i sciendo Zdr Varst. 2020;59(1):8-17 10.2478/sjph-2020-0002 Prevolnik Rupel V, Srakar A, Rand K. Valuation of EQ-5D-3L health states in Slovenia: VAS based and TTO based value sets. Zdr Varst. 2020;59(1):8-17. doi: 10.2478/sjph-2020-0002. VALUATION OF EQ-5D-3L HEALTH STATES IN SLOVENIA: VAS BASED AND TTO BASED VALUE SETS VREDNOTENJE ZDRAVSTVENIH STANJ EQ-5D-3L V SLOVENIJI: VREDNOSTI ZDRAVSTVENIH STANJ, PRIDOBLJENE Z METODAMA VAS IN TTO Valentina PREVOLNIK RUPEL1*, Andrej SRAKAR1, Kim RAND2 'Institute for Economic Research, Kardeljeva ploščad 17, 1000 Ljubljana, Slovenia 2Health Services Research Unit, Akershus University Hospital, L0renskog, Norway Received: May 21, 2019 Original scientific article Accepted: Sep 26, 2019 ABSTRACT Keywords: EQ-5D-3L, Slovenia, quality-adjusted life-years, social value set, utility Introduction: The two primary objectives of this paper were (a) to develop first logically consistent TTO based EQ-5D-3L value sets for Slovenia and (b) to revisit earlier developed VAS based EQ-5D-3L value sets. Methods: Between September 2005 and April 2006, face-to-face interviews with 225 individuals in Slovenia were conducted. Protocols from the Measurement and Value of Health study were followed closely. Each respondent valued 15 health states out of a total of 23. Model selection was informed by the criteria monotonicity/logical consistency. Predictive accuracy was assessed in terms of mean square difference between out-of-sample predictions and corresponding observed means, as well as Lin's Concordance Correlation Coefficient. Results: Modelling was based on 2,717 VAS and 2,831 TTO values elicited from 225 respondents. A 6-parameter constrained regression model with a supplementary power term was selected for VAS and TTO value sets, as it produces monotonic values, and proved superior in terms of out-of-sample predictive accuracy over the tested alternatives. Conclusion: This is the first EQ-5D-3L TTO based value set in Slovenia and the second in Central and Eastern Europe (besides Poland). It is also the first monotonic and logically consistent VAS value set in Central and Eastern Europe. Comparisons with Polish and UK TTO values show considerable differences, mostly due to mobility with having a substantially greater weight in Slovenia. The UK value set generally produces lower values and the Polish value set higher values for mild states. IZVLEČEK Ključne besede: EQ-5D-3L, Slovenija, kakovostno prilagojena leta življenja, vrednostni set VAS, koristnost Uvod: Dva osnovna cilja raziskave sta (a) prikazati prvi logično konsistentni vrednostni set EQ-5D-3L za Slovenijo, ki temelji na metodi časovne izmenjave, (b) izboljšati prejšnji vrednostni set EQ-5D-3L za Slovenijo, ki temelji na vrednostni lestvici (VAS-metodi). Metode: Od septembra 2005 do aprila 2006 je bilo opravljenih 225 osebnih intervjujev s posamezniki iz 40 slovenskih občin. Študija je natančno sledila protokolu študije MVH o merjenju in vrednotenju zdravja, ki je bila izvedena v Združenem kraljestvu. Vsak anketiranec je ocenil 15 od skupno 23 zdravstvenih stanj. Izbira modela za izračun vrednosti zdravstvenih stanj je temeljila na dveh osnovnih merilih: monotonosti in logični doslednosti vrednosti. Napovedno moč smo vrednotili s povprečno kvadrirano razliko med napovedmi izven vzorca in pripadajočimi ocenjenimi povprečji ter s pomočjo Linovega konkordančnega korelacijskega koeficienta. Rezultati: Izbrana modela temeljita na vrednostih zdravstvenih stanj 2,717 VAS in 2,831 TTO, ki smo jih pridobili v225 osebnih intervjujih. Za oceno vrednosti VAS in TTO smo izbrali šestparametrski regresijski model z omejitvami in dodanim potenčnim faktorjem, saj se je izkazalo, da so ocenjene vrednosti na temelju tega modela monotone in imajo boljšo napovedno moč ocen izven vzorca kot vsi drugi ocenjevani modeli. Zaključek: V študiji smo prikazali prvi slovenski vrednostni set EQ-5D, ki temelji na metodi TTO, hkrati pa je to drugi set, izračunan v srednji in vzhodni Evropi (poleg Poljske). Gre tudi za prvi monotoni in logično dosledni vrednostni VAS-set tako v Sloveniji kot srednji in vzhodni Evropi. Primerjave z vrednostmi poljskega in britanskega TTO kažejo precejšnje razlike med vrednostmi posameznih zdravstvenih stanj, predvsem zaradi dimenzije pokretnosti, ki ima bistveno večjo težo v Sloveniji. Vrednosti TTO v Združenem Kraljestvu so na splošno nižje za manj težavna zdravstvena stanja, poljske vrednosti zdravstvenih stanj pa so na splošne višje. Corresponding author: Tel. + 386 1 478 6870; E-mail: katkarupel@yahoo.com; katka.rupel@gmail.com NIJZ National Institute © National Institute of Public Health, Slovenia. 