quality of life in epilepsy: comparison of four preference measures

Epilepsy Research 29 (1998) 201–209

Quality of life in epilepsy: comparison of four preferencemeasures

Knut Stavem *

HELTEF Foundation for Health Ser6ices Research and Medical Department, Central Hospital of Akershus,N-1474 Nordbyhagen, Norway

Received 27 April 1997; received in revised form 16 September 1997; accepted 21 September 1997

Abstract

Several specific and general measures are available for the assessment of overall health related quality of life inepilepsy. Few of the commonly used measures provide utility weights for use in cost-utility analyses. This studycompares four methods for measuring utility weights: time trade-off (TTO), standard gamble (SG), 15D, end theEuroQol visual analog scale. All patients aged 18–67 years with a diagnosis of epilepsy, who had been admitted toor attended the outpatient clinic at a large county hospital 1987–1994, received a comprehensive questionnaire. From397 respondents, 82 patients were randomly selected. Most of the 57 patients completing the study generally hadwell-controlled epilepsy, but were still on anti-epileptic medication. Mean age was 44 years. Fourty-one percent weremale and 59% female. The resulting utility weights differed considerably between the measures, both with regard tocentral tendency and dispersion. Median utility scores: EuroQol visual analog scale 0.75, 15D 0.90, TTO 0.98, SG0.99. There was a good association between the EuroQol rating scale and the 15D, and a moderate associationbetween SG and TTO. These preference instruments measure different aspects of health-related quality of life andthus yield different results. Caution should be taken when interpreting cost-utility studies, as results will depend onthe choice of utility instrument. © 1998 Elsevier Science B.V. All rights reserved.

Keywords: Epilepsy; Quality of life; Utility; Preferences; Values; Health economics

1. Introduction

In economic evaluation studies of health inter-ventions cost-utility is an important outcome, in-dicating the monetary cost of achieving a healthimprovement. Results of cost-utility studies en-able comparisons across diseases, interventions

* Present address: Department of Thoracic Medicine, Rik-shospitalet, N-0027 Oslo, Norway. Tel.: +47 22868755/22868725; fax: +47 22868759.

0920-1211/98/$19.00 © 1998 Elsevier Science B.V. All rights reserved.

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K. Sta6em / Epilepsy Research 29 (1998) 201–209202

and technologies. Utility as a concept is describedin classical utility theory as von Neumann–Mor-genstern utilities (Von Neumann and Morgen-stern, 1953), incorporating a person’s attitude torisk in deriving expected utilities for decisionsunder uncertainty. Utility or preference weight fora health state is a score between 0 and 1, repre-senting a continuum between death and perfecthealth. These preference weights for health statesare derived through a valuation process, and theresulting preference weights are referred to asvalues, preferences, utilities, weights or quality oflife, frequently used interchangably in the litera-ture (Froberg and Kane, 1989a). In this paper theterms preferences or utilities will generally beused.

A number of specific measures are now avail-able for the assessment of overall health relatedquality of life (HRQL) in epilepsy, including theEpilepsy Surgery Inventory (ESI-55) (Vickrey etal., 1992), Quality of Life Inventory for Epilepsy(QOLIE-89) (Vickrey et al., 1993; Devinsky et al.,1995), and the Liverpool Quality of Life Assess-ment Battery (Baker et al., 1993). In additiongeneral measures have been integrated or usedseparately, such as the Short Form 36 (Ware andSherbourne, 1992; Hays et al., 1993), the Notting-ham Health Profile (Hunt et al., 1980), and theSickness Impact Profile (SIP) (Bergner et al.,1981; Langfitt, 1995). These scales are all profilemeasures, providing separate scores in several do-mains or aggregate scores in a few dimensions.They do not provide 0–1 values for health states,and thus cannot be used in cost utility studies.

Different procedures can be used for generatingutilities. The standard gamble (SG) method (VonNeumann and Morgenstern, 1953) is the classicaltechnique of measuring cardinal preferences fordifferent health outcomes under conditions of un-certainty (Torrance, 1986). A commonly used sub-stitute is the time trade-off technique (TTO),developed for use in health care by Torrance et al.(Torrance et al., 1972). Preferences can also begenerated using rating scales (RS), multiattributescales, equivalence techniques or willingness topay (Froberg and Kane, 1989a; Nord, 1992;Revicki and Kaplan, 1993).

