Using the EQ-5D index score as a predictor of outcomes in patients with type 2 diabetes.

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1 Using the EQ-5D index score as a predictor of outcomes in patients with type 2 diabetes. WORK IN PROGRESS: PLEASE DO NOT QUOTE Status: In press with Medical Care Authors: Philip M. Clarke Ph.D 1,2 Alison J. Hayes Ph.D 1,2, Paul G. Glasziou Ph.D 3, Russell Scott FRACP 4, John Simes FRACP 1,2 Anthony C. Keech. FRACP 1,2 Affiliations: 1. School of Public Health, Edward Ford Building, University of Sydney, Australia. 2. NHMRC Clinical Trials Centre, University of Sydney, NSW, Australia. 3. Department of Primary Care, Headington OX3 7LF University of Oxford, United Kingdom 4. Lipids and Diabetes Research, Christchurch Hospital, Private Bag 4710 Christchurch, New Zealand. Address for corresponding author: A/Prof Philip Clarke, School of Public Health, Edward Ford Building (A27), University of Sydney, NSW 2006 Australia; Phone.: ; Fax.: ; .: philipc@med.usyd.edu.au. 1

2 Abstract Objective: To examine whether index scores based on the EQ-5D, a 5-item generic health status measure, are an independent predictor of vascular events, other major complications and mortality in people with Type 2 diabetes and to quantify the relationship between these scores and future survival. Subjects: 5-year cohort study involving 7348 patients with type 2 diabetes, aged between years who had been recruited to the FIELD study from Australia and New Zealand. Measures: Multivariate Cox proportional hazard regression models were used to estimate the hazard ratio associated with index scores derived from the EQ-5D on: (i) cardiovascular events (including coronary heart disease event, stroke, hospitalization for angina, or cardiovascular death); (ii) other major diabetes-related complications (heart failure, amputation, renal dialysis and lower extremity ulcer); and (iii) death from any cause. Life table methods were used to derive expected survival for patients with different index scores. Results: After adjusting for standard risk factors, a 0.1 higher index score (derived from the United Kingdom algorithm) was associated with an additional 7% (95% CI 4-11%) lower risk of vascular events, a 13% (CI 09-17%) lower risk of complications and up to 14% (95% CI 8-19%) lower rate of all-cause mortality. Conclusions: Index scores derived from the EQ-5D are an independent predictor of the risk of mortality, future vascular events, and other complications in people with type 2 diabetes. This should be taken into account when extrapolating health outcomes such as Quality adjusted life years (QALYs). Keywords: EQ-5D; utility; mortality; survival; diabetes; Quality Adjusted Life Years Abbreviations: BMI, Body mass index; FIELD, Fenofibrate Intervention and Event Lowering in Diabetes, QALYS, Quality Adjusted Life Years, HRQL, Health Related Quality of Life. 2

3 Introduction Quality-adjusted life years (QALYs) are becoming a common metric for quantifying health outcomes, as they are able to combine measures of both life expectancy and health-related quality of life (HRQL) into a single summary measure. QALYs are usually calculated by weighting each year of a patients remaining life by a HRQL index, which has been referred to as a health state utility 1, where 1.0 is equivalent to full health and 0.0 is equivalent to being dead. Health economists have traditionally used techniques such as the time tradeoff method on patients or the general public (based on questionnaires involving health state descriptions) in order to estimate preferences for health states. However, it is often difficult to directly measure health state utilities as the elicitation techniques often require extensive personal interviews. In practice these values are often obtained from algorithms or value sets developed for multi-attribute instruments such as the EQ-5D for quantifying HRQL 2. The calculation of QALYs requires either knowledge or prediction of survival and measures of a health state utility over time. When follow-up data on patients is available, a QALY can be calculated by multiplying the utility or preference weight associated with each health state by the duration of time in that state and then summing these quantities over time. When survival time is censored a method such as quality adjusted survival analysis must be employed. 3 Without follow-up data, extrapolation of both life expectancy and the HRQL profile is required. In regard to the latter, it may be important to take account of the potential relationship between current levels of HRQL and future morbidity. For example, if patients with lower health state utilities were at higher risk of events this would impact on their expected future QALYs. In a similar fashion index scores from generic HRQL instruments such as the EQ-5D, which have also been used to construct QALYs, may also be predictive of future morbidity and mortality. Until recently there has been little empirical evidence of the association between index scores and subsequent outcomes. Index scores derived from the EQ-5D have also been shown to be predictive of short-term survival of terminal cancer patients 4 and of one-year survival after coronary revascularisation. 5 The degree to which index scores can be predictive of outcomes over longer periods and for most chronic diseases has not yet been explored. 3