8 of Public Health This work is licensed under the Creative 23.03.20 11:29 UTC 8.2478/sjph-2020-0002 Zdr Varst. 2020;59(1):8-17 1 INTRODUCTION Slovenia passed the regulation that required economic evaluation to inform drug and health technology reimbursement decision-making in the 1990s. Health technologies are assessed by various bodies (1, 2). The latest evaluation guidelines by the Health Insurance Institute of Slovenia recommend that the benefits of the treatment are expressed as quality-adjusted life years (QALYs). QALY is a measure that encapsulates a treatment's impact on a patient's life length and also on their health-related quality of life (HRQOL), which is recognized as a key indicator of treatment outcomes (3). The QALY requires data that expresses health-related quality of life (HRQOL) in the form of a single value, sometimes known as a health state utility value, which is scored on a scale that assigns a value of 1 to a state equivalent to full health and 0 to a state equivalent to death (4). Weinstein and Stason (1977) connected QALYs with utilities, specifically expected utility, rather than the "weights" of the earlier literature; and this connection has remained although, not everybody agrees with the concession of the term "quality" to refer only to expected utility-based measures (5). Anyhow, in health economics, utilities (values) are typically combined with survival estimates and aggregated across individuals to generate quality-adjusted life years (QALYs) for use in cost-utility analyses of healthcare interventions (6). There are many methods available regarding how the health states can be valued and grouped into two broad categories of measures: direct and indirect methods of measurement. The direct valuation methods include standard gamble (SG), time trade-off (TTO), DCE (discrete choice experiment), rating scales, equivalence technique, ratio scaling and person trade-off. The SG approach is the classic method of measuring preferences in economics under conditions of uncertainty, and is based on von Neumann Morgenstern utility theory (7). The theoretical underpinnings of all other methods are less clear. TTO valuation methodology does not conform to utility-under-uncertainty requirements under expected utility theory, but is still a dominant method in the valuation sets across countries (8). Regarding VAS values, there are a lot of criticisms and opposing views on their suitability for use in cost utility analysis. Mostly, criticisms consist of VAS values not being choice based and their lack of theoretical foundation (5). Due to these issues, most health economists would recommend a choice-based value set, derived from TTO or DCE data, especially for economic studies where cost-utility analysis is anticipated. If a choice-based value set is not available for the country/region, a choice-based value set can be selected from a country/region that most closely approximates the country where the study is being conducted. Alternatively, a VAS-based value set can be used if that is available for the country/region (9). Due to these issues, most health economists would recommend a choice-based value set, derived from TTO or discrete choice experiment (DCE) data to be used in studies that estimate the value of health states of any population. If a choice-based value set is not available for the country/ region, a choice-based value set can be selected from a country/region that most closely approximates the country where the study is being conducted. Alternatively, a VAS-based value set can be used if that is available for the country/region (9). Utilities (values representing preferences) for healthcare priority setting are typically obtained indirectly by asking the general population (or patients) to fill in a questionnaire and attach value to hypothetical heath state, later on converting the results to a value set for all health states, using population (or patient) values. There are at least two advantages that contributed to the popularity of the indirect methods: the pool of health states is already defined and so are their values (value set). When a patient defines his own health state in subsequent studies, a value can thus be attached to his/ her health state from the value set. Some of the established questionnaires are the Health Utility Index, the Short Form 6D, 15D instrument, Assessment of Quality of Life (AQOL) and the EuroQol 5D (EQ-5D). The EQ-5D is a prominent example of preference-based measures developed by the EuroQol Group (9). It has been suggested that these are the most widely used preference-based measures in the world (10). To improve the instrument's sensitivity and to reduce ceiling effect, EuroQoL Group developed