Introduction
Epilepsy is a heterogeneous group of central nervous system disorders characterized by unpredictable recurrent seizures [
1]. Epilepsy can significantly affect patients’ health-related quality of life (HRQoL), including their mental health, and role and social functioning [
2]. Seizure control can be achieved with antiepileptic drug (AED) treatment [
3,
4], but up to 30% of patients still have uncontrolled seizures. HRQoL can also be affected by AED-associated side effects even in controlled patients. HRQoL measures used in epilepsy trials should capture these varied effects.
Reimbursement agencies such as the National Institute for Health and Care Excellence (NICE) in the UK require effectiveness to be measured in terms of quality-adjusted life years (QALYs) [
5], which combines HRQoL with length of life. The quality of life component of QALYs can be estimated using generic preference-based measures, such as the EQ-5D-3L [
6] and the Short Form 6 dimensions (SF-6D) [
7] which are recommended by some reimbursement agencies such as NICE. EQ-5D-3L is the most widely used generic preference-based measure. Generic preference-based measures enable comparison across different diseases and populations and, as such, ensure a consistent basis for the assessment of cost-effectiveness. Alternatively, disease-specific preference-based measures can be used to generate QALYs, though these are not comparable with QALY estimates derived from other instruments. Where preference-based measures have not been used in clinical trials, some reimbursement agencies such as NICE allow mapping of data from disease-specific
non-preference-based measures, such as the epilepsy-specific Quality of Life in Epilepsy Inventory (QOLIE-31P) [
8], to generic preference-based measures, for use in cost-effectiveness analysis [
5].
In order to generate robust cost-effectiveness data, generic preference-based measures need to be validated in the population of interest; this also applies when mapping is to be used [
9]. However, few mapping studies report information on validity of the generic preference-based measures used in their analysis. There is evidence that generic preference-based measures do not adequately cover dimensions of HRQoL affected by certain diseases, including epilepsy, and as such are not usable. This coverage issue was found in a study (
n = 140) of patients evaluated for epilepsy surgery [
10]. Therefore, the primary aim of this study was to examine the validity and responsiveness of the EQ-5D-3L in a large sample of patients with epilepsy who had uncontrolled focal (partial-onset) seizures and who were taking part in trials of an approved antiepileptic drug. A secondary aim was to test mapping from the QOLIE-31P to the EQ-5D-3L if the two measures were shown to have sufficient overlap based on the psychometric performance of EQ-5D-3L, although there are no specific guidelines in the literature regarding what is acceptable for mapping purposes [
9].
Results
Overall, 1095 patients had both EQ-5D-3L and QOLIE-31P data available. Across the three studies, mean age of the population was 36.8–39.2 years, there were marginally more males than females, and the majority were Caucasian (Table
1). Median focal seizure count/28 days at baseline was 1.91–2.48 and the mean number of prior AEDs was ≈3 across the three studies. Pooled mean (SD) EQ-5D-3L utility score was 0.76 (0.23) at baseline and 0.78 (0.23) at follow-up, while pooled mean (SD) QOLIE-31P total scores were 55.6 (16.0) and 60 (16.1) at baseline and follow-up, respectively. QOLIE-31P subscale scores are given in Online Supplement 1.
