The Chimera of WTP for a QALY: Inconsistencies of Stated Preferences in Scenario Variations

Transcription

1 The Chimera of WTP for a QALY: Inconsistencies of Stated Preferences in Scenario Variations Marlies Ahlert, Martin Luther University Halle-Wittenberg Lars Schwettmann, Martin Luther University Halle-Wittenberg Priorities 2016, Birmingham, 7 9 September 2016 Martin Luther University Halle-Wittenberg

2 1. Introduction One (potential) criterion for prioritization: Value for money Account for preferences of the population (payers, patients, voters) A health economic approach: How much would people be willing to pay for the gain of a QALY? But: A QALY is a QALY is a QALY Or is it? (Weinstein, 1988) Doubts about established methods to determine WTP for health gains MLU Halle-Wittenberg Lars Schwettmann 2

3 1. Introduction: Some Doubts about Established Methods Ryen & Svensson (2015): Framing effects Pennington et al. (2015): WTP varies with the type of QALY gains Ahlert, Breyer & Schwettmann (2016): the type of question Dolan et al. (2005): Threshold values Bobinac et al. (2012): Get more, pay more? Kvamme et al. (2010): Individual level; identification (and partial exclusion) of inconsistent answers; preference camps The Chimera of WTP for a QALY Characterize and vary health gain scenarios w.r.t. specific attributes Focus: Individual reactions and inconsistencies Data: GermanVaQ project (Marlies Ahlert, Friedrich Breyer; financed by the German Science Foundation DFG) Based on one questionnaire of the EuroVaQ project MLU Halle-Wittenberg Lars Schwettmann 3

4 2. The Surveys Basic Sequence (as in the EuroVaQ project) Introduction: Motivation of the study Collection of socio-demographic characteristics and subjective life expectancy Introduction of a health thermometer type scale: health points Description of abstract health losses: 4 Dimensions Health loss (in terms of health points) Duration of health loss Point of time of health loss Certainty of health loss MLU Halle-Wittenberg Lars Schwettmann 4

5 2. The Surveys: Example of a Scenario (Donaldson et al., 2010) MLU Halle-Wittenberg Lars Schwettmann 5

6 2. The Surveys Basic Structure (as in the EuroVaQ project) Introduction: Motivation of the study Collecting socio-demographic characteristics und subjective life expectancy Introduction of a health thermometer type scale: health points Description of abstract health losses: 4 Dimensions Health loss (in terms of health points) Duration of health loss Point of time of health loss Certainty of health loss Question: WTP for the avoidance of the respective health loss? Binary filter: Positive WTP? yes / no (not in Survey IV) If yes : State amount If no : Give reason MLU Halle-Wittenberg Lars Schwettmann 6

7 2. The Surveys: Willingness to pay (Donaldson et al., 2010) MLU Halle-Wittenberg Lars Schwettmann 7

8 2. The Surveys: Summary of Answers (Donaldson et al., 2010) MLU Halle-Wittenberg Lars Schwettmann 8

9 2. The Surveys 4 Dimensions: Health loss Duration of health loss Point of time of health loss Certainty of health loss Monotonicity properties of WTP as minimum requirements Example: Strong Health Level Monotonicity (Strong HLM): If the number of health points to be gained increases, WTP goes up Weak Health Level Monotonicity (Weak HLM): If the number of health points to be gained increases, WTP does not go down MLU Halle-Wittenberg Lars Schwettmann 9

10 2. The Surveys: Scenarios and Versions Point-of-time Monotonicity Scenario gain gain Risk Health QALY Certainty/ Version Position Duration When Duration Monotonicity A 25 points 4 years 1 in 1 year s time Certainty A 1. A 1./2. B 25 points 4 years 1 End of life Certainty B 2. C 10 points 10 years 1 in 1 year s time Certainty C 1. C 2./1 D 10 points 10 years 1 End of life Certainty D 2. E Extra life 12+months 1 End of life Certainty E 1./2. E 3./4. F No coma 12+months 1 in 1 year s time Certainty F 2./1. F Health Level Monotonicity 4./3. G Extra life 12+months 1 in 1 year s time Certainty G 5. G 5. H 25 points 4 years 1 in 1 year s time Certainty H 4. I 25 points 1 year 0.25 in 1 year s time Certainty I 3./4. J 10 points 1 year 0.1 in 1 year s time Certainty J 4./3. K 25 points 4 years 0.1 in 1 year s time 10% risk K 3./4. L 10 points 10 years 0.1 in 1 year s time 10% risk L 3. M 25 points 4 years 0.05 in 1 year s time 5% risk M 4./3. MLU Halle-Wittenberg Lars Schwettmann 10