a new version in 2009, called EQ-5D-5L. The new version kept its original 5 dimensions, but expanded the response options from 3 to 5 levels. As there are a lot of existing 3L value sets in many countries, for comparison reasons a non-parametric model was set up to transform any EQ-5D-3L values into EQ-5D-5L values. In this way, 5L values can also be used in cases when 5L preferences directly elicited from representative general population samples are not yet available (11). The EQ-5D-3L descriptive system has been formally translated and validated into the Slovenian language in 1999/2000 (12). The two primary objectives of this paper were (a) to develop first logically consistent EQ-5D-3L TTO-based value sets for Slovenia and (b) to revisit earlier developed VAS-based EQ-5D-3L value sets for Slovenia (13). Some issues that went undetected with the previous VAS value set have been identified, and methodological advances seem to make it possible to improve on earlier modelling. Bereitgestellt von National & University Library Ljubljana | Heruntergeladen 23.03.20 1 1:29 UTC 3.2478/sjph-2020-0002 Zdr Varst. 2020;59(1):8-17 2 METHODS 2.1 Study Overview The study was a multicentre, population-based study, using face-to-face interviews. The sample was prepared by the Statistical Office of Slovenia using the Central Population Register. In the sample, 1,000 individuals aged 18+ from 40 Slovenian municipalities were included. At the first level, 40 municipalities were randomly selected and later on 25 individuals were selected from each municipality. Each person carried a name, last name, address, house number, postcode, municipality, age and gender. The investigators started the interviews in September 2005 and finished in April 2006. Participant recruitment was conducted primarily through landline telephone numbers for each participant in the sample. 225 participants agreed to participate in the survey. Interviews were conducted by three interviewers, who underwent one-day training on the health state valuations, the purpose of the interviews and TTO procedures. To facilitate the training, the interview book prepared by Gudex (14) was translated into Slovenian language and used for training of the interviewers and in the pilot training. Each investigator conducted 5 test interviews. 2.2 EQ-5D EQ-5D consists of a descriptive system and EQ visual analogue scale (EQ-VAS). The EQ-5D descriptive system consists of 5 dimensions: mobility (MO), self-care (SC), usual activities (UA), pain/discomfort (PD), and anxiety/ depression (AD). Each dimension has 3 levels: no problems, some problems, and extreme problems (9). The respondent is asked to indicate his or her health state by ticking the box that marks the most appropriate level of problems in each dimension. A unique health state can be described by using a 5-digit vector formed according to the responses to the 5 questions. For example, no problems in MO and SC, some problems in UA and PD and extreme problems in AD can be referred to as "11223." Health states defined in this way may be converted into a single summary index by applying a formula that attaches values to each of the levels in each dimension. A total of 243 possible health states can be defined. 2.3 Health State Selection In the valuation task, each investigator had 3 sets of health states, and investigators decided randomly which set to use with each respondent. The sets were named A, B and C. Each set contained 15 health states. Some health states were included in all three sets, but some were not. Health states in each set represent the complete scale of health states, from worst to best health states. Sets B and C were developed in 2000 (16). The number of all various directly valued health states in all three sets is 23 plus unconscious and dead. These states are 11211, 11111, 21111, 12111, 11112, 11121,11122, 11113, 11131, 11133, 11312, 13311, 32211, 22222, 21232, 22323, 22233, 32223, 32313, 23232, 33321, 33323, 33333, unconscious and dead. Health states can also be divided into mild, moderate and severe states (17) in such a way that all the categories were represented in all three sets. 2.4 Interview Process The questionnaire consists of four parts. In the first part, the respondent indicated his/her own health state on the day of the interview using an EQ-5D descriptive system. Furthermore, the respondent marks how good or bad his/her health state is on a visual analogue scale (VAS) from 0 to 100 (where 0 represents the worst health state imaginable and 100 represents the best health state imaginable). The second part of the questionnaire is a valuation of the 15 selected health states. Once the respondent had