If these methods are to be used in analysesabout resource allocation decisions, it would behelpful to know whether the methods yield thesame results. This study is an effort to establishpreference weights for health states among pa-tients with epilepsy. Four methods for measuringindividual health state preferences of persons withepilepsy, on a scale between 0 (death or a condi-tion worse than death) and 1 (complete health),are compared: TTO, SG, a 15-dimensional multi-attribute scale (15D) (Sintonen and Pekurinen,1993) and a rating scale (EuroQol visual analogscale; EQ-VAS) (EuroQol© Group, 1990). Thepairwise agreement and rank correlation betweenthe four methods is compared in a population ofpatients with well controlled epilepsy.

2. Materials and methods

2.1. Subjects

All patients aged 18–67 years, who had beenadmitted to the Central Hospital of Akershus orattended the hospital’s outpatient clinic forepilepsy (ICD-9 code 345) during a period of 7years (1987–94), received a comprehensive ques-tionnaire by mail. The diagnosis of epilepsy wasconfirmed by review of the medical record in 696patients, using standard criteria. These patientswere assumed to be representative for the popula-tion of epilepsy patients in the hospital’s catch-ment area of 280 000 inhabitants, as there are noneurologists in private practice in the area, andgeneral practitioners would generally rely on spe-cialist services at the hospital.

From the 397 respondents to this questionnaire,82 patients were randomly selected for this study,and 57 completed the study. All respondents wereassigned a number, and random numbers weregenerated through the random number functionof a spreadsheet.

The participating patients received a self-ad-ministered questionnaire including the 15D instru-ment (Sintonen and Pekurinen, 1993; Sintonen,1994a,b) and the EuroQol visual analog scale(EuroQol© Group, 1990; Essink-Bot et al., 1993),and within 2 days they were interviewed with SG

K. Sta6em / Epilepsy Research 29 (1998) 201–209 203

and TTO. All interviews were done by the sameinterviewer. Time trade-off was administered be-fore the standard gamble during the interviews,and the order was the same throughout the study.Two reminders were sent.

2.2. Preference measures

Standard gamble is derived from the axioms ofutility theory (Von Neumann and Morgenstern,1953) and has been used extensively in decisionanalysis. The patients faced a choice between twoalternatives: (1) living 10 years in their presentcondition, or (2) participating in a gamble with aprobability of P of immediate death, and a proba-bility of 1−P of becoming healthy. Then P wasvaried until the patient was indifferent betweenthe two alternatives. The preference assigned totheir own condition was 1−P, using P at thepoint of indifference. The method is described indetail elsewhere (Torrance, 1986; Froberg andKane, 1989b; Kaplan et al., 1993). A 10-yearperspective was used to ease comparison withother techniques. No visual aid was used.

TTO is a commonly used substitute for thestandard gamble, which is easier for subjects tocomprehend (Torrance et al., 1972; Torrance,1986; Froberg and Kane, 1989b; Kaplan et al.,1993). Patients were given a choice between twooutcomes: (1) Living 10 years in their presentcondition or (2) x years in full health, where xwas varied until the patient was indifferent be-tween the two alternatives. The preference weightfor the actual health state was determined as x/10.A slide board was used as a visual aid in this partof the interview. The patients initially scored twotheoretical conditions as a warming up, beforeproceeding to their own case.

EuroQol visual analog scale: the EuroQol ques-tionnaire has been developed by a Europeangroup as a standard non disease-specific instru-ment for describing and valuing quality of life(EuroQol© Group, 1990). It is a descriptive clas-sification system consisting of five items, eachwith three levels—now called EQ-5D. In additionit contains a vertical visual analog scale of thethermometer type for assessing global healthstatus today from worst imaginable to best imag-

inable health state (0–100). This study used a 10cm version of the EuroQol visual analog scale(EQ-VAS).

15D is a multiattribute utility measure, having15 dimensions each with five levels (Sintonen andPekurinen, 1993; Sintonen, 1994a,b). It is devel-oped in Finland and validated in a large Finnishpopulation. Population weights are assigned toeach item score, and the weights for all 15 itemscores are added to yield a utility score between 0and 1. The self-administered questionnaire wastranslated into Norwegian independently by twophysicians (from an English version), who dis-cussed the translations before arriving at a con-sensus version. This version was againbacktranslated into English for comparison withthe original version. The Norwegian version hasalso been compared with the original Finnishversion, finding the translation satisfactory.

2.3. Data analysis

Agreement between the measures was assessedby estimating limits of agreement, using themethod of Bland and Altman (Altman and Bland,1983; Bland and Altman, 1986). Validity was as-sessed several ways: (1) association with the otherpreference measures, and (2) association withmedication use and having had seizures during thelast year. Convergence with the first and diver-gence relative to the latter would be expected andwould support construct validity.