4 The primary purpose of this study is to test whether measures of HRQL based on index scores derived from the EQ-5D are independent predictors of morbidity and mortality of people with type 2 diabetes. The study is based on Australian and New Zealand patients with type 2 diabetes who participated in the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study. 6,7 A secondary purpose is to calculate estimates of the difference in life expectancy for different levels of index score. Methods Background Complications of diabetes which include events such as myocardial infarction, stroke, amputation and renal failure elevate the risk of mortality and reduce HRQL. These events are known to be influenced by risk factors such as blood pressure. 8 Risk equations are now available for people with diabetes 9,10 and these form the basis of simulation models for estimating outcomes such as QALYs in health economic evaluations. Typically such models focus on risk-factors derived from clinical assessment or biochemical measurement, rather than subjective measures of HRQL for predicting future morbidity and mortality 11. Patients The FIELD study is a double blind, placebo controlled trial performed in 63 centers in Australia, New Zealand and Finland, details of the design have been published elsewhere 6,7. In brief, 9,795 patients with type 2 diabetes, aged between years, were randomized to fenofibrate 200mg or matching placebo capsules. Patients were recruited from hospital clinics and community based sources. At entry to the main study all individuals had an initial plasma total-cholesterol concentration between mg/dl and mg/dl, plus either a total-cholesterol/ HDL-cholesterol of 4.0 or more, or a plasma triglyceride concentration of between 88.6 mg/dl and mg/dl. Exclusion criteria included taking lipid-modifying therapies, known renal impairment, chronic liver disease, symptomatic gallbladder disease, or a cardiovascular event within 3 months of recruitment. The study design conforms to the guidelines of the Declaration of Helsinki and it was approved by the local and national ethics committees. All patients gave informed written consent. The study is registered as an International Standard Randomized Controlled Trial, ISCTN

5 Assessment of health-related quality of life To measure HRQL, the EQ-5D questionnaire was administered to FIELD patients residing in Australia and New Zealand at a mean (SD) time of 2.87(0.81) years after randomisation to therapy. The EQ-5D is a multi-attribute instrument with a descriptive system covering five dimensions of self-reported health (mobility, self-care, usual activity, pain/discomfort, anxiety and depression) with three levels (no problem, some problem, or extreme problems) For example, stands for a health state where there are no problems in 4 of the 5 dimensions, but some problems with self-care. The developers of the EQ-5D have generated value sets in several countries to calculate a preference-based index for the 243 health states defined by responses to the five questions of the EQ-5D, using a scale on which 0.0 represents being dead and 1.0 full health. Values of the index can be negative for states that are deemed to be worse than death : so for example, the minimum value in the UK based value set 2 is to represent the worst possible health state (i.e., 33333). As there is no value set based on Time Trade Off developed for the Australian or New Zealand population we have used the two most commonly used values currently available for the EQ-5D. In this study the primary analysis used the EQ-5D value set developed from a valuation of key health states using the time trade off method conducted in the United Kingdom 2 and in a secondary analysis used valuations of the same health states based on a value set for the US population. 16 A total of 7704 (91.7%) of the 8402 Australian and New Zealand patients participating in the FIELD study completed at least one EQ-5D. Of these, 356 patients had one or more missing responses on the EQ-5D questionnaire and hence were excluded from the analysis. The age, baseline systolic blood pressure, total cholesterol:hdl ratio, HbA 1c levels and BMI of the excluded patients were not significantly different from those remaining in the analysis population (n=7348). For a small number of patients (<1%), mean imputation was used to estimate values for missing risk factors. Assessment of other risk factors Patients were required to attend scheduled study visits every 4-6 months, and continued to receive their normal care from their health professionals. Information was recorded on demographic and clinical characteristics including height, weight, blood pressure, smoking 5