familiarised himself/herself with the health states by reading them, he/she ranked the selected states from worst to best. After ranking he/she attached the value from 0 to 100 to all 15 health states. The third part of the interview is the valuation of the same selected 15 health states using time trade off (TTO) method. The interviewers follow the adapted Measurement and Value of Health study (MVH) protocol (14). The MVH study was a large exercise, in which 3,395 respondents valued 13 different health states. Because of the limited budget, we included 23 health states altogether. Out of 23 health states, we made three different sets of 15 health states (sets A, B and C) as described in Chapter 2.3. The objective of the TTO is to determine the length of lifetime the respondent would be willing to forego to live in a better health state (typically 'full health') and to avoid living in a bad health state. This is achieved by presenting respondents with a series of choice tasks, each involving two alternative hypothetical lives. The two lives are presented so that the respondent is forced to choose between a longer life in the health state of interest and a shorter life in better health (15). The last part of the interview collects social demographic data: gender, age, education, work experience, smoking habits, experience with illness and postcode. 2.5 Preference Elicitation Techniques In the TTO procedure, the interviewers used a TTO board and a set of health state cards. A TTO board was made of three layers of thick cardboard and incorporated a sliding scale from 0 to 10 years. Both sides of the board were used, one for states better than dead and the other for health states worse than dead. The respondent was taken through each of 15 health states to be valued, Bereitgestellt von National & University Library Ljubljana | Heruntergeladen 23.03.20 1 1:29 UTC 10.2478/sjph-2020-0002 Zdr Varst. 2020;59(1):8-17 one at a time, with the interviewer moving the scale as appropriate. The respondent needed to make a series of choices between two hypothetical lives: one involving x years of healthy life, followed by death (Life A) and the other involving t years in a worse health state (where x3xAnif + e (3) This model, hereafter MULT6P, was included under the assumption that respondents may display diminishing sensitivity to health problems when combined, so that the perceived disutility of problems on two separate dimensions at the same time may be smaller than the sum of the disutility of each problem in isolation. Standard error estimation is non-trivial in regression models involving multiplication of two or more (presumably normally distributed) parameters. Consequently, standard errors for model parameters and modelled values were estimated for MULT6 and MULT6P using bootstrapping 11 Bereitgestellt von National & University Library Ljubljana | Heruntergeladen 23.03.20 1 1:29 UTC 8.2478/sjph-2020-0002 Zdr Varst. 2020;59(1):8-17 (22). Briefly, 10,000 bootstrap samples were drawn (with replacement) at the level of individual study participants, each subsample of the same size as the observed data. The regression models were fitted to each bootstrap sample, and standard errors were calculated by taking the standard deviation for the resulting coefficients and the predicted health state values. Given the limited number of valued health states, and the relatively small sample size used in this study, we were concerned that regular regression models might produce results that were highly susceptible to random error. We, therefore, tested the included model variants using penalised regression, including Lasso (20), Ridge regression (17-19), and Elastic net (21). Model selection was informed by two primary criteria, being monotonicity/logical consistency. Modelled state values should reflect the hierarchical structure of the EQ-5D descriptive system, so that further problems on any dimension should always result in worse (lower) values. Monotonic models were compared in terms of out-of-sample predictive accuracy for observed means. This was compared using leave-out-by-state cross-validation (18, 22). Predictive accuracy was assessed in terms of mean square difference between out-of-sample predictions and corresponding observed means, as well as Lin's Concordance Correlation Coefficient. The final Slovenian TTO model was compared visually to the Polish EQ-5D-3L value set (25) and the UK MVH value set (26), and the final VAS model was compared visually to the EU VAS value set (27). All statistical analyses were performed in the R statistical package, version 3.3.2, in the RStudio environment, using ggplot for graphical output (28-30). Regression models were run in the xreg package (31). 