11 2. The Surveys: Different Framings (GermanVaQ project only) Stronger emphasis on hypothetical nature Survey I Survey II Contact Online Online Survey III Survey IV No Yes Yes Yes Face-toface Online Binary filter Yes Yes Yes No Total number of scenarios Sample size MLU Halle-Wittenberg Lars Schwettmann 11

12 3. Results: Health Level Monotonicity (HLM) Scenario I: 25 points; duration: 1 year; in 1 year s time, certainty Scenario J: 10 points; duration: 1 year; in 1 year s time, certainty Comparison of Stated Values Strong HLM Weak HLM Value I > Value J Value I = Value J > Value I = Value J = Value J > Value I Sample size Surveys I II III IV 43.3% 50.9% 51.4% 61.2% 23.3% 21.1% 27.9% 26.8% 22.1% 18.4% 9.9% 0.8% 11.3% 9.6% 10.8% 11.2% MLU Halle-Wittenberg Lars Schwettmann 12

13 3. Results: Mean Willingness to pay for a QALY (in, after 1% trimming) Scenario I: Mean WTP / QALY Total sample Only Strong HLM Scenario J: Mean WTP / QALY Total sample Only Strong HLM Survey I 21,070 43,428 30,349 Survey II 28,802 39,852 39,959 Survey III 33,009 38,228 63,601 Survey IV 48,608 57,102 74,646 35,870 34,462 25,625 53,342 MLU Halle-Wittenberg Lars Schwettmann 13

14 Results: Prominent Numbers Stated Value I = Stated Value J > 0 (n=281): Prominent numbers 12% 10% 8% 6% 4% 2% 0% MLU Halle-Wittenberg Lars Schwettmann 14

15 4. Discussion and Conclusion Exclusions based on violations of Strong HLM would reduce the total sample by about 50%! Exclusions would change mean values of WTP for a QALY Survey design matters Similar results for other monotonicity properties MLU Halle-Wittenberg Lars Schwettmann 15

16 4. Discussion: Point-of-time Monotonicity (PotM) Scenario A: 25 points; duration: 4 years; in 1 year s time, certainty Scenario B: 25 points; duration: 4 years; at the end of life, certainty Comparison of Stated Values Strong PotM Surveys Indiff. I II III IV Value A > Value B % 52.8% 62.4% 46.0% Value A = Value B > % 17.4% 17.1% 29.9% Value A = Value B = % 9.6% 3.4% 1.4% Value B > Value A % 20.2% 17.1% 22.7% Sample size MLU Halle-Wittenberg Lars Schwettmann 16

17 4. Discussion: Duration Monotonicity (DurM) Scenario C: 10 points; duration: 10 years; in 1 year s time, certainty Scenario J: 10 points; duration: 1 year; in 1 year s time, certainty Comparison of Stated Values Strong DurM Weak DurM Surveys I II III IV Value C > Value J % 48.3% 50.5% 52.2% Value C = Value J > % 19.8% 16.2% 24.2% Value C = Value J = % 15.0% 9.5% 1.1% Value J > Value C % 16.8% 23.8% 22.6% Sample size MLU Halle-Wittenberg Lars Schwettmann 17

18 4. Discussion and Conclusion Exclusions based on violations of Strong HLM would reduce the total sample by about 50%! Exclusions would change mean values of WTP for a QALY Survey design matters Similar results for other monotonicity properties What can we do? Ignoring the problem? Data analysis: Exclusion of inconsistent cases? Design: Avoid inconsistencies by more simple questions? Better understanding of sources of inconsistencies Consideration of cognitive processes: How do respondents perceive characteristics of scenarios? How do they determine WTP? MLU Halle-Wittenberg Lars Schwettmann 18

19 Literature Ahlert, M., Breyer, F., & Schwettmann, L. (2016). How you ask is what you get: Framing effects in willingness-to-pay for a QALY. Social Science & Medicine, 150, Bobinac, A., van Exel, N. J. A., Rutten, F. F. H., & Brouwer, W. B. F. (2012). Get more, pay more? An elaborate test of construct validity of willingness to pay per QALY estimates obtained through contingent valuation. Journal of Health Economics, 31, Dolan, P., Shaw, R., Tsuchiya, A., Williams, A. (2005). QALY maximisation and people s preferences: A methodological review of the literature. Health Economics, 14, Donaldson, C. et al. (2010), European Value of a Quality Adjusted Life Year, Final Publishable Report. Kvamme, M. K., Gyrd-Hansen, D., Olsen, J. A., Kristiansen, I.S. (2010). Increasing marginal utility of small increases in life-expectancy? Results from a population survey. Journal of Health Economics, 29, MLU Halle-Wittenberg Lars Schwettmann 19