The distribution of scores on the preferencescales were skewed with a compression in theupper end of the scale. Therefore, Spearman’srank correlation was used for assessing associa-tions. Comparison of respondents and final inter-viewees was done using Mann–Whitney U test.Analyses were performed with the SPSS statisticalsoftware (SPSS, Chicago, IL). The study was ap-proved by the regional medical ethics review com-mittee.

3. Results

Fifty-seven patients completed the study. Inaddition two patients were interviewed and ex-


Table 1Analysis of respondents with epilepsy at various stages

Final interviewees (n=57)Respondents (n=397)Total sample (n=696)

43.743.8Mean age (years) 43.349.7 52.4Gender (% female) 57.9— 17.8aMean duration of epilepsy (years) 16.8b

29.5 26.3—% With seizures last year80.1 75.4% Using epilepsy medication —

— 62.2 68.4% Working or full time education

a n=272.b n=37.

cluded for lack of comprehension during the in-terview. Most participating patients had well-con-trolled epilepsy, but were still using antiepilepticmedication (Table 1). Mean age was 44 years(S.D.=11.7). Forty-two percent of the subjectswere male and 58 percent were female. Among thepatients completing the study there were morewomen, and they were younger, used less medica-tion and had fewer seizures during the last yearthan the respondents to the questionnaire. Abreakdown of the respondents at the variousstages of the study indicates the representativenessof the respondents interviewed (Table 1), howeverthe differences were not statistically significant.

The resulting preference values differed consid-erably between the measures, both with regard tocentral tendency and dispersion (Table 2). TheEQ-VAS scale gave lowest values (median 0.75),the preference based methods TTO and SG thehighest (median 0.98 and 0.99, respectively), while15D gave values in between (median 0.90).

Fig. 1a–f show pairwise comparisons betweenthe different value measures, plotting the differ-ence in score between the two measures againstthe mean of each measurement pair, also indicat-

ing the limits of agreement (Altman and Bland,1983; Bland and Altman, 1986). The figures showthat the best agreement was for 15D with SG andTTO, and for SG with TTO. Limits of agreementbetween the different measures are shown numeri-cally for each comparison pair as the mean differ-ence of the scores (mean bias)92 S.D. (Table 3).

There was a good association between the EQ-VAS and the 15D, and a moderate associationbetween SG and TTO. Other associations betweenthe measures were poor and not statistically sig-nificant (Table 4). Associations between the pref-erence measures and indicators of disease activity(having seizures during the last year, and epilepsymedication use) were low and not statisticallysignificant (Table 5).

4. Discussion

The different instruments for measuring utilitygave different results in this sample of well con-trolled epilepsy patients. This is of crucial impor-tance when using such preference values incost-utility studies and as a support for decision-making.

Preference scores were smallest for the ratingscale EQ-VAS, with 15D, TTO and SG in increas-ing order of magnitude. These findings are consis-tent with patients’ preference weights in otherstudies: SG\TTO\RS (Bombardier et al.,1982; Lalonde et al., 1995), in contrast to a studyof end-stage renal disease where patients scoredhigher on a visual analog scale than with TTO(Churchill et al., 1987). Scores on the 15D were

Table 2Summary statistics of scores using different value measures

Range nMedianMeasure S.D.Mean

0.30–1.00 57EuroQol VAS 0.750.74 0.17550.63–1.000.0915D 0.900.89

0.50–1.00 57TTO 0.92 0.98 0.11570.50–1.000.93 0.11SG 0.99


Fig. 1. Pairwise comparisons between the value measures, with differences in scores between the two measures versus the mean ofthe same pair of measurements. The solid horizontal line represents mean difference, the two broken lines represent mean92 S.D.,defining limits of agreement: (a) 15D and EuroQol VAS; (b) 15D and time trade-off; (c) 15D and standard gamble; (d) EuroQolVAS and standard gamble; (e) EuroQol VAS and time trade-off; (f) standard gamble and time trade-off.

between RS and TTO. The preferences elicited inthis study are individual preferences of patientsscoring their own conditions, and cannot fairly becompared with studies of social preferences typi-cally generated from healthy people scoring theo-

retical conditions. The cardinal preference weightscan be compared with weights in a study of asthmapatients, with mean values of 0.69 for RS and 0.87for SG at baseline (Rutten-van et al., 1995), valuesfor patients with epilepsy were not available.


Fig. 1. (Continued)

Agreement was better between 15D and thepreference based measures SG and TTO, thanbetween 15D and the RS. The agreement was alsobetter between SG and TTO, than for each ofthem with the rating scale.