3 RESULTS In total, 225 respondents completed the interview, of which 126 (56%) were female. Distribution of the respondents according to social and demographic variables is shown in Table 1. The sample was well representative of the Slovenian population in terms of age, educational level and activity with students being slightly underrepresented and unemployed being slightly overrepresented. Regarding gender distribution, women were overrepresented in the sample. The majority of problems reported in the EQ-5D descriptive system were pain/discomfort, followed by problems with usual activities and mobility. A really small share of the sample had problems with self-care (9.3%). The mean health state recorded on the EQ-VAS was 72.15 (SD 20.2) and the mean estimated interview difficulty was 2.87 (1 is very easy and 5 is very difficult). Table 1. Study sample characteristics compared with the Slovenian general population data 2005. Group Mean pre-test Mean post-test scores (SD) scores (SD) Age 18-24 27 (12%) 190,239 (11.5%) 25-34 48 (21.3%) 300,793 (18.2%) 35-44 43 (19.1%) 304,490 (18.5%) 45-54 39 (17.3%) 310,757 (18.9%) 55-64 28 (12.5%) 229,580 (13.9%) 65+ 40 (17.8%) 312,874 (19%) Mean age (SD) 45.3 (17.4) n/a Gender Male 99 (44%) 981,465 (49%) Female 126 (56%) 1,021,893 (51%) Educational level Primary 53 (23.6%) 494 (28.8%) Secondary 147 (65.3%) 952 (55.5%) High 25 (11.1%) 267 (15.6%) Work Employed 111 (49.3%) 813,100 (47.3%) Retired 62 (27.5%) 529,622 (30.8%) Housewife 8 (3.6%) n/a Student 20 (8.9%) 112,228 (6.5%) Unemployed 18 (8%) 92,575 (5.4%) Other 6 (2.7%) n/a EQ-5D dimension n/a problems (%) Mobility 68 (30.2%) Self-care 21 (9.3%) Usual activities 69 (30.7%) Pain/discomfort 101 (44.9%) Anxiety/Depression 64 (28.4%) EQ VAS own health (SD) 72.15 (20.2) n/a Cross-validation fit statistics can be found in Table 2. The fitted parameters of ADD10 were not monotonic. MULT6 and MULT6P with no intercept were monotonic for both VAS and TTO, while the version with an intercept was monotonic for TTO only. MULT6 displayed poor fit, both in direct estimation and in cross-validation. Ridge regression improved out-of-sample predictive accuracy for ADD10 and MULT6, but not for MULT6P. By comparison, MULT6P had substantially improved fit, outperforming all other tested variants in terms of out of sample predictive accuracy, both for VAS and TTO data. MULT6P with an intercept did not improve predictions for TTO, and did not converge for VAS. MULT6P was selected for generating VAS and TTO value sets. Coefficients and bootstrap-based SE estimates for the two models can be found in Table 3. Bereitgestellt von National & University Library Ljubljana | Heruntergeladen 23.03.20 1 1:29 UTC 6.2478/sjph-2020-0002 Zdr Varst. 2020;59(1):8-17 Table 2. Cross-Validation fit statistics. TTO ADD10 ADD10i MULT6 MULT6i MULT6P MULT6iP Monotonicity - - ✓ ✓ ✓ ✓ R 0.920 0.941 0.934 0.930 0.966 0.966 CCC 0.894 0.938 0.893 0.929 0.966 0.966 MSE 0.046 0.022 0.048 0.024 0.012 0.012 MAE 0.181 0.114 0.182 0.126 0.087 0.087 VAS ADD10 ADD10i MULT6 MULT6i MULT6P MULT6iP Monotonicity - - ✓ ✓ ✓ - R 0.926 0.919 0.891 0.923 0.971 - CCC 0.879 0.897 0.886 0.883 0.941 - MSE 0.02 0.015 0.015 0.021 0.01 - MAE 0.123 0.094 0.096 0.102 0.082 - R - Pearson's correlation coefficient, CCC - concordance correlation coefficient, MSE - mean squared error, MAE - mean absolute error Table 3. Coefficients and bootstrap-based SE estimates. TTO VAS Coefficient SE Coefficient SE MO 0.943 0.126 0.424 0.070 SC 0.243 0.052 0.105 0.029 UA 0.202 0.039 0.103 0.028 PD 0.448 0.049 0.180 0.012 AD 0.239 0.037 0.137 0.021 L2 0.125 0.043 0.176 0.025 P 0.551 0.044 0.423 0.020 4 DISCUSSION The Slovenian VAS and TTO based value sets are presented in Annex 1 and 2. The first VAS value set for Slovenia was calculated back in year 2000, however, the values of the health state were not monotonic: some of the logically superior health states displayed lower values (12). In 2012, a new improved set was published (13), however, again due to some methodological issues discovered later, it cannot be recommended for Slovenia's priority setting. With the advanced methodology, for the first time in Slovenia it was possible to obtain a logically consistent and monotonic VAS based value set as well as a 3L TTO based value set, which is also the second 3L TTO based value set in Central and Eastern Europe. Figure 1 displays the TTO value set compared to observed mean values, along with TTO-based values from a UK MVH study and the Polish TTO-based EQ-5D-3L valuation study. Figure 2 presents the Slovenian VAS value set, observed mean values, and the EU VAS value set. Figure 1. Graphical comparison of Slovenian EQ-5D-3L TTO value set versus (a) UK TTO and (b) Polish TTO value sets. 13 Bereitgestellt von National & University Library Ljubljana | Heruntergeladen 23.03.20 1 1:29 UTC 10.2478/sjph-2020-0002 Zdr Varst. 