Insinga, R.P.; Fryback, D.G. Understanding differences between self-ratings and population ratings for health in the EuroQOL. Qual. Life Res. 2003, 12, 611–619. [Google Scholar] [CrossRef]
  73. Versteegh, M.M.; Brouwer, W.B.F. Patient and general public preferences for health states: A call to reconsider current guidelines. Soc. Sci. Med. 2016, 165, 66–74. [Google Scholar] [CrossRef] [PubMed]
  74. Reed Johnson, F.; Lancsar, E.; Marshall, D.; Kilambi, V.; Mühlbacher, A.; Regier, D.A.; Bresnahan, B.W.; Kanninen, B.; Bridges, J.F.P. Constructing experimental designs for discrete-choice experiments: Report of the ISPOR conjoint analysis experimental design good research practices task force. Value Health 2013, 16, 3–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Ijerph 18 04428 g001 550
Figure 1. PRISMA flowchart, 18 June 2020.
Figure 1. PRISMA flowchart, 18 June 2020.
Ijerph 18 04428 g001
Table
Table 1. Dimensions and levels selected in the generic tools.
Table 1. Dimensions and levels selected in the generic tools.
15DAQoL-7DAQoL-8DCAT-5D-QOLCORE-6DEQ-5D-5LHUI2HUI3PROMIS-29QWB-SAReQoL-UISF-6Dv2
Physical Domain
VisionXXX XX X
Hearing/ListeningXXX XX X
Speech/CommunicationXXX XX X
BreathingX X
Eating/NutritionXXX X
ExcretionX X
SleepX X XX
Physical discomfort/PainXXXXXXXXXXXX
Usual/Daily ActivitiesXXXXXX XX X
Self-care XX XX XXX
Mobility/AmbulationXXXX XXXXXXX
Dexterity/Handling X X X
Fertility X
Mental Domain
Autonomy/Control/Dependence XX X X
Adaptation/Coping XX X
Feeling of burden to other(s) X
Vitality/EnergyXXX XX X
Mental/cognitive functionXX XX X
Anxiety/DistressXXX XX XX X
Sadness/DepressionXXX XX XX
Calm/Agitation/Irritability XX X X
Anger X X X
Well-being/Happiness/Satisfaction XX XXX X
Self-confidence/esteem X X X
Loneliness X XX
Enthusiasm/Pleasure X X XX
Terror/Panic/Fear X X
Humiliation/Shame X
Suicidal idea X X
Social Domain
Personal/Close relationship XX XX X
Social inclusion/Connectedness XX X
Other Domain
Appearance (deformity, weight, skin) X
Sexual activityX X
Number of dimensions (items)157 (26)8 (35)5 (25)65788 (29)5 (77)2 (7)6
Number of levels by dimensions54, 5, 6, 74, 5, 64353, 4, 55, 65, 112, 4, 555, 6
Table
Table 2. Methods used for the development of generic instruments.
Table 2. Methods used for the development of generic instruments.
InstrumentsMethod of Choice for Dimensions and LevelsValidation MethodElicitation MethodModel UsedReferences
15 dimensions (15D)Factor analyses; patient surveys; instrument user feedback.Multimethod multivariate matrices based on empirical measurements of the dimensions of 15D, NHP, SF-20 and EQ-5D.VASAdditive model[20]
Assessment of Quality of Life (AQoL)-7DLiterature review and focus group; factor analysis; structural equation modelling; logical considerations.Not foundTTOMultiplicative regression model[16]
Assessment of Quality of Life-8 (AQoL-8D)Iterative process of entering and removing potential items in the AQoL model until all possible combinations are analyzed.Loevinger H (homogeneity)TTOMultivariate linear regression[10]
Computerized adaptative testing quality of life 5 dimensions (CAT-5D-QOL)IRTNot foundSGMultiplicative regression model[21]
Clinical Outcomes in Routine Evaluation 6 dimensions (CORE-6D)Rasch analysisNot foundTTOAdditive model[2]