Table 1
Baseline demographics, epilepsy characteristics, and baseline and follow-up health-related quality of life scores of patients included in the analysis
Age [mean (SD)] | 37.5 (13.0) | 39.2 (12.1) | 36.8 (11.5) | 37.8 (12.2) |
Male, [n (%)] | 202 (56.9) | 176 (50.7) | 200 (50.9) | 578 (52.8) |
Race [n (%)] | | | | |
Caucasian | 280 (78.9) | 248 (71.5) | 232 (59.0) | 760 (69.4) |
Other | 75 (21.2) | 99 (28.5) | 161 (41.0) | 335 (30.6) |
Baseline focal seizure frequency/28 days | | | | |
Mean (SD) | 4.01 (6.5) | 6.37 (12.3) | 5.06 (12.4) | 5.13 (3.04) |
Median (IQR) | 1.91 (2.3) | 2.48 (4.0) | 2.21 (3.2) | 2.14 (3.04) |
Number of prior AEDs [mean (SD)] | 3.5 (2.43) | 3.3 (2.53) | 3.1 (2.18) | 3.3 (2.38) |
EQ-5D-3L [mean (SD)] | | | | |
Baseline | 0.756 (0.234) | 0.762 (0.226) | 0.758 (0.234) | 0.759 (0.232) |
Follow-up | 0.770 (0.241) | 0.791 (0.221) | 0.771 (0.229) | 0.777 (0.230) |
QOLIE-31P total score [mean (SD)] | | | | |
Baseline | 56.3 (16.3) | 54.8 (17.1) | 55.6 (14.7) | 55.6 (16.0) |
Follow-up | 59.8 (15.8) | 59.6 (17.6) | 60.2 (15.0) | 59.9 (16.1) |
Convergent validity
No strong correlations between EQ-5D-3L dimensions and QOLIE-31P subscales were noted. At baseline, the EQ-5D-3L usual activities dimension had weak-to-moderate correlations with QOLIE-31P daily activities/social functioning (
ρ = −0.319), emotional well-being (
ρ = −0.316), energy/fatigue (
ρ = −0.290), and cognitive function subscales (
ρ = −0.286) (Table
2). EQ-5D-3L anxiety/depression had moderate correlations with emotional well-being (
ρ = −0.455) and energy/fatigue (
ρ = −0.334), but the correlation with the seizure worry subscale was lower than expected (
ρ = −0.274). There were moderate correlations between QOLIE-31P overall quality of life with EQ-5D-3L usual activities (
ρ = −0.327) and anxiety/depression (
ρ = −0.397). Most other correlations were weak (
ρ < –0.3), with little evidence of association between mobility, self-care, and pain/discomfort and the QOLIE-31P subscales. There were no associations between EQ-5D-3L or QOLIE-31P dimensions/subscales and seizure frequency.
Table 2
Convergent validity of EQ-5D-3L and QOLIE-31P using Spearman’s rank correlation at baseline (pooled data)
QOLIE-31P subscales (n = 1076) |
Energy/fatigue | −0.188 | −0.115 | −0.290 | −0.232 | −0.334 | 0.363 |
Emotional well-being | −0.197 | −0.132 | −0.316 | −0.255 | −0.455 | 0.449 |
Daily activities/social functioning | −0.163 | −0.151 | −0.319 | −0.253 | −0.234 | 0.345 |
Cognitive functioning | −0.166 | −0.177 | −0.286 | −0.238 | −0.288 | 0.348 |
Medication effects | −0.156 | −0.074 | −0.261 | −0.229 | −0.205 | 0.285 |
Seizure worry | −0.106 | −0.102 | −0.261 | −0.173 | −0.274 | 0.280 |
Overall QoL | −0.233 | −0.127 | −0.327 | −0.274 | −0.397 | 0.424 |
QOLIE-31P total score | −0.234 | −0.197 | −0.413 | −0.328 | −0.427 | 0.496 |
Focal seizure frequency (n = 1072) | 0.040 ns | 0.052 ns | 0.065 | 0.026 ns | −0.032 ns | −0.031 ns |
The EQ-5D-3L utility score had moderate correlations with all QOLIE-31P subscales (ρ = 0.345–0.496) except medication effects and seizure worry, which were weakly correlated (ρ = 0.285 and 0.280, respectively). There was no association between EQ-5D-3L utility score and seizure frequency (ρ = −0.031).