2020;59(1):8-17 Observed means and predicted VAS values i "M J, L 1 1 ' Pf (v 14 M ífl HI 4 f[0j! ^ S ^ 1 1 1 1 i I ii i EQ-5D-3L health states ordered by mean predicted values — eu • own*«! — SMO Figure 2. Graphical comparison of Slovenian VAS value set versus EU VAS value set. For the TTO value set, there are two main drivers of differences between the national value sets: first, mobility has a substantially greater weight in the Slovenian value set. Second, the UK value set produces lower values for mild states, while the Polish value set produces higher values. The VAS value set is more in line with the EU VAS value set, but generally produces somewhat higher values. Due to considerable differences between TTO value sets in Slovenia in comparison to the UK and Poland, its use should be recommended for Slovenian studies. After testing various modelling approaches, the Slovenian TTO and VAS value sets were fitted using a 6-parameter constrained regression model with a supplementary power term, which produces monotonic values and was superior in terms of out-of-sample predictive accuracy over the tested alternatives. The Slovenian TTO-based value set, being a choice-based method, is recommended for use in all studies, including economic analysis. Systematic pairwise comparison across all conditions and value sets in previous studies (32) revealed the greatest differences between the TTO and VAS-based value sets, as well as the varying sensitivity of the disease burden evaluations of chronic disease conditions to the choice of value sets. Therefore, using a VAS value set in the presence of newly developed TTO value set in Slovenia would unnecessarily produce incomparable results. However, in order to allow for comparisons with previous studies where VAS values were used due to the absence of a TTO-based value set, it is suggested to present VAS-based results in parallel. Another option is also the presentation of the results in parallel with the UK TTO value set, given that it has been the most used value set in the region (33). Further analysis of the differences between the first two TTO based value sets in Central and Eastern Europe (CEE) is recommended - it has always been claimed that CEE countries display more similar values of health states, which differ from value sets in Western European countries, however, the first glance at both value sets does not confirm such speculations. The main limitation of the study is the year of the data collection: the completion of the valuation study has been substantially delayed (from the data collection in 2005), as earlier modelling attempts produced non-monotonic values. Attempts at ameliorating these modelling issues through the application of exclusion criteria failed, indicating that the observed non-monotonicities were not reflective of a small subgroup of respondents displaying conflicting preferences. The improved fit of the chosen model, which included a power term below 1, indicates that respondents display substantially diminishing sensitivity to increasing health problems. Whether or not this diminishing sensitivity is unique to this study population may warrant further investigation. Besides the modelling issues, sample size and the low number of health states valued were additional reasons why it was difficult to obtain the monotonic value set. Currently, the EuroQol Group Association recommends the sample size of 1,000 units to complete the valuation study with sufficient statistical power. Back in 2005, such recommendations were not in place and our data collection was limited in financial terms as well as timewise. 5 CONCLUSION The article presents the first TTO-based EQ-5D value set for Slovenia. There have been two previous attempts to present an EQ-5D VAS based value set in Slovenia, once in 2000 (12) and once in 2012 (13), however, those value sets either lacked logical consistency or consistent modelling techniques. The use of a constrained ordinary least squares approach built upon experiences from EQ-5D-5L valuation studies in China, but extended to handle diminishing sensitivity to increasing health problems, allowed us to generate logically consistent value sets for VAS and TTO. The two value sets presented in this paper are recommended for use in EQ-5D-3L studies in Slovenia. CONFLICT OF INTEREST VPR and KR are members of the EuroQol Group, a not-for-profit organisation that develops and distributes instruments for measuring and valuing health. Bereitgestellt von National & University Library Ljubljana | Heruntergeladen 23.03.20 1 1:29 UTC 11.2478/sjph-2020-0002 Zdr Varst. 