6 status and pre-existing medical conditions. All blood and urine samples were analyzed in a single central laboratory in Adelaide, Australia. Values of all risk factors were calculated as an average of available measures between randomisation and the time of the first administration of EQ-5D. Each patient s mean risk factor values were used as covariates in the statistical analysis, which is comparable with methods used to develop previous diabetes risk prediction equations. 10 Outcomes We focused on three outcomes: (i) cardiovascular events (involving the first of any of a coronary heart disease event, stroke, hospitalization for angina, or cardiovascular death); (ii) other major diabetes-related complications (i.e. heart failure, amputation, renal dialysis and lower extremity ulcer); (iii) death from any cause. A diagnosis of myocardial infarction required at least two of three criteria: ECG changes, ischaemic symptoms, or raised cardiac enzymes. A stroke required evidence of onset of focal neurological defects, lasting at least 24 hours, with cerebral imaging to confirm an ischaemic stroke. Congestive heart failure was ascertained from a hospital admission with a principal diagnosis code of I50 ICD-10. Angina events were determined from hospital admissions with a pincipal diagnosis of ICD- 10 codes I20.00, I20.80 and I Amputation included all non-traumatic amputations of either digit or limb. Renal failure was defined as time of first dialysis, and lower extremity ulcer was defined by ICD-10 codes E11.73, E11.69 and L All major cardiovascular disease events, including deaths and all deaths from other causes were adjudicated by an outcomes assessment committee blinded to treatment allocation. The follow-up period for this study was from the time of administration of the first EQ-5D questionnaire until death, or close out of the FIELD study. Statistical methods The statistical analyses were undertaken in STATA and were divided into three phases. Unless otherwise noted all statistical inferences for significance were drawn at the 5% level. 6

7 Firstly, to visually examine the degree to which risk varies across ranges of index scores, estimates of Nelson Aalen cumulative hazard were plotted for the following groups (mean of each group reported in brackets): index score < 0.75 (0.59); index score >=0.75 and index score < 1.00 (0.81), index score = 1.00 (1.00). Each group represents an approximate tertile of the data. Standard log-rank methods without adjustment for covariates were used to test for significant differences between these groups for each specified endpoint. Multivariate Cox proportional hazard models were used to assess whether index scores and responses to dimensions were independently predictive of outcomes. This provided a hazard ratio associated with responses to EQ-5D index scores after controlling for baseline covariates including age, sex, duration of diabetes, HbA 1c, systolic blood pressure, BMI, lipid levels and previous complications. These factors were chosen as they have been shown to be significant predictors of diabetes related complications in previous risk models based on the UKPDS 9,10. Following a similar approach to the development of other risk models 18 factors were dropped through backwards stepwise elimination if their hazard ratios were not significant at the 10% level. For death from any cause, separate analyses were undertaken for those patients with prior events as this may impact on survival (including diagnosis of cancer or cardiovascular disease, or other major complications). To investigate whether there was heterogeneity in the hazard ratio of the index score by treatment allocation (i.e. fenofibrate or placebo) we interacted the index score weight by the treatment allocation indicator and tested for differences. The proportional hazards assumption underlying all models was examined using a test based on Schoenfeld residuals. 19 Secondly, to gain a measure of how an EQ-5D index score is associated with absolute risk an estimate of cumulative hazard for selected index scores (0.60 and 1.00) were calculated for an individual aged 60 with covariates set to their mean values (male, non-smoker, BMI 30.6, HbA 1c 7%, Systolic Blood Pressure 137mm Hg, Total: LDL cholesterol ratio 4.6) This involved multiplying the baseline hazard by the estimated hazard ratio. Incidence rates expressed as events of 100 person-years at risk for each type of event were reported to provide an overall summary measure of the differences in risk across levels of the index score. 7

8 Finally, estimates of the impact of index score on survival were obtained from a partial life table constructed using methods outlines in the statistical appendix. These tables were used to calculate the average life years lived for men and women aged between years for different levels of index score by gender and smoking status. Results The characteristics of the patients completing the initial EQ-5D are shown in Table 1. The average (SD) age of the 7,348 respondents was 66.0 (6.9) years and the average (SD) index score based on the UK valuation algorithm was 0.81 (0.22). The average EQ-5D index score differed between those with and without prior events. For those patients with no prior complications the mean (SD) index score score was 0.82 (0.20), where as it was lower for those who had experienced events: for those, with prior vascular events it was 0.76 (0.25) and for other major complications it was 0.65 (0.29). The mean (SD) duration of follow-up after EQ-5D completion was 2.4 (0.49) years with the longest duration being 4.5 years. During this period, 453 patients had one or more vascular events, comprising: vascular deaths (89), MI (129), stroke (83), angina (152); 193 patients experienced other complications, as defined above, with the breakdown of first complication being heart failure (95), ulcers (44), amputations (30), ulcer plus amputation (7) and renal dialysis (17); there were a total of 284 deaths including 180 non-vascular deaths. Figure 1 shows the cumulative hazard stratified by ranges of the index score. Across all three outcomes the lowest risk patients are those with an index score equal to 1 (i.e. at fullhealth) followed by those with a EQ-5D index score between 0.75 and For vascular events, other major diabetes-related complications and death from any cause in each case the EQ-5D index score is a significant independent predictor of risk after accounting for established risk factors (Table 2). In regard to interpretation, the HR estimates indicate that a 0.10 higher UK index score (which ranges from to 1.0) is associated with a 7% (95% CI 4-11%) lower risk of vascular events and a 13% (CI 09-17%) lower risk of complications. For all-cause mortality a 0.1 increase in the index score was 8