20 Literature Pennington, M., Baker, R., Brouwer, W., Mason H., Gyrd-Hansen, D., Robinson, A., Donaldson, C., & the EuroVaQ Team (2015). Comparing WTP values of different types of QALY gain elicited from the general public. Health Economics, 24, Ryen, L., & Svensson, M. (2015). The willingness to pay for a quality adjusted life year: A review of the empirical literature. Health Economics, 24, Weinstein, M.C. (1988). A QALY is a QALY is a QALY Or is it?, Journal of Health Economics, 7, MLU Halle-Wittenberg Lars Schwettmann 20

21 Further Results Stated Value I > Stated Value J 0: Subdivisions Scenario I: 25 points; duration: 1 year; in 1 year, certainty (QALY gain: 0.25) Scenario J: 10 points; duration: 1 year; in 1 year, certainty (QALY gain: 0.10) Subdivisions (w.r.t. stated values) Resulting Value of a QALY (VaQ) Survey I (n=149) Survey II (n=174) Survey III (n=57) Survey IV (n=224) Value I > 0 and Value J = 0 Value I Value J > 2.5 Value I Value J = 2.5 Value I Value J < 2.5 VaQ I > VaQ J VaQ I > VaQ J VaQ I = VaQ J VaQ I < VaQ J 23.5% 29.3% 33.3% 0.4% 23.5% 24.7% 21.1% 40.2% 3.4% 4.6% 1.8% 7.1% 49.7% 41.4% 43.9% 52.2% MLU Halle-Wittenberg Lars Schwettmann 21

22 Further Results: Binary Logit Model Fulfilment of Strong HLM Characteristic dy/dx (S.E.) Male (0.032) Age *** (0.001) Income: very low ** (0.051) low * (0.050) high 0.095* (0.049) very high (0.051) no response ** (0.054) East Germany (0.036) OECD coefficient ** (0.029) Education: medium 0.120*** (0.036) high 0.139*** (0.041) Health: ** (0.050) (0.049) * (0.038) Ordering: Scenario I before J (0.031) Survey II (0.040) Survey III 0.127** (0.054) Survey IV 0.165*** (0.038) # observations = 1159 y = Pr(fulfilment) = Wald χ 2 (18) = 94.82*** Pseudo R 2 = Levels of significance: * 10%, ** 5%, *** 1% 22

23 Further Results: Point-of-time Monotonicity (PotM) II Scenario C: 10 points; duration: 10 years; in 1 year s time, certainty Scenario D: 10 points; duration: 10 years; at the end of life, certainty Comparison of Stated Values Strong PotM Surveys Indiff. I II III IV Value C > Value D % 40.0% 40.8% 36.8% Value C = Value D > % 24.8% 19.4% 36.0% Value C = Value D = % 16.4% 13.6% 1.1% Value C > Value D % 18.8% 26.2% 26.1% Sample size MLU Halle-Wittenberg Lars Schwettmann 23

24 Further Results: Risk Monotonicity (RiskM) Scenario K: 25 points; duration: 4 years; in 1 year s time, risk: 10% (0.1 QALY) Scenario M: 25 points; duration: 4 years; in 1 year s time, risk: 5% (0.05 QALY) Comparison of Stated Values Strong RiskM Weak RiskM Surveys I II III IV Value K > Value M % 41.9% 41.3% 44.3% Value K = Value M > % 24.0% 25.6% 35.5% Value K = Value M = % 22.6% 23.1% 2.7% Value M > Value K % 11.5% 9.9% 17.5% Sample size MLU Halle-Wittenberg Lars Schwettmann 24

25 Screenshots (Donaldson et al., 2010) MLU Halle-Wittenberg Lars Schwettmann 25

26 Screenshots (Donaldson et al., 2010) MLU Halle-Wittenberg Lars Schwettmann 26

27 Screenshots (Donaldson et al., 2010) MLU Halle-Wittenberg Lars Schwettmann 27

28 Screenshots (Donaldson et al., 2010) MLU Halle-Wittenberg Lars Schwettmann 28

29 Further Results: Verbal Answers (only Surveys I to III) [3] Stated Value I = Stated Value J = 0 (n=150) Given Answers I J It wouldn t be too bad / I could live with it 39.6% 43.3% I would get better anyway, so it is not worth paying for the treatment. I do value the treatment, but I cannot afford to pay anything for it. I do value the treatment, but do not want to pay because the government should provide health care. 43.6% 42.0% 38.9% 41.3% 15.4% 13.3% Other, please specify 5.4% 5.3% Remark: Respondents were asked to select as many of the options that applied to them. MLU Halle-Wittenberg Lars Schwettmann 29

30 Further Results: Sample Characteristics (Surveys I to III) Characteristics Mean value (n=795) Male 0.46 Age Household- Very low ( 1375) Income Low ( ) Medium ( ) High ( ) Very high (>= 3600) Missing response East Germany incl. Berlin 0.24 OECD coefficient Level of education: Low 0.36 Medium 0.38 High 0.25 Health level: Survey I 0.43 Survey II 0.43 Survey III