2020;59(1):8-17 FUNDING The study received funding from the EuroQol Research Foundation. ETHICAL APPROVAL Not required as the data in the study is not personal, but values of hypothetical health states. REFERENCES 1. Health Insurance Institute of Slovenia. Rules of the Commission on enlisting drugs for public financing [Slovenian]. Ljubljana: Health Insurance Institute of Slovenia, 2014. Accessed March 26th, 2019 at: http://www.pisrs.si/Pis.web/pregledPredpisa?id=PRAV11493. 2. Ministry of Health of Republic of Slovenia. Procedures on handling the applications for new health care programmes [Slovenian]. Ljubljana: Ministry of Health, 2015. 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Accessed May 20th, 2019 at: www.rstudio.com. 30. Wickham H. Ggplot2 - Elegant Graphics for Data Analysis. 2nd ed. New York: Springer-Verlag, 2016. 31. Rand K. xreg: flexible multi-frame non-linear regression model. Accessed May 17th, 2019 at: https://www.github.com/ intelligentaccident/xreg. 32. Zrubka Z, Beretzky Z, Hermann Z, Brodszky V, Gulácsi L, Rencz F, et al. A comparison of European, Polish, Slovenian and British EQ-5D-3L value sets using a Hungarian sample of 18 chronic diseases. Eur J Health Econ. 2019;20(Suppl 1):119-32. doi: 10.1007/s10198-019-01069-8. 33. Rencz F, Gulácsi L, Drummond M, Golicki D, Prevolnik Rupel V, Simon J, et al. EQ-5D in Central and Eastern Europe: 2000-2015. Qual Life Res. 2016;25:2693-710. doi: 10.1007/s10198-019-01069-8. National & University Library Ljubljana | Heruntergeladen 23.03.20 1 1:29 UTC 10.2478/sjph-2020-0002 Zdr Varst. 2020;59(1):8-17 Annex 1. Slovenian EQ-5D-3L VAS value set. State Utility State Utility State Utility State Utility 11111 1 23131 0.351316 32222 0.243814 12313 0.435993 21111 0.6664 33131 0.135372 13222 0.51722 22313 0.371997 31111 0.304328 11231 0.496121 23222 0.440402 32313 0.149199 12111 0.81524 21231 0.423003 33222 0.19261 13313 0.362674 22111 0.63367 31231 0.181925 11322 0.54139 23313 0.307537 32111 0.291652 12231 0.476714 21322 0.45996 33313 0.105132 13111 0.61471 22231 0.406756 31322 0.20431 11123 0.528806 23111 0.51633 32231 0.171722 12322 0.5 19463 21123 0.44983 33111 0.236074 13231 0.396696 22322 0.442235 31123 0.198291 11211 0.817021 23231 0.337714 32322 0.19372 12123 0.507633 21211 0.634367 33231 0.126114 13322 0.431312 22123 0.432532 31211 0.291936 11331 0.414277 23322 0.367949 32123 0.187808 12211 0.753447 21331 0.353134 33322 0.146516 13123 0.421857 22211 0.605136 31331 0.136599 11132 0.489607 23123 0.359742 32211 0.279555 12331 0.398349 21132 0.417575 33123 0.141042 13211 0.587925 22331 0.339169 31132 0.17854 11223 0.508098 23211 0.496312 32331 0.12711 12132 0.470522 21223 0.432916 33211 0.22514 13331 0.330462 22132 0.401527 31223 0.188043 11311 0.618425 23331 0.2786 32132 0.168393 12223 0.488074 21311 0.519047 33331 0.084473 13132 0.391584 22223 0.416293 31311 0.237527 11112 0.793055 23132 0.333207 32223 0.177737 12311 0.590712 21112 0.624379 33132 0.123018 13223 0.406013 22311 0.498429 31112 0.287819 11232 0.470944 23223 0.345901 32311 0.226314 12112 0.73681 21232 0.401884 33223 0.131702 13311 0.485851 22112 0.596115 31232 0.168621 11323 0.423985 23311 0.414433 32112 0.275534 12232 0.452735 21323 0.361592 33311 0.17657 13112 0.579401 22232 0.38639 31323 0.142281 11121 0.767966 23112 0.489795 32232 0.158637 12323 0.407701 21121 0.612567 33112 0.221496 13232 0.376774 22323 0.347381 31121 0.282798 11212 0.737905 23232 0.320094 32323 0.132707 12121 0.718127 21212 0.596725 33232 0.113935 13323 0.338527 22121 0.58538 31212 0.275809 11332 0.393568 23323 0.285876 32121 0.270628 12212 0.69438 21332 0.334958 33323 0.089715 13121 0.569227 22212 0.570879 31332 0.124222 11133 0.384893 23121 0.481929 32212 0.263795 12332 0.378355 21133 0.327293 33121 0.217045 13212 0.555435 22332 0.321498 31133 0.118936 11221 0.719125 23212 0.471123 32332 0.114913 12133 0.369963 21221 0.585969 33212 0.210836 13332 0.313094 22133 0.314037 31221 0.2709 11312 0.582693 23332 0.262868 32133 0.109702 12221 0.678933 21312 0.49232 33332 0.073032 13133 0.305756 22221 0.560972 31312 0.222913 11113 0.568446 23133 0.256196 32221 0.258995 12312 0.557946 21113 0.481322 33133 0.068137 13221 0.545982 22312 0.473103 31113 0.216699 11233 0.370296 23221 0.463627 32312 0.211981 12113 0.544737 21233 0.314334 33221 0.206466 13312 0.46132 22113 0.462634 31233 0.109909 11321 0.572413 23312 0.393717 32113 0.205884 12233 0.355824 21321 0.484402 33312 0.163382 13113 0.451156 22233 0.301411 31321 0.218451 11122 0.705034 23113 0.385039 32233 0.100801 12321 