9 associated with a 14% (95% CI 8-19%) reduction in risk for patients with no known prior diabetes-related events or cancer and a 12% (95% CI 7-16%) reduction of risk for patients with a co-morbidity. A global test based on Schoenfeld residuals did not indicate a violation of the proportional hazards assumption of any model. Testing for heterogeneity in the hazard ratio of the index score between therapies did not indicate a significant difference for patients allocated to fenofibrate or placebo. The adjusted incidence of vascular events for those without prior events ranged from 0.86 to 0.60 per 100 person-years at risk for those with a index score of 0.60 and 1.00 respectively. The comparative rates for other outcomes were: 0.63 and 0.35 for other complications; 0.56 and 0.31 for mortality in individuals without prior events; and 2.57 and 1.44 for those with prior events. A secondary analysis, using US EQ-5D index scores instead of UK values in the Cox regression model also produced significant results with larger effects. A 0.1 increment in index score was associated with: an 11% (95% CI 07-16%) lower risk of vascular events; a 19% (CI 13-24%) lower risk of complications; and in regard to mortality a 20% (95% CI 12-27%) lower risk for patients without prior events; 17% (95% CI 11-23%) lower risk with prior events. Figure 2 shows the hazard ratios associated with items of the EQ-5D after controlling for other risk factors. The top half reports the hazard ratios with the degree and number of problems relative to category no problems on any dimension. Having some problems (i.e. on the second level) on two or more dimensions or having extreme problems (i.e. on the third level) on one dimension is associated with higher risk for all types of events. In regard to the individual dimensions having problems with mobility appears to be the strongest predictor of future risk (i.e. significant for all dimensions except mortality for those with prior events). Table 3 reports estimates of the average life years lived by gender and smoking status for people with different index scores derived using a partial life table, that contains estimates of risk over a twenty year period from 60 years of age. The left hand side of the table 9

10 reports expected life years lived for smokers and the right hand side for non-smokers. Higher index scores are associated with substantially longer life expectancy. For example, the average estimated life years over a twenty year period for 60 year old male nonsmokers who rate themselves at full health (i.e. index score of 1) is (17.42, 18.34) or 1.85 (1.05, 2.67) years more than those with a index score of Discussion This study has demonstrated that index scores derived from the EQ-5D can predict the risk of vascular events, diabetes-related complications and all-cause mortality in people with Type 2 diabetes. The multivariate analyses indicate that the index score provides additional information on risk of these outcomes that is over and above that determined from the major established risk factors such as HbA 1c, systolic blood pressure, lipid levels and smoking status. Patients who had a score 0.10 points higher on the UK based index scores had 7% lower rates of cardiovascular events, regardless of their clinical history, and 13% lower rates of other major diabetes-related complications if they had no history of diabetic complications. This difference in index score was also associated with a reduction in mortality of between 12-14%. The hazard ratios associated with US index scores were generally higher, but showed a similar pattern across outcomes. The main implication of this study for economic evaluations, is that patients with lower index scores are likely to have a lower HRQL over their remaining lifetime, not only because a lower score will mean they accumulate QALYs at a slower rate, but also because they have a higher incidence of events contributing to both morbidity and mortality in the future. For example, the incidence rate of some major complications and the risk of mortality for a patient with an index score of 0.60 is almost double the rate of those with an index score of When this difference in mortality is extrapolated using life table methods, this level of difference in risk produces up to a three years difference in life expectancy. Our results are likely to be particularly relevant to economic evaluations of therapies for diabetes, when the analysis is conducted alongside randomized controlled trials. In addition to the FIELD study, the EQ-5D has been used to measure HRQL in several recent large 10