Known group validity
Mean EQ-5D-3L utility scores varied with QOLIE-31P groups, and differences across the groups were statistically significant (
p < 0.001), with mainly small ES between groups (Table
3). The presence of secondarily generalized focal seizures (severe seizures) was associated with statistically significant lower EQ-5D-3L utility scores and QOLIE-31P scores (both
p < 0.001). ES were small for both EQ-5D-3L (−0.21) and QOLIE-31P (−0.32). Few patients reported seizures on the day they completed the instrument (
n = 82). There were no statistically significant differences between EQ-5D-3L and QOLIE-31P scores for patients who reported seizures versus those who did not (Table
3). Further assessment indicated there was no monotonic relationship between the number of seizures that patients reported and either measure. There was some evidence that patients with ≥5 prior AEDs had lower health status based on a statistically significant difference between EQ-5D-3L utility scores (
p = 0.002). ES for this difference were small (−0.22), but were larger than the equivalent ES for QOLIE-31P total score (−0.11).
Table 3
Known group validity of EQ-5D-3L and QOLIE-31P at baseline (pooled data)
QOLIE-31P total score groups | 0–40 | 195 | 0.575 (0.28) | |
F
4,1090 = 73.8
p < 0.001 | | | |
41–50 | 210 | 0.697 (0.22) | 0.53 | | – | – | |
51–60 | 280 | 0.779 (0.20) | 0.36 | | – | – | |
61–70 | 208 | 0.832 (0.16) | 0.23 | | – | – | |
71–100 | 202 | 0.897 (0.15) | 0.28 | | – | – | |
Secondarily generalized focal seizures at baseline | No | 734 | 0.775 (0.22) | |
t
1089 = 3.3
p < 0.001 | 57.2 (15.4) | |
t
1089 = 5.0
p < 0.001 |
| Yes | 357 | 0.726 (0.26) | −0.21 | | 52.1 (16.8) | −0.32 | |
Any seizure on day of completion of PRO | No | 968 | 0.759 (0.23) | |
t
1048 = −0.98
p = 0.327 | 55.5 (16.1) | |
t
1048 = −1.5
p = 0.138 |
| Yes | 82 | 0.785 (0.24) | 0.11 | | 58.3 (15.7) | 0.17 | |
Baseline focal seizure frequency/week (log) | <1 | 418 | 0.765 (0.23) | |
F
2,1088 = 1.16
p = 0.314 | 56.3 (14.8) | |
F
2,1088 = 3.0
p = 0.050 |
| 1–<2 | 485 | 0.748 (0.24) | −0.07 | | 54.2 (17.1) | –0.13 | |
| ≥2 | 188 | 0.775 (0.21) | 0.12 | | 57.1 (15.8) | 0.18 | |
Number of prior AEDs in the past 5 years | 0–1 | 331 | 0.780 (0.22) | |
F
2,1092 = 6.12
p = 0.002 | 56.3 (16.1) | |
F
2,1092 = 1.6
p = 0.202 |
| 2–4 | 479 | 0.768 (0.22) | −0.05 | | 55.9 (15.4) | −0.02 | |
| ≥5 | 285 | 0.718 (0.25) | −0.22 | | 54.1 (17.0) | −0.11 | |
Across the three studies, a number of patients had full health based on the EQ-5D-3L utility scores (24.9%). However, the majority of these patients (>84%) reported less than full health in QOLIE-31P total scores and subscales (Table
4).
