Valuing SF-6Dv2 in Australia Using an International Protocol

A Spanish value set for the SF-6D based on the SF-12 v1

AIM

This paper reports the first estimation of an SF-6D value set based on the SF-12 for Spain.

METHODS

A representative sample (n = 1020) of the Spanish general population valued a selection of 56 hypothetical SF-6D health states by means of a probability lottery equivalent (PLE) method. The value set was derived using both random effects and mean models estimated by ordinary least squares (OLS). The best model was chosen on the basis of its predictive ability assessed in terms of mean absolute error (MAE).

RESULTS

The model yielding the lowest MAE (0.075) was that based on main effects using OLS. Pain was the most significant dimension in predicting health state severity. Comparison with the previous SF-6D (SF-36) model estimated for Spain revealed no significant differences, with a similar MAE (0.081). Nevertheless, the new SF-6D (SF-12) model predicted higher utilities than those generated by the SF-6D (SF-36) scoring algorithm (minimum value - 0.071 vs - 0.357).

CONCLUSION

A value set for the SF-6D (SF-12) based on Spanish general population preferences elicited by means of a PLE technique is successfully estimated. The new estimated SF-6D (SF-12) preference-based measure provides a valuable tool for researchers and policymakers to assess the cost-effectiveness of new health technologies in Spain.

The Short Form 6 Dimensions (SF-6D): Development and Evolution

This paper considers the development and evolution of the short-form 6 dimensions (SF-6D), a generic preference-weighted measure consisting of a health classification with accompanying value set that was developed from one of the widest used health related quality of life measures, the SF-36 health survey. This enabled health state utility values to be directly generated from SF-36 and SF-12 data for a range of purposes, including to produce quality adjusted life years for use in economic evaluation of healthcare interventions across a range of different conditions and treatments. This paper considers the rationale for the development of the measure, the development process, performance and how the SF-6D has evolved since its conception. This includes the development of an updated version, SF-6D version 2 (SF-6Dv2), which was generated to deal with some criticisms of the first version, and now includes a standalone version for inclusion in studies without relying on use of SF-36 or SF-12. Valuation methods have also evolved, from standard gamble in-person interviews to online discrete choice experiment surveys. International work related to the SF-6Dv1 and SF-6Dv2 is considered. We also consider recommendations for use, highlighting key psychometric evidence and reimbursement agency recommendations.

Comparison of four approaches in eliciting health state utilities with SF-6Dv2

OBJECTIVE

To empirically compare four preference elicitation approaches, the discrete choice experiment with time (DCETTO), the Best-Worst Scaling with time (BWSTTO), DCETTO with BWSTTO (DCEBWS), and the Standard Gamble (SG) method, in valuing health states using the SF-6Dv2.

METHODS

A representative sample of the general population in Quebec, Canada, completed 6 SG tasks or 13 DCEBWS (i.e., 10 DCETTO followed by 3 BWSTTO). Choice tasks were designed with the SF-6Dv2. Several models were used to estimate SG data, and the conditional logit model was used for the DCE or BWS data. The performance of SG models was assessed using prediction accuracy (mean absolute error [MAE]), goodness of fit using Bayesian information criterion (BIC), t-test, Jarque-Bera (JB) test,  Ljung-Box (LB) test, the logical consistency of the parameters, and significance levels. Comparison between approaches was conducted using acceptability (self-reported difficulty and quality levels in answering, and completion time), consistency (monotonicity of model coefficients), accuracy (standard errors), dimensions coefficient magnitude, correlation between the value sets estimated, and the range of estimated values. The variance scale factor was computed to assess individuals' consistency in their choices for DCE and BWS approaches.

RESULTS

Out of 828 people who completed SG and 1208 for DCEBWS tasks, a total of 724 participants for SG and 1153 for DCE tasks were included for analysis. Although no significant difference was observed in self-reported difficulties and qualities in answers among approaches, the SG had the longest completion time and excluded participants in SG were more prone to report difficulties in answering. The range of standard errors of the SG was the narrowest (0.012 to 0.015), followed by BWSTTO (0.023 to 0.035), DCEBWS (0.028 to 0.050), and DCETTO (0.028 to 0.052). The highest number of insignificant and illogical parameters was for BWSTTO. Pain dimension was the most important across dimensions in all approaches. The correlation between SG and DCEBWS utility values was the strongest (0.928), followed by the SG and BWSTTO values (0.889), and the SG and DCETTO (0.849). The range of utility values generated by SG tended to be shorter (-0.143 to 1) than those generated by the other three methods, whereas BWSTTO (-0.505 to 1) range values were shorter than DCETTO (-1.063 to 1) and DCEBWS (-0.637 to 1). The variance scale factor suggests that respondents had almost similar level of certainty or confidence in both DCE and BWS responses.

CONCLUSION

The SG had the narrowest value set, the lowest completion rates, the longest completion time, the best prediction accuracy, and produced an unexpected sign for one level. The BWSTTO had a narrower value set, lower completion time, higher parameter inconsistency, and higher insignificant levels compared to DCETTO and DCEBWS. The results of DCEBWS were more similar to SG in number of insignificant and illogical parameters, and correlation.

A novel approach for health state valuation: Multiple bounded dichotomous choice compared to the traditional standard gamble

OBJECTIVE

to assess the feasibility of a new stated preference approach, the multiple bounded dichotomous choice (MBDC), designed to generate value sets for preference-based measurement of health-related quality of life.

METHODS

MBDC and standard gamble (SG) tasks were completed to derive SF-6Dv2 value sets from a sample of the general population in Quebec, Canada. Participants were randomized between the two approaches: 6 health states were evaluated in SG and 11 health states in MBDC. Several models were used to estimate data in each approach, and the preferred models were chosen by using mean absolute error (MAE), logical consistency of parameters, and significance levels. Results of MBDC were compared with SG in terms of acceptability (self-reported difficulty and quality levels in answering, and completion time), consistency (monotonicity of model coefficients), accuracy (standard errors), dimensions coefficient magnitude, correlation between the value sets estimated, and the range of estimated values. The intra-class correlation coefficient (ICC) was computed to assess value sets' consistency.

RESULTS

Out of 655 individuals who completed MBDC tasks and 828 who completed SG tasks, a total of 585 participants for MBDC and 714 for SG tasks were included for analysis. The preferred models for both approaches were GLS Tobit. No significant difference was observed in self-reported difficulties and qualities in answers among approaches, but MBDC had less excluded participants and was less prone to report difficulties in answering. Additionally, completion time in the MBDC group was significantly lower (99.80 vs 68.12 s). Most standard errors in the MBDC were lower than those in SG, and the number of non-significant parameters was also lower. The range of utility values generated by MBDC tended to be wider (-0.372 to 1) than those generated by the SG (-0.137 to 1) and the number of worse-than-dead states in MBDC (0.91%) was higher than for SG (0.08%). The Pain dimension was identified as the most significant, while the Vitality dimension showed the lowest significant decrement. Both approaches exhibited a tendency to overestimate severe health state values and underestimate better health state values. The correlation and ICC between the two value sets were 0.937 and 0.983, respectively.

CONCLUSION

Based on empirical evidence, it can be inferred that the MBDC method is not only feasible but also holds the potential to generate meaningful and well-informed preference data from respondents. This approach can be used to derive a value set for preference-based instrument.

Creating an SF-6Dv2 social value set for New Zealand

The SF-6D health descriptive system and its second version published in 2020, the SF-6Dv2, is used worldwide for valuing health-related quality of life (HRQoL) for economic evaluation and measuring patient-reported health outcomes. In this study, a valuation tool was developed and applied to create a social value set, comprising 18,750 health state values, for the SF-6Dv2 for New Zealand (NZ). This tool was adapted and extended from the one used to create a social value set for the EQ-5D-5L, a simpler health descriptive system with fewer dimensions and health states. The tool implements the PAPRIKA method, a type of adaptive discrete choice experiment, and a binary search algorithm to identify health states worse than dead and has extensive data quality controls to ensure the validity and reliability of the social value set derived from participants' personal value sets. The tool, accompanied by a short introductory video designed specifically for the SF-6Dv2, was distributed via an online survey to a large representative sample of adult New Zealanders in June-July 2022. The tool's data quality controls enabled participants who failed to understand or sincerely engage with the valuation tasks to be identified and excluded, resulting in the participants being pared down to a sub-sample of 2985 'high-quality' participants whose personal value sets were averaged for the social value set. These results, including participants' positive feedback, demonstrate the feasibility and acceptability of using the tool to value larger health descriptive systems such as the SF-6Dv2. Having successfully created an SF-6Dv2 social value set for NZ, the valuation tool can be readily applied to other countries, used to generate personal value sets for personalised medicine and adapted to create value sets for other health descriptive systems.

Valuing the SF-6Dv2 in the capital of Iran using a discrete choice experiment with duration

BACKGROUND

The second version of the Short-Form 6-Dimension (SF-6Dv2) classification system has recently been developed. The objective of this study was to develop a value set for SF-6Dv2 based on the societal preferences of a general population in the capital of Iran.

METHODS

A representative sample of the capital of Iran (n = 3061) was recruited using a stratified multistage quota sampling technique. Face-to-face interviews were conducted using binary choice sets from the international valuation protocol of the discrete choice experiment with duration. The conditional logit was used to estimate the final value set, and a latent class model was employed to assess heterogeneity of preferences.

RESULTS

Coefficients generated from the models were logically consistent and significant. The best model was the one that included an additional interaction term for cases where one or more dimensions reached their most severe levels. It provides a value set with logical consistent coefficients and the lowest percentage of worse than death health states. Predicted values for the SF-6Dv2 were within the range of - 0.796-1. Pain dimension had the largest impact on utility decrement, whereas vitality had the least impact. The presence of preference heterogeneity was evident, and the Bayesian Information Criterion indicated the optimal fit for a latent class model with two classes.

CONCLUSION

This study provided the SF-6Dv2 value set for application in the context of Iran. This value set will facilitate the use of the SF-6Dv2 instrument in health economic evaluations and clinical settings.

QALYs and rare diseases: exploring the responsiveness of SF-6D, EQ-5D-5L and AQoL-8D following genomic testing for childhood and adult-onset rare genetic conditions in Australia

BACKGROUND

Genomic testing transforms the diagnosis and management of rare conditions. However, uncertainty exists on how to best measure genomic outcomes for informing healthcare priorities. Using the HTA-preferred method should be the starting point to improve the evidence-base. This study explores the responsiveness of SF-6D, EQ-5D-5L and AQoL-8D following genomic testing across childhood and adult-onset genetic conditions.

METHOD

Self-reported patient-reported outcomes (PRO) were obtained from: primary caregivers of children with suspected neurodevelopmental disorders (NDs) or genetic kidney diseases (GKDs) (carers' own PRO), adults with suspected GKDs using SF-12v2; adults with suspected complex neurological disorders (CNDs) using EQ-5D-5L; and adults with dilated cardiomyopathy (DCM) using AQol-8D. Responsiveness was assessed using the standardised response mean effect-size based on diagnostic (having a confirmed genomic diagnosis), personal (usefulness of genomic information to individuals or families), and clinical (clinical usefulness of genomic information) utility anchors.

RESULTS

In total, 254 people completed PRO measures before genomic testing and after receiving results. For diagnostic utility, a nearly moderate positive effect size was identified by the AQoL-8D in adult DCM patients. Declines in physical health domains masked any improvements in mental or psychosocial domains in parents of children affected by NDs and adult CNDs and DCM patients with confirmed diagnosis. However, the magnitude of the changes was small and we did not find statistically significant evidence of these changes. No other responsiveness evidence related to diagnostic, clinical, and personal utility of genomic testing was identified.

CONCLUSION

Generic PRO measures may lack responsiveness to the diagnostic, clinical and personal outcomes of genomics, but further research is needed to establish their measurement properties and relevant evaluative space in the context of rare conditions. Expected declines in the physical health of people experiencing rare conditions may further challenge the conventional application of quality of life assessments.

Deciding Between SF-6Dv2 Health States: A Think-Aloud Study of Decision-Making Strategies Used in Discrete Choice Experiments

OBJECTIVE

This study aimed to gain insight into decision-making strategies individuals used when evaluating pairs of SF-6Dv2 health states in discrete choice experiments (DCEs).

METHODS

This qualitative, cross-sectional, noninterventional study asked participants to use a think-aloud approach to compare SF-6Dv2 health states in DCEs. Thematic analysis focused on comprehension and cognitive strategies used to compare health states and make decisions.

RESULTS

Participants (N = 40) used 3 main strategies when completing DCEs: (1) trading, (2) reinterpretation, and (3) relying on previous experience. Trading was the most common strategy, used by everyone at least once, and involved prioritizing key attributes, such as preferring a health state with significant depression but no bodily pain. Reinterpretation was used by 17 participants and involved reconstructing health states by changing underlying assumptions (eg, rationalizing selecting a health state with significant pain because they could take pain medications). Finally, some (n = 13) relied on previous experience when making decisions on some choice tasks. Participants with experience dealing with pain, for instance, prioritized health states with the least impact in this dimension.

CONCLUSIONS

Qualitatively evaluating the decision-making strategies used in DCEs allows researchers to evaluate whether the tasks and attributes are interpreted accurately. The findings from this study add to the understanding of the generation of SF-6Dv2 health utility weights and the validity of these weights (e.g., reinterpreting health states could undermine the validity of DCEs and utility weights), and the overall usefulness of the SF-6Dv2. The methodology described in this study can and should be carried forth in valuing other health utility measures, not just the SF-6Dv2.