EuroQol 5 dimensions (EQ-5D-5L)Literature reviewPatients were asked to assess the interpretability and plausibility of the instrument.VASNon applicable[12]
Health Utilities Index 2 and 3 (HUI2-HUI3)General population survey: the importance the public places on each attribute was considered.Not foundVAS; SGMultiattribute multiplicative model[22]
Patient-Reported Outcomes Measurement Information System—29 (PROMIS-29 v2.0)Item response theory; Factor (exploratory factor and confirmatory) analysesComparison with other instruments.DCERelativity model[23]
Quality of Well Being Self-Administered (QWB-SA)Inputs from the QWB.Test–retest; test the impact of the administration mode on total scores.VASAdditive model[15]
Recovering Quality of Life utility index (ReQoL-UI).Literature review, interviews, factor analyses and IRTNot foundTTORandom effects models[24]
Short-Form 6-dimension (SF-6Dv2)Exploratory and confirmatory factor analyses; Rasch analysis; literature review; expert opinion.DIF on sub-samples.DCEttoConditional Logit[7,25]
Note: VAS = Visual analogue scale; TTO = Time trade-off; SG = Standard gamble; DCE = Discrete choice experiment; DCEtto = Discrete choice experiment with duration; IRT = Item Response Theory; NHP = Nottingham Health Profile; DIF = Differential item functioning.
Table
Table 3. Dimensions and levels selected in specific instruments.
Table 3. Dimensions and levels selected in specific instruments.
InstrumentsNumber of Dimensions/ItemsNature of DimensionsNumber of Levels Per Dimension/Item
Social care and dependency
Aberrant Behaviour Checklist Utility Index (ABC-UI)7Mood; Distractible; Aggressive; Impulsive; Speech; Social; Movements.3
Adult Social Care Outcomes Toolkit (ASCOT)8Personal cleanliness and comfort; Accommodation cleanliness and comfort; Food and drink; Safety; Social participation and involvement; Occupation; Control over daily life; Dignity.4
Carer Quality of Life—7 Dimensions (CarerQol-7D)7Fulfilment; Relational problems; Mental health problems; Problems with combining daily activities; Financial problems; Social support; Physical health problems of caregiving3
Dependency 6 dimensions (DEP-6D)6Eat; Incontinence; Personal care; Mobility; Housework and Cognition/mental problems.3, 4
Impact of Weight on Quality of Life—Lite (IWQOL-Lite)8Problems doing usual daily activities; Physical symptoms; Worrying about health; Low self-esteem; Sexual problems; Problems moving around or sitting in public places; Teasing or discrimination by others; Problems doing your job or getting recognition at work.3
Index of capability for older people (ICECAP-O)5Attachment; Security; Role; Enjoyment and control.4
Older Persons Utility Scale (OPUS)5Food and nutrition; Personal care; Safety; Social participation and involvement; Control over daily living.3
Neurological disorders
Alzheimer’s disease (AD-5D)5Interpersonal environment; Physical; Self-functioning; Memory; Mood.4
Amyotrophic Lateral Sclerosis Utility Index (ALSUI)4Speech and swallowing; Eating; Dressing and bathing; Leg function and Respiratory function.5, 6
Cerebral palsy-specific 6 dimensions (CP-6D)6Social well-being and acceptance; Physical health; Communication; Pain and discomfort; Manual ability; Sleep.5
Epilepsy-specific preference-based measure (NEWQOL-6D)6Worry about attacks; Depression; Memory; Concentration; Stigma; control.4