Table 4
QOLIE-31P scores for those with EQ-5D-3L utility score = 1 (pooled data)
QOLIE-31P subscales |
Energy/fatigue | 273 | 61.6 | 17.7 | 65.0 | 10.0 | 100 | 98.9 |
Emotional well-being | 273 | 73.6 | 16.0 | 76.0 | 24.0 | 100 | 95.6 |
Daily activities/social functioning | 273 | 67.0 | 22.5 | 66.0 | 0.0 | 100 | 86.4 |
Cognitive functioning | 273 | 66.2 | 24.0 | 67.8 | 0.0 | 100 | 89.0 |
Medication effects | 273 | 67.4 | 23.6 | 69.4 | 0.0 | 100 | 84.2 |
Seizure worry | 273 | 54.6 | 27.6 | 59.3 | 0.0 | 100 | 94.5 |
Overall QoL | 273 | 68.9 | 15.3 | 72.5 | 22.5 | 100 | 96.0 |
QOLIE total score | 273 | 66.4 | 14.1 | 65.8 | 31.1 | 97.6 | 100 |
Responsiveness
There was little evidence at baseline of a large proportion of respondents reporting the lowest levels in EQ-5D-3L and QOLIE-31P dimensions/subscales. However, all five EQ-5D-3L dimensions had a large proportion reporting no problems (mobility: 83, 85, 81; self-care: 92, 93, 90%; usual activities: 64, 62, 62%; pain/discomfort: 52, 49, 52%; anxiety/depression: 43, 48, 43%) in the N01252, N01253 and N01254 studies, respectively. This is consistent with the fact that mobility and self-care were expected to be less problematic in this population. The QOLIE-31P subscales did not have equivalent large proportions reporting no problems except for medication effects in trial N01254 which was at 11%.
Mean changes in EQ-5D-3L dimensions were not statistically significant with small SRMs (SRM ≈ 0.1 in all three trials). Mean changes in QOLIE-31P subscales were also small, but they were statistically significant (p < 0.05) and larger than EQ-5D-3L changes (SRM = 0.1–0.4)) except for medication effects (ES ≤ 0.1). The largest SRM in the QOLIE-31P was in seizure worry in studies N01252 and N01254 (SRM = 0.3 and 0.4, respectively). This indicated minor improvements in health status and HRQoL over time based on the disease-specific measure.
For responsiveness based on ≥50% reduction in focal seizures, changes in EQ-5D-3L utility scores were higher in responders vs non-responders in studies N01253 (ES = 0.41) and N01254 (ES = 0.11), but the difference was statistically significant only in study N01253 (
p = 0.002) (Table
5). In contrast, QOLIE-31P total score change was statistically significantly higher in responders in all three studies.
Table 5
Responsiveness based on response to treatment status and clinician/patient evaluation of change at follow-up
N01252 |
Response to treatment (≥50% reduction in focal seizure frequency) | Non-responders | 226 | 0.018 (0.24) | |
t
315 = 0.07
p = 0.947 | 2.01 (12.4) | |
t
315 = −3.7
p < 0.001 |
| Responders | 91 | 0.016 (0.21) | −0.01 | | 7.94 (14.5) | 0.45 | |
PGES | 1–4 | 96 | 0.016 (0.22) | |
F
3,308 = 0.09
p = 0.966 | −1.07 (9.6) | |
F
3,308 = 9.25
p < 0.001 |
5 | 87 | 0.027 (0.21) | 0.05 | | 3.67 (10.9) | 0.36 | |
6 | 73 | 0.009 (0.28) | −0.08 | | 6.58 (14.6) | 0.22 | |
7 | 56 | 0.020 (0.18) | 0.05 | | 9.13 (16.3) | 0.19 | |
