Which health-related quality of life score? A comparison of alternative utility measures in patients with Type 2 diabetes in the ADVANCE trial

All patients included in this study were participating in the ADVANCE (Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation) study. ADVANCE is a randomised 2 × 2 factorial trial in 11,140 normotensive patients with Type 2 diabetes comparing (i) intensive, gliclazide-based therapy or regular, guideline-based glucose control therapy in patients with Type 2 diabetes, and (ii) intensive blood pressure lowering based on a perindopril-indapamide combination or matching placebo [11]. The intensive gliclazide-based regimen aims to reduce haemoglobin A1C to 6.5% or lower (compared with haemoglobin A1C targets of 7–8% suggested by most regional guidelines) [5].

Two health-related quality of life questionnaires are being used in the ADVANCE trial: the EQ-5D (EuroQol 5-Dimensions) and the SF-36v2 (Short Form 36 version 2). The EQ-5D has five questions – on mobility, personal care, usual activities, pain/discomfort, and anxiety/depression – each encompassing the range of values 1 (= no problems), 2 (= some problems), 3 (= severe problems).

The SF-36v2 is a multipurpose short-form survey with 36 questions that measure eight health attributes using multi-item scales containing 2 to 10 items each. These attributes are: (1) physical functioning, (2) role limitations due to physical health problems, (3) bodily pain, (4) general health, (5) vitality (energy/fatigue), (6) social functioning, (7) role limitations due to emotional problems, and (8) mental health (psychological distress and psychological well-being). The SF-12 and SF-6D are health-related quality of life instruments that use a subset of these items.

The EQ-5D was administered to all patients in the ADVANCE trial, from the 20 participating countries. The SF-36v2 questionnaire was administered to the subgroup of Australian patients only. Both questionnaires were administered at baseline and 2 years after randomization to the study. As the analysis involves a comparison of the EQ-5D with the SF-36 we have restricted the analysis to the Australian patients.

We undertook a literature search to identify studies that reported algorithms to convert generic health-related quality of life instruments into utility values which could potentially be used in valuing outcomes in economic evaluations. We identified seven studies [10, 12‐17] that used items from the SF-36, and two [8, 9] using the EQ-5D, that were published prior to December 2005. The main features of these algorithms (which are ranked by the number of items they require) are summarized in Table 1. The algorithms can be grouped into four classes: (i) those based on the EQ-5D [8, 9]; (ii) those algorithms using fewer items than the SF-12 (i.e. SF-6D) [12, 13] (iii) those that use the SF-12 [10, 14‐16]; (iv) those that use all items of the SF-36 [17]. All studies except Lawrence [14] and Franks [15, 16] involve utilities derived from studies involving primary direct assessment of utilities e.g., using standard gamble or time trade-off methods.

We report summary statistics for patients with a history of any of seven diabetes-related complications: stroke or transient ischemic attack (TIA), peripheral revascularization and/or amputation, hospital admission for unstable angina, myocardial infarction, coronary artery bypass graft, and currently treated for hypertension. This history was reported on the baseline form. The mean (sd) utility for those with the complication, without the complication, and without any of the complications, was calculated for all nine algorithms. Confidence intervals were calculated for differences in the mean level of utility of those experiencing each type of complication with those not experiencing that complication in the Australian ADVANCE population, and displayed graphically. To compare how the mean difference in utility varied between algorithms we ranked the severity (using mean deficit in utility values) of the seven complications of diabetes for each utility measure, then used Spearman's rank correlation coefficient to assess the degree of consistency in ranking. We also graphed the average difference in mean utility for these complications by the four classes of algorithm.

To estimate the within-person variability, we used the measures in the subgroup of patients who had had no major adverse event between the baseline and two-year qol measurements. This was used to define a signal-to-noise ratio as the difference in mean utility between those who had a major event and those who did not, divided by the standard deviation of the utility measure in those who did not have a major event. Furthermore, linear regression models were used to assess the statistical significance of the presence of a major event on change in 2-year utility score, adjusted for baseline score.

Characteristics of the ADVANCE study population have been published [11], and included a total of 978 Australian patients randomized. This Australian cohort was 71% male, had a mean age of 67 (range = 55–86) years at registration, and a baseline mean hemoglobin A1c (HbA1c) of 7.2 (sd = 1.2). The average duration of diabetes was 7.2 years (sd = 6.1 years). The two health-related quality of life measurement instruments, the SF-36v2 and the EQ-5D, were administered at baseline to all 978 patients, with 975 responses (99.6%) to the EQ-5D, and 978 responses (100%) to the SF-36v2.

Table 1 includes information on the range of utility values in our study population. The Weighted Health Index (WHI) utility measure (calculated from the EQ-5D) was -0.18 to 1.0 when calculated using the Dolan algorithm, and 0.20 to 1 for the Shaw algorithm. The range of the Brazier index, based on the SF-6D, was 0.35 to 1.0, and the Lundberg 0.43 to 0.98.

Table 2 shows the mean (sd) utility scores for each of the utility measures broken down by presence or absence of the seven individual diabetes-related complications at baseline. It also includes these same measurements for the subset of patients (21.5%) who had no history of any of these complications at baseline. There was moderate variation in the average utility values. For example, for the 10.5% of patients who had previously experienced a stroke or TIA, the mean utility ranged from approximately 0.650 (for Fryback and Franks SF-12-MEPS algorithms), up to 0.783 for the Shaw (EQ-5D) algorithm. When examining the difference in utility values between those who had a history of the nominated medical conditions, and those who didn't, the instruments based on either the EQ-5D (Dolan and Shaw), or based on instruments that mapped utility values from the EQ-5D (i.e. Franks MEPS, Lawrence MEPS, Franks SF-12) appear to have greater differences than the algorithms designed specifically for the SF-36 (i.e. Lundberg, SF-6D and Fryback). For example, the difference between the utility value of a past stroke/TIA, for those instruments based on EQ-5D or mapped values from the EQ-5D, ranged between a decrement of 0.104 (Dolan) to 0.073 (Shaw), while the decrement for the others ranged from 0.051 for Brazier (SF-36) to 0.034 for Fryback. Diagramatically, this information is presented in Figure 1, where, for each algorithm, the mean differences (and 95% CI) for all of the complications are shown as deficits in utility value, with zero indicating no difference.

Table 3 shows the results of the rank correlations of the utility differences derived from values reported in Table 2. Overall there is a good consistency in the ranking for the seven complications: the Spearman's rank correlation coefficient ranges from 0.991 (between Shaw and Dolan) to 0.757 (between Dolan and Franks). The utility differences were then allocated to the four algorithm classes and averaged for each class. Figure 2 graphically represents the variability amongst the four algorithm classes.

The two health-related quality of life instruments (SF-36 and EQ-5D) were administered to all patients again at approximately two year after baseline. During this time, details of any serious adverse events (SAEs) experienced by the patients were also collected and then used in conjunction with the 2-year qol information to examine changes in health status since baseline. In the ADVANCE study, serious adverse events are defined as events that: (1) result in death, (2) are life threatening in the opinion of the responsible investigator,(3) require inpatient hospitalisation or prolongation of existing hospitalization, (4) result in persistent or significant disability or incapacity, (5) result in congenital anomaly or birth defect (unlikely since participants must be aged 55 years or over at entry to study), and (6) are important medical events in the opinion of the responsible investigator. The presence or absence of an SAE was then used in conjunction with the difference in utility measure between baseline and two-year values to calculate regression coefficients and signal/noise ratios (Table 4). Most algorithms showed similar signal-to-noise ratios, indicating that they have similar sensitivity to clinically relevant changes in health status.