Multiple Sclerosis Impact Scale 29 (MSIS-29)8Problems with your balance; Being clumsy; Limitations in your social and leisure activities at home; Difficulties using your hands in everyday tasks; Having to cut down the amount of time you spent on work or other daily activities; Feeling mentally fatigued; Feeling irritable; impatient or short tempered; Problems concentrating.4
Prototype Preference-Based MS Index (P-PBMSI)5Walking; Fatigue; Cognition; Mood; Work.3
Respiratory problems
Asthma Quality of Life (AQL-5D)5Concern; Short of breath; Weather and pollution; Sleep; Activities.5
Cambridge Pulmonary Hypertension Outcome Review (CAMPHOR)4Social activities; Travelling; Dependence and Communication.2, 3
Chronic obstructive pulmonary disease (COPD)3COPD; Non-serious exacerbations; Serious exacerbations.3
Rhinitis Symptom Utility Index (RSUI)5Stuffy/blocked nose; Runny nose; Sneezing; Itchy/watery eyes and Itching nose/throat.10
Cancer
European Organization for Research and Treatment of Cancer (EORTC-8D)8Physical functioning; Role functioning; Social functioning; Emotional functioning; Pain; Fatigue and Sleep disturbance; Nausea; Constipation and diarrhoea.4, 5
Quality of Life Questionnaire for Cancer 30 (QLQ-C30)8Trouble taking a long walk; Limited in doing either your work or other daily activities; Have you had pain; Have you felt nauseated; Were you tired; Difficulty in concentrating on things; Did you worry; Has your physical condition or medical treatment interfered with your social activities.4, 7
Quality of Life Utility Measure—Core 10 Dimensions (QLU-C10D)10Physical functioning; Role functioning; Social functioning; Emotional functioning; Pain; Fatigue; Sleep; Appetite; Nausea; Bowel problems.4
Diabetes
Diabetes Health Profile 3 (DHP-3D)3Mood; Social limitations; Eating.4
Diabetes Health Profile 5 (DHP-5D)5Mood; Social limitations; Eating; Hypoglycaemic attacks; Vitality.4,5
Diabetes Utility Index (DUI)5Physical ability and energy; Relationships; Mood and feelings; Enjoyment of diet and Satisfaction with management of diabetes.3, 4
Sexuality/fertility
International Index of Erectile Function (IIEF)2Ability to Attain and maintain an erection sufficient for satisfactory sexual performance.5
Labour and Delivery Index (LADY-X)7Availability of competent professionals; The information provided; Professionals’ responses to needs; Professionals’ emotional support; Feelings of safety; Concerns about the child’s condition; Duration until first contact with child.3
Sexual quality of life questionnaire (SQOL-3D)3Sexual performance; Sexual relationship and Sexual anxiety.4
Bladder
King’s Health Questionnaire (KHQ)5Role limitation; Physical limitations; Social limitations/family life; Emotions; and Sleep/energy.4
Overactive Bladder 5 dimensions (OAB-5D)5Urge; Urine loss; Sleep; Coping; Concern.5
Menopause/flushing
Flushing Symptoms Questionnaire (FSQ)5Redness of skin; Warmth; Tingling; Itching; Sleep difficulty4, 5
Menopause specific health quality of life questionnaire7Hot flushes; Aching joints/muscles; Anxious/frightened feelings; Breast tenderness; Bleeding; Vaginal dryness and Undesirable androgenic signs.3, 5
Musculoskeletal disorders
Dupuytren’s contracture (DC)8Joint #1: index finger, PIP joint; Joint #2: index finger, MCP joint; Joint #3: middle finger, PIP joint; Joint #4: middle finger, MCP joint; Joint #5: ring finger, PIP joint; Joint #6: ring finger, MCP joint; Joint #7: little finger, PIP joint; Joint #8: little finger, MCP joint.3