CGES | 1–4 | 108 | 0.051 (0.21) | |
F
3,312 = 1.7
p = 0.167 | 0.61 (12.1) | |
F
3,312 = 5.6
p < 0.001 |
5 | 95 | 0.022 (0.22) | −0.13 | | 3.57 (11.2) | 0.23 | |
6 | 76 | −0.023 (0.25) | −0.20 | | 5.96 (13.4) | 0.18 | |
7 | 37 | 0.005 (0.20) | 0.12 | | 9.73 (16.7) | 0.29 | |
N01253 | | | | | | | | |
Response (≥50% reduction in focal seizure frequency) | Non-responders | 234 | 0.000 (0.25) | |
t
303 = −3.1
p = 0.002 | 3.46 (12.6) | |
t
303 = −3.2
p = 0.001 |
| Responders | 71 | 0.103 (0.25) | 0.41 | | 9.71 (18.7) | 0.43 | |
PGES | 1–4 | 97 | −0.003 (0.21) | | F
3,297 = 1.9 p = 0.123 | −0.76 (11.5) | | F
3,297 = 9.2 p < 0.001 |
5 | 68 | 0.005 (0.33) | 0.03 | | 5.51 (11.6) | 0.44 | |
6 | 77 | 0.020 (0.19) | 0.06 | | 6.99 (13.3) | 0.10 | |
7 | 59 | 0.092 (0.28) | 0.28 | | 10.34 (19.1) | 0.23 | |
CGES | 1–4 | 113 | −0.004 (0.21) | |
F
3,297 = 2.8 p = 0.043 | 0.46 (12.9) | |
F
3,297 = 9.9
p < 0.001 |
5 | 83 | −0.004 (0.30) | 0.00 | | 4.91 (11.7) | 0.31 | |
6 | 65 | 0.054 (0.22) | 0.23 | | 6.93 (14.0) | 0.14 | |
7 | 40 | 0.111 (0.28) | 0.23 | | 13.64 (18.4) | 0.47 | |
N01254 | | | | | | | | |
Response (≥50% reduction in focal seizure frequency) | Non-responders | 244 | 0.010 (0.23) | |
t
342 = −0.92
p = 0.356 | 3.16 (12.0) | |
t
342 = −4.2
p < 0.001 |
| Responders | 100 | 0.036 (0.26) | 0.11 | | 9.38 (14.0) | 0.48 | |
PGES | 1–4 | 111 | −0.021 (0.21) | |
F
3,335 = 3.6
p = 0.015 | −0.20 (10.6) | |
F
3,335 = 19.3
p < 0.001 |
5 | 92 | 0.048 (0.23) | 0.28 | | 3.29 (12.0) | 0.28 | |
6 | 88 | −0.011 (0.24) | −0.24 | | 8.33 (12.0) | 0.40 | |
7 | 48 | 0.096 (0.33) | 0.44 | | 13.85 (13.3) | 0.43 | |
CGES | 1–4 | 128 | −0.016 (0.20) | |
F
3,341 = 1.9
p = 0.138 | −1.12 (10.6) | |
F
3,341 = 21.7
p < 0.001 |
5 | 86 | 0.010 (0.27) | 0.11 | | 5.12 (13.6) | 0.48 | |
6 | 91 | 0.059 (0.27) | 0.20 | | 11.36 (11.9) | 0.48 | |
7 | 40 | 0.039 (0.23) | −0.08 | | 9.17 (11.8) | −0.17 | |
Assessment of response based on PGES and CGES showed mostly no statistically significant differences between groups with small ES in EQ-5D-3L utility scores (Table
5). EQ-5D-3L utility scores differed based on CGES groups for study N01253 and for PGES groups for study N01254, but the latter was not a monotonic relationship. In contrast, QOLIE-31P total score change had a linear association with the statistically significant improvements reported in PGES and CGES (
p < 0.001), except for CGES in study N01254.
Discussion
To assess the psychometric properties of the EQ-5D-3L in patients with uncontrolled focal seizures, we used data from three large Phase III, randomized, placebo-controlled studies of brivaracetam, which is approved as adjunctive therapy for focal seizures in adults with epilepsy. Epilepsy-specific measures including the QOLIE-31P were used as proxies for severity in convergent and known group analysis. The responsiveness of EQ-5D-3L and QOLIE-31P was assessed based on their ability to detect differences in treatment outcome groups.
Despite differences in the focus of the measures (generic vs disease-specific), some association between measures was expected. Correlation analyses confirmed some association between similar dimensions/subscales of each instrument, although generally it was weak. Contrary to expectations, the EQ-5D-3L usual activities and the QOLIE-31P energy/fatigue and daily activities/social functioning subscales were only weakly correlated. Similarly, only a weak correlation was observed between EQ-5D-3L anxiety/depression and QOLIE-31P seizure worry. An earlier study reported that some patients with epilepsy had difficulty answering the anxiety/depression dimension of the EQ-5D-3L as they did not consider themselves to be depressed [
18]. Therefore, patients may not have considered seizure worry when completing the more general anxiety/depression questions of the EQ-5D-3L. Dimensions relating to mobility and self-care had little association with QOLIE-31P subscales, and there were mainly weak correlations between the pain dimension and QOLIE-31P subscales. This might be expected, as these aspects of HRQoL may not be impaired in patients with epilepsy. EQ-5D-3L utility scores had weak-to-moderate correlations with the QOLIE-31P subscales. Baseline seizure frequency was neither correlated with the EQ-5D-3L dimensions or utility scores, nor with QOLIE-31P subscale scores.
The EQ-5D-3L was able to reflect differences in groups based on the QOLIE-31P total score. Neither EQ-5D-3L nor QOLIE-31P scores reflected differences in baseline number of seizures. Poor association between seizure frequency and HRQoL may be due to the severity or timing of seizures experienced. The episodic nature of epilepsy means that seizure-free periods can be associated with good HRQoL which decreases following a seizure [
2]. Furthermore, EQ-5D-3L asks patients about their health on the day of assessment, whereas QOLIE-31P covers the past 4 weeks and attempts to get a reading of ‘average’ HRQoL. The presence of seizures on the day of questionnaire completion was not negatively associated with HRQoL in either measure; however, it was unknown whether seizures occurred before or after questionnaire completion. The lack of association between seizure frequency and HRQoL may also be because large gains in HRQoL are only achieved with seizure freedom [
19], and this was achieved by relatively few patients. However, there was evidence to suggest that seizure severity may impact on HRQoL; patients with secondarily generalized seizures, a proxy for more severe seizures, had lower health status/HRQoL in both EQ-5D-3L and QOLIE-31P, although the ES for EQ-5D-3L were smaller. This observation is consistent with several previous studies which found negative associations between seizure severity and HRQoL [
20].
EQ-5D-3L had large proportions reporting no problems in the dimensions particularly in the mobility and self-care dimensions (80–90%), which was not unexpected as patients were not expected to have problems ‘walking about’ or ‘washing and dressing’ themselves. In terms of the overall EQ-5D-3L utility score, 24.9% were in full health. The QOLIE-31P did not have comparable proportions without problems. The vast majority of respondents who reported a score of 1 in EQ-5D-3L reported scores lower than 100 (best functioning) in the QOLIE-31P subscales. This indicated that EQ-5D-3L dimensions were not relevant or sensitive enough to assess the impact of epilepsy-specific symptoms in this population. Where concepts do overlap between the measures, QOLIE-31P has more items and so may be able to capture these effects better than the single items of the EQ-5D-3L. Langfitt et al. [
10] found that the SF-6D, which also has more items per dimension and similar dimensions (social, role functioning, energy, and emotional well-being), performed better than the EQ-5D-3L.
In terms of responsiveness, the EQ-5D-3L and QOLIE-31P dimension/subscale scores all showed positive change over the trial period, but only QOLIE-31P subscales had statistically significant changes. The SRMs were smaller for EQ-5D-3L utility score than for QOLIE-31P (0.1 vs. 0.1–0.4), indicating that small changes in the population were captured by some of the QOLIE-31P subscales but not by the EQ-5D-3L dimensions. Responsiveness based on 50% seizure frequency reduction indicated small ES in EQ-5D-3L utility and QOLIE-31P total scores (−0.01 vs. 0.45). The efficacy gain in terms of seizure frequency in this population is more modest than in less refractory patients and, as noted, few patients achieve seizure freedom; as such, it may be more difficult to show improvement in HRQoL [
19,
21]. In addition, meaningful changes in seizure frequency, coping and lifestyle as a consequence of treatment efficacy may not be reflected in HRQoL outcomes in studies of short duration [
22]. In contrast to the QOLIE-31P, change in EQ-5D-3L utility scores was largely not associated with patient and clinician evaluations of improvement. These results suggest that even if the outcome achieved in this population was modest, the QOLIE-31P detected some improvements that the EQ-5D-3L was not sensitive enough to reflect.