The internal correlations between the prefer-ence measures divided the measures into two

groups: (1) SG and TTO with a moderate correla-tion, and (2) EQ-VAS and 15D with a highcorrelation, the latter close to the correlation re-ported in a Finnish study (Sintonen, 1994b). Cor-relations between the preference measures andproxies for disease activity generally were poor,although best for EQ-VAS and 15D, and weaker


Fig. 1. (Continued)

for TTO and SG. This is in accord with theconclusions of others. Revicki et al. in a reviewreported that psychometric health status scales arepoorly to moderately correlated with SG and TTOscores, and that rating scales and the multiat-tribute utility measures are more closely correlatedwith various individual health status measures(Revicki and Kaplan, 1993). The lack of associa-tion between assessment methods and the effect ofthis variability on the cost-effectiveness of hemodi-alysis has been demonstrated by Hornberger et al.(1992).

That the techniques give different results reflectthe difference in the questions and the cognitive

tasks involved, also reflecting differences in fram-ing effects, contexts, anchoring points, duration ofconditions, time preferences, and attitude towardrisk. The patients in this study were risk averseand unwilling to gamble, and also unwilling totrade life-years over the 10-year period they facedin the TTO interview.

What does this mean for the validity of themeasures? A measure is valid if it accuratelyreflects the concept or phenomenon it claims tomeasure. Two approaches to assessing validity canbe used (Torrance, 1986): (1) Following the ax-ioms of von Neumann–Morgenstern SG gives avalid health state utility score per definition. In

Table 3Agreement between the value measures

nLimits of agreement (mean92 S.D.)Mean difference

UpperLower

0.14 0.4015D-EuroQoL VAS −0.12 550.2115D-SG −0.05 55−0.30

550.21−0.2915D-TTO −0.040.17EuroQOL VAS-SG −0.19 57−0.560.19EuroQOL VAS-TTO −0.18 57−0.55

0.01 0.25−0.22SG-TTO 57


Table 4Spearman’s rank correlation coefficients between the value measures

15D (n=55) EuroQol VAS (n=57) Time trade-off (n=57)

EuroQol VAS 0. 62*0.190.19Time trade-off

0.18 0.26 0.48Standard gamble

* PB0.001.

Table 5Spearman’s rank correlation coefficients between value measures and indicators of disease activity

15D (n=53) EuroQol VAS (n=55) Time trade-off (n=55) Standard gamble (n=55)

Had no seizures last year −0.190.16 0.080.28Used no medication last year −0.04−0.010.190.10 (n=54)

All values ns (P\0.05).

this case TTO shows moderate correlation withSG and is relatively valid, while RS and themultiattribute model 15D are not. (2) To showvalidity the preference measures should measurethe overall quality of life, and should correlatewell with other measures of HRQL or measuresof symptoms or function. Given this approach,none of the measures perform well, when usingmedication use and seizures last year as markerfor disease activity. These findings could becaused by a lack of this marker to representhealth related quality of life, i.e. that they aredifferent concepts, or could mean that these mea-sures are too crude and lack sensitivity whenused in a population with well controlledepilepsy patients.

It has been argued that a RS elicits a valuefunction, while the SG results in a utility func-tion that captures risk attitudes, which impliesthat if a decision involves uncertainty the utilityfunction should be used (Bowe, 1995). However,there is no gold standard in this field. Methodsfor preference measurements are value based,and hence, the weights used in cost-utility studiescan always be discussed and will remain contro-versial.

It is important to be aware that these mea-sures incorporate different conceptual models,explaining why they yield different results. How-ever, in the literature the resulting preference

measures are by many authors used inter-changably, or the result of one cost-utility studymight be compared with results of studies usingconceptually completely different preference mea-sures. The preference/utility scales are con-structed for use in cost-effectiveness analyses,and for assistance in decisions about resourceallocation. The desired properties of such scaleswould be that they have at least interval proper-ties, are reliable, valid and responsive for theoutcome to be assessed in a study or for thecomparisons to be undertaken. Preference mea-sures are conceptually attractive, but raise manypractical problems, e.g. how to aggregate individ-uals’ values, and whose values to use.

In conclusion, the resulting utility weights dif-fered considerably between the measures, bothwith regard to central tendency and dispersion.These preference instruments measure differ-ent aspects of health-related quality of life andthus yield different results. Caution should betaken when interpreting cost-utility studies, asresults will depend on the choice of utility instru-ment.

Acknowledgements

Thanks to Erik Nord for guidance throughoutthis study.


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quality of life in epilepsy: comparison of four preference measures

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