0.548422 21122 0.577498 33113 0.157758 13233 0.29333 22321 0.465569 31122 0.266943 11213 0.545255 23233 0.244864 32321 0.207603 12122 0.667085 21213 0.463048 33233 0.059767 13321 0.454006 22122 0.553138 31213 0.206126 11333 0.307413 23321 0.387478 32122 0.255124 12213 0.523088 21333 0.257703 33321 0.159344 13122 0.538492 22213 0.445189 31333 0.069245 11131 0.516128 23122 0.457638 32213 0.195504 12333 0.294662 21131 0.439509 33122 0.202938 13213 0.434188 22333 0.24608 31131 0.192067 11222 0.66788 23213 0.370437 32333 0.060669 12131 0.49567 21222 0.55367 33213 0.148167 13333 0.238788 22131 0.422629 31222 0.255388 11313 0.453441 23333 0.194682 32131 0.181692 12222 0.634971 21313 0.386994 33333 0.021893 13131 0.412197 22222 0.530963 31313 0.15903 Bereitgestellt von National & University Library Ljubljana | Heruntergeladen 23.03.20 1 1:29 UTC 10.2478/sjph-2020-0002 Zdr Varst. 2020;59(1):8-17 Annex 2. Slovenian EQ-5D-3L TTO value set. State Utility State Utility State Utility State Utility 11111 1 23131 0.128439 32222 -0.03062 12313 0.34613 21111 0.661462 33131 -0.25294 13222 0.430732 22313 0.207237 31111 0.130169 11231 0.389626 23222 0.282156 32313 -0.18975 12111 0.853651 21231 0.245965 33222 -0.13124 13313 0.218732 22111 0.606623 31231 -0.1593 11322 0.46964 23313 0.091724 32111 0.097211 12231 0.349529 21322 0.31599 33313 -0.28289 13111 0.623976 22231 0.210278 31322 -0.10538 11123 0.49996 23111 0.444812 32231 -0.18735 12322 0.426358 21123 0.342034 33111 -0.01073 13231 0.221799 22322 0.278326 31123 -0.08574 11211 0.850867 23231 0.094535 32322 -0.13419 12123 0.455282 21211 0.60504 33231 -0.28058 13322 0.290467 22123 0.303556 31211 0.096229 11331 0.254042 23322 0.1571 11 32123 -0.11484 12211 0.770598 21331 0.12401 1 33322 -0.22977 13123 0.315952 22211 0.554403 31331 -0.25653 11132 0.385375 23123 0.180134 32211 0.063968 12331 0.218141 21132 0.242199 33123 -0.21131 13211 0.570517 22331 0.091182 31132 -0.16224 11223 0.453969 23211 0.401345 32331 -0.28333 12132 0.34543 21223 0.302415 33211 -0.04194 13331 0.1018 22132 0.20661 31223 -0.11571 11311 0.616823 23331 -0.01638 32132 -0.19025 12223 0.411364 21311 0.439082 33331 -0.37271 13132 0.2181 22223 0.265157 31311 -0.0148 11112 0.841454 23132 0.091144 32223 -0.14437 12311 0.565379 21112 0.59959 33132 -0.28336 13223 0.277172 22311 0.397094 31112 0.092834 11232 0.344249 23223 0.145055 32311 -0.04503 12112 0.763187 21232 0.205552 33223 -0.23949 13311 0.410581 22112 0.549314 31232 -0.19109 11323 0.310866 23311 0.264468 32112 0.060641 12232 0.305699 21323 0.175548 33311 -0.1449 13112 0.565321 22232 0.170885 31323 -0.2 1497 11121 0.779161 23112 0.397046 32232 -0.21871 12323 0.273356 21121 0.560205 33112 -0.04506 13232 0.182084 22323 0.141589 31121 0.067744 1 1212 0.76107 23232 0.058026 32323 -0.24229 12121 0.711564 21212 0.54785 33232 -0.31063 13323 0.152565 22121 0.512356 31212 0.059681 11332 0.213388 23323 0.030755 32121 0.03604 12212 0.69593 21332 0.086822 33323 -0.33326 13121 0.527634 22212 0.5007 31332 -0.28691 11133 0.237367 23121 0.365528 32212 0.028129 12332 0.17853 21133 0.108786 33121 -0.06823 13212 0.515767 22332 0.054748 31133 -0.26893 11221 0.709673 23212 0.355487 32332 -0.31335 12133 0.201903 21221 0.510957 33212 -0.07569 13332 0.065126 22133 0.076272 31221 0.035094 11312 0.558733 23332 -0.05061 32133 -0.29558 12221 0.65042 21312 0.391579 33332 -0.4016 13133 0.08679 22221 0.465742 31312 -0.04905 111 13 0.592636 23133 -0.03037 32221 0.003995 12312 0.510968 21113 0.419503 33133 -0.38449 13221 0.48022 22312 0.351413 311 13 -0.0288 11233 0.200849 23221 0.325111 32312 -0.07872 121 13 0.542813 21233 0.075303 33221 -0.09848 13312 0.364335 221 13 0.378293 31233 -0.29638 11321 0.521351 23312 0.223497 321 13 -0.0588 12233 0.166296 21321 0.360218 33312 -0.17692 131 13 0.391539 22233 0.043453 31321 -0.07217 1 1122 0.703183 231 13 0.247659 32233 -0.32271 12321 0.475612 21122 0.506132 331 13 -0.15798 13233 0.05376 22321 0.321143 31122 0.031824 11213 0.541361 23233 -0.06124 32321 -0.10148 12122 0.644578 21213 0.377075 33233 -0.41062 13321 0.333726 22122 0.461154 31213 -0.0597 11333 0.082479 23321 0.196118 32122 0.000785 12213 0.494567 21333 -0.0344 33321 -0.19857 13122 0.47556 22213 0.337423 31333 -0.38789 11131 0.432582 23122 0.321098 32213 -0.0892 12333 0.05049 21131 0.283774 33122 -0.10151 13213 0.350186 22333 -0.0643 31131 -0.13 11222 0.642901 23213 0.210866 32333 -0.41322 12131 0.390857 21222 0.459833 33213 -0.18688 13333 -0.05462 22131 0.247056 31222 -0.00014 11313 0.386101 23333 -0.16321 32131 -0.15845 12222 0.589543 21313 0.242843 33333 -0.498 13131 0.258901 22222 0.416978 31313 -0.16174 17 Bereitgestellt von National & University Library Ljubljana | Heruntergeladen 23.03.20 1 1:29 UTC