The psychometric analyses indicated that QOLIE-31P would be poor predictors of EQ-5D-3L due to the lack of sufficient overlap between measures evidenced by lower sensitivity of the EQ-5D-3L. This highlights the importance of assessing that generic preference-based measures are appropriate in the population of interest in terms of psychometric properties before carrying out mapping analysis. However, the applicability of the generic measure in the patient population concerned is not always reported in mapping studies.
Overall, the results suggest that although there is some association between the EQ-5D-3L and the QOLIE-31P, this is not sufficient to capture changes over time to the same degree, as the latter measure includes epilepsy-specific concepts such as seizure worry. The existing disease-specific measures, such as the QOLIE-31P, could be converted into a preference-based measure, which could then be applied to existing datasets without utility values. Alternatively, other more broad generic HRQoL measures could be used in mapping studies (e.g. the SF-36 and thus the SF-6D). Finally, utility values could be generated from an existing epilepsy-specific QALY measure, such as the Quality of Life in Newly Diagnosed Epilepsy Instrument 6 dimensions (NEWQOL-6D), which is derived from the NEWQOL [
23,
24]. Values for NEWQOL-6D health states were found to be similar in patients and the general population, suggesting that using general population utility weights to estimate QALYs is appropriate and generally represents patient preferences [
24]. If the measure proves to be psychometrically valid in patients with uncontrolled focal seizures, the NEWQOL-6D could be used as an alternative to generate QALYs [
24].
A number of studies have assessed the performance of EQ-5D-3L in populations with epilepsy [
10,
25,
26]. Overall, results support the findings of this study in that some EQ-5D-3L dimensions may be relevant to a population with uncontrolled focal seizures. However, outcomes such as seizure control may not be as closely associated with the EQ-5D-3L. Differences in levels of seizure severity and interventions make it difficult to compare these studies directly. One study, which assessed the relationship between seizure frequency and preference-based HRQoL, found that in patients with recurrent seizures, seizure frequency was not monotonically related to preference-based HRQoL, with substantial overlap across different seizure frequency categories, thereby mirroring some of the findings in this study [
27].
The analyses presented in this study provide important information on the performance of EQ-5D-3L in a patient population with uncontrolled focal seizures; however, there are a number of limitations. The studies used in the analysis were designed to assess the efficacy, safety and tolerability of adjunctive brivaracetam; assessment of HRQoL was an exploratory objective, and this may have impacted on the analysis of HRQoL. The study populations were based on clinical (e.g. seizure frequency) rather than HRQoL criteria; therefore, their HRQoL data may not be applicable to the overall population of patients with uncontrolled focal seizures. Furthermore, the nature of the instruments themselves may affect results as the QOLIE-31P covers the previous 4 weeks, whereas EQ-5D-3L focuses on a single day, which may exclude typical HRQoL effects that occur over a period of time.
In summary, while some EQ-5D-3L dimensions overlapped with similar concepts in the disease-specific QOLIE-31P, the content of the measure was unable to capture self-reported epilepsy-specific concerns or to reflect change over time. Given the lack of correlation and joint responsiveness between the measures, using the EQ-5D-3L for cost-effectiveness analysis including from mapping is not recommended. A disease-specific preference-based measure may offer an alternative.
Acknowledgements
The authors thank the patients and their caregivers, in addition to the investigators and their teams who contributed to studies N01252, N01253 and N01254 on which these analyses are based. The studies were sponsored by UCB Pharma; UCB Pharma was involved in the design and conduct of the studies, collection, management, and analysis of the data, and preparation and review of the manuscript. The authors acknowledge Rachel Bell, PhD, and Sally Cotterill, PhD (QXV Communications, an Ashfield Business, Macclesfield, UK), for writing assistance, which was funded by UCB Pharma.