Health Assessment Questionnaire for arthritis (HAQ)5Stand up from a straight chair; Walk outdoors on flat ground; Get on / off toilet; Reach and get down a 5-pound object (such as a bag of sugar) from just above your head; Open car doors.4
Vision/glaucoma
Glaucoma Utility Index (GUI)6Central and near vision; Lighting and glare; Mobility; Activities of daily living; Eye discomfort; Other effects of glaucoma and its’ treatment4
Visual Function Questionnaire–Utility Index (VFQ-UI)6Near vision activities; Distance vision activities; Vision-specific social functioning; Role difficulties; Dependency; and Mental health.5
Digestive function
Short Bowel Syndrome-specific quality of life scale (SBS-QoL)6Diet; Eating and drinking habits; Diarrhoea; Fatigue/weakness; Mobility and self-care/everyday activities; Leisure activities/social life; Emotional life.2
Prostate
International prostate symptom score (IPSS)2Obstructive symptoms; Irritative symptoms.3
Table
Table 5. Analysis of the quality of studies using the COSMIN grid.
Table 5. Analysis of the quality of studies using the COSMIN grid.
AuthorsVery GoodAdequateDoubtful/UndeterminedInadequate
[2]57.89%-36.84%5.26%
[7]57.89%-42.11%-
[8]42.11%-52.63%5.26%
[9]84.21%-10.53%5.26%
[10]57.89%-31.58%10.53%
[12]42.11%-47.37%10.53%
[15]47.37%-42.11%10.53%
[16]57.89%-36.84%5.26%
[17]52.63%-42.11%5.26%
[20]57.89%-36.84%5.26%
[21]57.89%-26.32%15.79%
[22]47.37%-47.37%5.26%
[23]78.95%-21.05%-
[24]57.89%-36.84%5.26%
[25]42.11%-31.58%-
[35]63.16%-36.84%-
[36]52.63%-47.37%-
[37]63.16%-26.32%10.53%
[38]63.16%-36.84%-
[39]57.89%-42.11%-
[40]57.89%-42.11%-
[41]47.37%-42.11%5.26%
[42]57.89%-36.84%5.26%
[43]36.84%5.26%52.63%5.26%
[44]57.89%-26.32%15.79%
[45]94.74%-5.26%-
[46]57.89%-36.84%5.26%
[47]63.16%-31.58%5.26%
[48]52.63%-42.11%5.26%
[49]36.84%-42.11%21.05%
[50]57.89%-31.58%10.53%
[51]73.68%-26.32%-
[52]57.89%-36.84%5.26%
[53]63.16%-31.58%5.26%
[54]52.63%-36.84%10.53%
[55]63.16%-36.84%-
[56]47.37%-47.37%5.26%
[57]63.16%-31.58%5.26%
[58]52.63%-42.11%5.26%
[59]57.89%-36.84%5.26%
[60]57.89%-36.84%5.26%
[61]42.11%-57.89%-
[62]57.89%-42.11%-
[63]52.63%-36.84%10.53%
[64]42.11%-47.37%10.53%
[65]36.84%-52.63%10.53%
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Touré, M.; Kouakou, C.R.C.; Poder, T.G. Dimensions Used in Instruments for QALY Calculation: A Systematic Review. Int. J. Environ. Res. Public Health 2021, 18, 4428. https://doi.org/10.3390/ijerph18094428

AMA Style

Touré M, Kouakou CRC, Poder TG. Dimensions Used in Instruments for QALY Calculation: A Systematic Review. International Journal of Environmental Research and Public Health. 2021; 18(9):4428. https://doi.org/10.3390/ijerph18094428

Chicago/Turabian Style

Touré, Moustapha, Christian R. C. Kouakou, and Thomas G. Poder. 2021. "Dimensions Used in Instruments for QALY Calculation: A Systematic Review" International Journal of Environmental Research and Public Health 18, no. 9: 4428. https://doi.org/10.3390/ijerph18094428

APA Style

Touré, M., Kouakou, C. R. C., & Poder, T. G. (2021). Dimensions Used in Instruments for QALY Calculation: A Systematic Review. International Journal of Environmental Research and Public Health, 18(9), 4428. https://doi.org/10.3390/ijerph18094428

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop