A systematic review of SF-6D health state valuation studies

Figures & data

Figure 1. PRISMA flowchart up to 8 September 2022. Notes. *References refer to articles that were found from the reference list of the articles included.

Table 1. Main characteristics of SF-6D studies.

Authors	Country/region	Year of survey	Population	No. respondents	Version*	Survey mode	Elicitation method	Death or pits	No. health states	Primary/secondary data
Brazier et al. 1998^11	UK	NA	Health professions, students & patients	165	v0	Self-completion	VAS^a+SG	Death	59	Primary
Brazier et al. 2002^6	UK	NA	General	836	v1	Face-to-face	SG	Pits + death	249	Primary
Brazier and Roberts 2004^13	UK	NA	General	836	v1 SF12	Face-to-face	SG	Pits + death	241	Primary
Kharroubi et al. 2005^14	UK	NA	General	836	v1	Face-to-face	SG	Pits + death	249	Secondary
McCabe et al. 2006^15	UK	NA	General	836	v1	Face-to-face	Rank	Pits + death	249	Secondary
Kharroubi et al. 2007^16	UK	NA	General	836	v1	Face-to-face	SG	Pits + death	249	Secondary
Kharroubi et al. 2007^17	UK	NA	General	836	v1	Face-to-face	SG	Pits + death	249	Secondary
Lam et al. 2008^18	Hong-Kong	2002–2003	General	126	v1	Face-to-face	SG	Death	50	Primary
Brazier et al. 2009^19	Japan	2005	General	600	v1	Face-to-face	SG	Pits + death	241	Primary
Ferreira et al. 2010^20	Portugal	2006	General	140	v1	Face-to-face	SG	Pits + death	55	Primary
Ferreira et al. 2011^21	Portugal	2006	General	140	v1	Face-to-face	Rank	Pits	55	Secondary
Cruz et al. 2011^22	Brazil	2007–2008	General	469	v1	Face-to-face	SG	Pits + death	248	Primary
McGhee et al. 2011^23	Hong-Kong	2004–2006	General	641	v1	Face-to-face	SG	Pits + death	197	Primary
Méndez et al. 2011^24	Spain	2007	General	1020	v1	Face-to-face	Lottery equivalent	Death	78	Primary
Abellán Perpiñán et al. 2012^25	Spain	2007	General	1020 + 60	v1	Face-to-face	LE + SG	Death	78	Secondary
Craig et al. 2013^26	USA	2010	General	666	v1	Online	DCE	Death	NA	Primary
Kharroubi et al. 2013^27	Hong-Kong	2004–2006	General	641	v1	Face-to-face	SG	Pits + death	197	Secondary
Kharroubi et al. 2014^28	HK vs. UK	2004–2005; NA	General	641;836	v1	Face-to-face	SG	Pits + death	197;249	Secondary
Norman et al. 2014^29	Australia	NA	General	1160	v1	Online	DCE	Death	NA	Primary
Craig 2016^30	UK	NA	General	836	v1	Face-to-face	SG	Pits + death	249	Secondary
Kharroubi 2015^31	Japan vs. UK	2005; NA	General	600;836	v1	Face-to-face	SG	Pits + death	241;249	Secondary
Kharroubi 2018^9	HK & UK	2004–2005; NA	General	641;836	v1	Face-to-face	SG	Pits + death	197;249	Secondary
Jonker et al. 2018^32	Netherlands	NA	General	1775	v1 SF-12	Online	DCEtto	None	224	Primary
Kharroubi et al. 2020^33	Lebanon	2019	University	126	v1	Face-to-face	SG	Pits	49	Primary
Kharroubi 2020^34	Lebanon	2019	University	126	v1	Face-to-face	SG	Pits	49	Secondary
Mulhern et al. 2020^8	UK	NA	General	3014	v2	Online	DCEtto +  DCEtto with death	Death	About 600	Primary
Dufresne et al. 2021^35	Canada	2018–2019	Food-allergic patients OR parents	241 + 125	v2	Face-to-face OR online	TTO + DCE	Pits + death	76 + 140	Primary
Kharroubi et al. 2021^36	Japan & UK	2005; NA	General	600;836	v1	Face-to-face	SG	Pits + death	241;249	Secondary
Xie et al. 2020^37	China (Tianjin)	2018	General	533	v2	Face-to-face	TTO +  DCE/DCEtto	Pits + death	115 + 300	Primary
Wu et al. 2021^38	China (eight provinces)	2019	General	3401	v2	Face-to-face	TTO +  DCEtto	Death	295 + 600	Primary
Osman et al. 2021^39	China	2019	General	463	v2	Online	BWS	Death	About 1000	Primary
Authors	Best model	Pits in best model	MAE	No. Inconsistency^b	CREATE score	Highest dimension	Lowest dimension	Order of dimensions
Brazier et al. 1998^11	Fixed effect	0.455	NA	2	27/35	MH (−0.200)	VT (NS)	MH,PN,PF,RL,SF,VT
Brazier et al. 2002^6	Mean (10)	0.296	0.073	5	31/35	PN (−0.167)	RL (−0.050)	PN,MH,PF,VT,SF,RL
Brazier and Roberts 2004^13	Mean parcimonious (4)	0.345	0.079	0	29/35	PN (−0.137)	PF (−0.045)	PN,MH,VT,SF,RL,PF
Kharroubi et al. 2005^14	MCMC	0.203	0.103^c	–	21/35	–	–	–
McCabe et al. 2006^15	Mean rescaled	0.18	0.08	3	18/35	PF (−0.225)	RL (−0.063)	PF,PN,MH,SF,VT,RL
Kharroubi et al. 2007a^16	MCMC	0.203	0.089^c	–	22/35	–	–	–
Kharroubi et al. 2007b^17	MCMC covariates	0.209	–	–	24/35	–	–	–
Lam et al. 2008^18	OLS mean (3)	0.152	0.054	5	28/35	PF (−0.232)	RL (−0.067)	PF,PN,VT,SF,MH,RL
Brazier et al. 2009^19	Random effects	0.387	0.111	6	30/35	PF (−0.137)	VT (−0.055)	PF,PN,SF,RL,MH,VT
Ferreira et al. 2010^20	GEE (7)	0.373	0.209	8	28/35	PF (−0.214)	RL (−0.054)	PF,MH,VT,PN,SF,RL
Ferreira et al. 2011^21	Rank-ordered parcimonious (3)	0.124	0.1	0	27/35	PF (−0.349)	RL (−0.051)	PF,PN,SF,MH,VT,RL
Cruz et al. 2011^22	Random effects (3)	0.264	0.064	9	32/35	PF (−0.177)	VT (−0.083)	PF,PN,MH,SF,RL,VT
McGhee et al. 2011^23	OLS mean	0.315	0.057	0	26/35	PF (−0.178)	RL (−0.054)	PF,PN,MH,SF,VT,RL
Méndez et al. 2011^24	IPW adjusted	−0.515	0.167^c	4	28/35	PF (−0.451)	RL (−0.134)	PF,PN,VT,SF,MH,RL
Abellán Perpiñán et al. 2012^25	OLS mean (3)	−0.357	0.081	0	31/35	PF (−0.338)	RL (−0.70)	PF,PN,MH,SF,VT,RL
Craig et al. 2013^26	Stacked probit	0.013	–	0	26/35	PN (−0.221)	VT (−0.12)	PN,MH,PF,SF,RL,VT
Kharroubi et al. 2013^27	MCMC covariates	0.098	0.085^c	–	26/35	–	–	–
Kharroubi et al. 2014^28	Bayesian model	0.066;0.100	0.07^c	–	26/35	PF;PN	VT;SF	PF,MH,PN,SF,RL,VT(HK) PN,PF,MH,VT,RL,SF(UK)
Norman et al. 2014^29	Random effects (B)	−0.363	–	1	27/35	PN	RL	PN,PF,MH,VT,SF,RL
Craig 2016^30	ERUM	−0.029	–	0	26/35	PN (−0.248)	RL (−0.085)	PN,PF,MH,SF,VT,RL
Kharroubi 2015^31	Bayesian model	0.224;0.220	0.08;0.09	–	21/35	–	–	–
Kharroubi 2018^9	Bayesian model (HK/UK)	0.0708	0.045^c	10%	25/35	–	–	–
Jonker et al. 2018^32	MCMC (hyperbolic)	−0.44	–	0	29.5/35	PN (−0.38)	RL (−0.16)	PN,MH,VT,SF/PF,RL
Kharroubi et al. 2020^34	OLS mean	0.431	0.036	2	29.5/35	PF (−0.168)	VT (−0.024)	PF,SF,PN,MH,RL,VT
Kharroubi 2020^34	BR + RE	0.331	0.058^c	2	30.5/35	PF (−0.797)	VT (−0.246)	PF,SF,RL,PN,MH,VT
Mulhern et al. 2020^8	CL anchored model (3)	−0.574	NA	0	30/35	PN (−0.620)	RF (−0.102)	PN,MH,PF,SF,VT,RL
Dufresne et al. 2021^35	Hybrid (likelihood)	−0.119	NA	1	28/35	PN (−0.281)	SF/RL (−0.107)	PN,MH,PF,VT,SF/RL
Kharroubi et al. 2021^36	Bayesian models (2)	0.289	0.076^c	–	26.5/35	–	–	–
Xie et al. 2020^37	FE (TTO); Conditional logit (DCE)	TTO (−0.229); DCE (−0.105); DCEtto (−0.726)	TTO (0.1003)^d; DCE (0.1339)^d; DCEtto (0.162)^d	TTO (3); DCE (2); DCEtto (3)	30.5/35	TTO (PF:−0.410); DCE (PF:−0.364)); DCEtto (PN:−0.518)	TTO (RL:−0.086); DCE (SF:−0.067); DCEtto (SF:−0.058)	PF,PN,MH,SF,VT,RL(TTO) PF,PN,MH,RL,VT,SF(DCE) PN,PF,MH,VT,RL,SF(DCEtto)
Wu et al. 2021^38	RE (TTO); Conditional logit (DCE)	TTO (−0.277); DCEtto (−0.535)	TTO (0.0616)^d	0	33/35	TTO (PN:−0.427); DCEtto (PN:−0.541)	TTO (RL:−0.097); DCEtto (RL:−0.112)	PN,PF,MH,VT,SF,RL(TTO;DCEtto)
Osman et al. 2021^39	Anchored rescaled Hierarchical Bayes	0.197	–	0	33/35	PN (−0.2034)	VT (−0.1032)	PN,PF,MH,SF,RL,VT

Abbreviations. NA, not available; UK, United Kingdom; HK, Hong-Kong; NA, non-available; VAS, visual analog scale; SG, standard gamble; LE, lottery equivalent; DCE, discrete choice experiment; DCEtto, discrete choice experiment with a duration dimension; TTO, time trade-off; BWS, best-worst scaling; MCMC, Markov-chain Monte-Carlo; OLS, ordinary least square; GEE, generalized estimating equations; IPW, inverse probability weighting; ERUM, episodic random utility model; MAE, mean absolute error; TTO, time trade-off; DCE, discrete choice experiment; DCEtto, discrete choice experiment with a duration dimension; NS, not significant; BR, Beta regression; RE, random effect; FE, fixed effect; CL, conditional logit. *SF-6D version. ^aResults for VAS are not presented. ^bInconsistency indicates the number of coefficients on the dimension levels that are not decreasing when the health state gets worse. ^cValue is for RMSE (root mean square error). ^dValue is for MAD (mean absolute difference).

Table 2. Survey design characteristics in primary studies.

Characteristics	Number of studies	References^a
SF-6D version
v0	1	^11
v1	10	^6^,^18–20^,^22–24^,^26^,^29^,^33
v1 from SF-12	2	^13^,^32
v2	5	^8^,^35^,^37–39
Elicitation methods*
SG	7	^6^,^13^,^18–20^,^22^,^23^,^33
VAS	1	^11
Rank	2	^6^,^20b
Lottery equivalent (LE)	1	^24
DCE	4	^26^,^29^,^35^,^37
DCEtto	4	^8^,^32^,^37^,^38
DCEtto with death	1	^8
TTO	3	^35^,^37^,^38
BWS	1	^39
Use of death or pits
Death	8	^8^,^11^,^18^,^24^,^26^,^29^,^38^,^39
Pits	1	^33
Pits + death	7	^6^,^13^,^19^,^20^,^22^,^23^,^35^,^37
None	1	^32
Survey mode*
Self-completion	1	^11
Face-to-face	11	^6^,^13^,^18–20^,^22–24^,^33^,^35^,^37^,^38
Online	6	^8^,^26^,^29^,^32^,^35^,^39
Survey year
NA	5	^6^,^8^,^11^,^13^,^29^,^32
2002–2003	1	^18
2004–2006	1	^23
2005	1	^19
2006	1	^20
2007	1	^24
2007–2008	1	^22
2010	1	^26
2018–2019	1	^35
2018	1	^37
2019	3	^33^,^38^,^39
No. Respondents
<200	4	^11^,^18^,^20^,^33
201–500	3	^22^,^35^,^39
501–1000	5	^6^,^13^,^19^,^23^,^26^,^37
1001–2000	3	^24^,^29^,^32
>2000	2	^8^,^38

Notes. ^a[13] used a subsample from [6]. ^bRank data were analyzed in [15] and [21], respectively. *Some surveys used more than one method.

Table 3. Modeling characteristics in SF-6D studies.

Best model	Regression approach	References
Individual fixed effect	Additive OLS	^11^,^37
Mean level model	Additive OLS (constant forced to unity)	^7^,^18^,^25^,^33
Parsimonious consistent model at mean level	OLS constrained	^13^,^23
	Rank-ordered logistic regression (ROLogit)	^21
Mean rescaled	Conditional logistic regression	^15
Random effects individual-level model	GEE (constant forced to unity)	^19^,^20^,^22
	Probit regression (with life expectancy)	^29
	Tobit regression	^38
Anchored model	Conditional logistic regression	^33^,^37^,^38
Bayesian non-parametric model	MCMC	^14^,^16^,^32
	MCMC covariates	^17^,^33
	Estimated function for the difference between HK & UK; Japan & UK	^28^,^31
	UK as informative prior to HK; Japan	^9^,^36
Beta regression with random effects	MCMC	^27
Anchored Hierarchical Bayes model	MCMC	^39
Inverse probability weighted (IPW) adjusted	Semi-parametrically estimated	^24
Episodic random utility model (ERUM)	Stacked probit	^26
	Semi-parametric	^30
Hybrid regression model	Likelihood	^35

Abbreviations. OLS, ordinary least square; GEE, generalized estimating equations; MCMC, Markov-chain Monte-Carlo; UK, United Kingdom; HK, Hong-Kong.

Table 4. Value set and dimension categorized by culture.

Anglo-Saxon country/region	PF	RL	SF	PN	MH	VT	Ref	Order of dimensions
UK v2	3	6	4	1	2	5	^8	PN,MH,PF,SF,VT,RL
USA v1	3	5	4	1	2	6	^26	PN,MH,PF,SF,RL,VT
Australia v1	2	6	5	1	3	4	^29	PN,PF,MH,VT,SF,RL
Netherlands^a v1	4/5	6	4/5	1	2	3	^32	PN,MH,VT,SF/PF,RL
Canada^b v2	3	5/6	5/6	1	2	4	^35	PN,MH,PF,VT,SF/RL
Non-Anglo-Saxon country/region	PF	RL	SF	PN	MH	VT	Ref	Order of dimensions
Hong Kong v1	1	6	4	2	3	5	^23	PF,PN,MH,SF,VT,RL
Japan v1	1	4	3	2	5	6	^19	PF,PN,SF,RL,MH,VT
Portugal v1	1	6	5	4	2	3	^20	PF,MH,VT,PN,SF,RL
Brazil v1	1	5	4	2	3	6	^22	PF,PN,MH,SF,RL,VT
Spain v1	1	6	4	2	5	3	^24	PF,PN,VT,SF,MH,RL
Lebanon v1	1	5	2	3	4	6	^33	PF,SF,PN,MH,RL,VT
China v2	2	6	5	1	3	4	^38	PN,PF,MH,VT,SF,RL

Notes. ^aNetherland people speak Flemish. However, this language shares a common root with English. ^bIn the Canadian sample, most respondents were from Quebec and responded in French, but they are evolving in a North American context.

The columns in red/green stand for the most/least important dimension in each category.

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