11 trials of patients with type 2 diabetes such as the ADVANCE study. 20 For example, a potential application is adjustment of the outcomes to take account of differences in HRQL between patients in the trial population and those in a community setting. Such differences may arise when selection criteria for recruitment to a randomized control trial, or the quality of care has an impact on HRQL. It also suggests that differences in EQ-5D index scores could potentially be a useful intermediate outcome measure of the efficacy of a therapy. For example, if there were statistically significant improvements in EQ-5D index scores by a therapy at the end of a study, this would not only indicate potential difference in QALY within the trial, but also potentially continuing benefits as a consequence of the lower risk of future events. EQ-5D index scores could have value in a clinical setting where QALYs have recently been proposed as a performance measure. 21 It is important to stress that EQ-5D index scores are derived from responses to the five questions making up the EQ-5D questionnaire. It is interesting to note that having problems with mobility appears to be the only dimension that is consistently an independent predictor of future events. What appears to be more useful at identifying high risk subjects, are those who report problems on more than one dimension, or having extreme problems in any dimension. In this regard, a holistic summary measure such as the EQ-5D index score would appear to be necessary in identifying high risk patients as it is based on the pattern of responses across all dimensions. It should be noted that the EQ-5D is just one of a number of instruments that can be used to generate preference based values for assessing HRQL. It would be interesting to explore whether the preference scores generated by instruments such as the SF-6D 22 and HUI3 23 are also independent predictors of future morbidity and mortality in people with type 2 diabetes or other chronic diseases. A common feature of all these instruments is that they do not include a general self-reported health question and so the scores for health states are produced by responses to combinations of questions rather than a global rating of health. It is unclear why index scores an independent predictor of risk for subsequent fatal and nonfatal events. It is possible that this relates simply to additional prognostic factors which are linked to both worse quality of life and poorer outcomes. It is also unclear whether the patients health status and hence health related quality of life HRQL is the major driver or whether a patient s score for a given health state is a factor. For example, index scores may 11

12 be an indicator of patient behavior, or mood that might more directly influence subsequent events. Whatever the underlying mechanism, this does not negate the practical value of these results in making a more valid assessment of the net clinical benefit of therapy measured in terms of long term QALYs. In regard to limitations, the models in this study were confined to risk factors used in previous diabetes specific risk equations based on the UKPDS 10 and did not contain risk factors, such as microalbuminuria, which have been proposed as a potential additional risk factors for cardiovascular disease. 24 As it is possible that the impacts of different index scores are mediated by some of these omitted factors, the influence that additional clinical information has on the association between the index scores and these outcomes could be explored in future work. Patients in this study were followed for an average of two and a half years, which is a relatively short time compared with the patients remaining lifetimes. Hence, it would be useful to examine longer follow-up periods so as to understand the duration over which index scores can predict elevations in risk. It would also be useful to examine whether index scores are predictive of other types of complications, such as diabetic retinopathy, in future work. Finally, while we have quantified how EQ-5D index scores influence event rates and life expectancy, it would be useful to quantify the impact in terms of a holistic measure such as expected QALYs. This would require the development of a simulation model in which the transition probabilities between states depend not only upon the current state, but preference based values for these states. In summary, our results show that index scores derived from the EQ-5D can be used to identify diabetic patients who are at higher risk of complications and death. It suggests these scores represent a potential intermediate outcome measure and could be used to improve predictive models where morbidity and mortality are important outcomes of economic evaluations of therapies for diabetes. 12

13 Appendix: Construction of a partial life table to estimate life expectancy A partial life table reflecting risk between 60 and 80 years was used to construct estimates of average number of life years by level of EQ-5D index scores for smokers and nonsmokers. This was based on a proportional hazards model in which current age was time at risk (used to calculate the baseline hazard) and gender, smoking status, and duration of diabetes were covariates. The last covariate was included to capture the higher risk associated with long durations of diabetes. The hazard ratios (SE) for the estimated model were: index score 0.83 (0.02) ; female gender 0.64 (0.09) ; smoker 2.6 (0.42); duration of diabetes 1.14 (0.10). Tests of heterogeneity in the hazard ratio associated with index score for smokers and non-smokers indicated a non-significant difference (P=0.424). The annual probability of mortality ( q(t) ) between the ages of 60 and 80 years was calculated using the baseline survivor function ( S 0 ( t) ) using the following q t) = 1 S ( t + 1) / S ( t 0 0 ( ), where t (t=1 20) reflects number of years from the persons 60 th birthday. This probability was then adjusted for duration of diabetes. Separate probabilities were estimated for females and smokers by multiplying the annual probability by the relevant hazard ratio. Life tables were then constructed in the traditional manner, in which q(t) was used to estimate the number of persons alive l(t) at each age between 60 and 80. Based on the assumption of constant annual mortality, the number of person-years lived was then calculated as L( t) = ( l( t) + l( t + 1)) / 2 and the average life years lived at the age of 60 years was calculated: e( 1) = L( t) / l(1). Non-parametric bootstrapping is used to estimate confidence intervals for life-expectancies. This involves drawing with replacement 1000 samples from the original data and calculating life-expectancy with each replication. The confidence intervals can be calculated by selecting the 26th and 975th of the bootstrap replicates. 13

14 References 1. Torrance GW.Measurement of health state utilities for economic appraisal. J Health Econ. 1986; 5: Dolan P. Modeling valuations for Euroqol Health States. Med Care. 1997; 35: Glasziou PP, Simes RJ, Gelber RD. Quality adjusted survival analysis. Stat Med. 1990;9: Park SM, Park MH, Won JH, et. al. EuroQol and survival prediction in terminal cancer patients: a multi-center prospective study in hospice-palliative care units, Support Care Cancer. 2006; 14: Lenzen MJ, Scholte WMJ, op Reimer WJ, et. al. The additional value of patient-reported health status in predicting 1-year mortality after invasive coronary procedures: a report from the Euro Heart Survey on Coronary Revascularisation. Heart. 2007;93: FIELD Study Investigators: The need for a large-scale trial of fibrate therapy in diabetes: the rationale and design of the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study. Cardiovasc Diabetol 2004; 3:9. 7. FIELD study investigators Effects of long-term fenofibrate therapy on cardiovascular events in 9795 people with type 2 diabetes mellitus (the FIELD study): randomised controlled trial. Lancet 2005; 366: Stratton IM, Adler AI, Neil HA, et al. on behalf of the UK Prospective Diabetes Study Group. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 2000; 321: Stevens RJ, Kothari V. Adler, AI, et. al. The UKPDS risk engine: A model for the risk of coronary heart disease in Type II diabetes (UKPDS 56). Clinical Science. 2001; 101: Clarke P, Gray A, Briggs A, et. al. A model to estimate the lifetime health outcomes of patients with Type 2 diabetes: the United Kingdom prospective diabetes study (UKPDS) outcomes model (UKPDS 68). Diabetologia. 2004;47: The Mount Hood 4 Modeling Group. Computer Modeling of Diabetes and its Complications: A report on the Fourth Mount Hood Challenge Meeting, Diabetes Care. 2007; 30: Rabin R, de Charro F. EQ-5D: a measure of health status from the EuroQol Group. Ann Med. 2001; 33: Brooks R, Rabin R, de Charro F, eds. The Measurement and Valuation of Health Status Using EQ-5D: A European Perspective. Dordrecht: Kluwer Academic Publishers;

15 15. Szende, A.; Oppe, M; Devlin, N. (Eds.) EQ-5D Value Sets: Inventory, Comparative Review and User Guide Series: EuroQol Group Monographs, Vol. 2 Springer, Shaw JF, Johnson J, Coons SJ, US Valuation of the EQ-5D health states. Development and Testing of the D1 valuation model. Med Care. 2005; 43: StataCorp.. Statistical Software: Release 7.0. College Station, TX: Stata Corporation, Wilson PWF, D'Agostino RB, Levy D, et. al. Prediction of coronary heart disease using risk factor categories. Circulation 1998; 97: Schoenfeld D. Partial residuals for the proportional hazards regression model. Biometrika (1): Glasziou P, Alexander J, Beller E, et. al. Which health-related quality of life score? A comparison of alternative utility measures in patients with Type 2 diabetes in the ADVANCE trial, Health and Quality of Life Outcomes 2007, 5: Schmittdiel, J. Vijan, S. Fireman, B. Lafata, JE, Oestreicher, N. Selby, JV Predicted Quality-Adjusted Life Years as a Composite Measure of the Clinical Value of Diabetes Risk Factor Control. Medical Care. 45(4): , April Brazier, J., Roberts J. and Deverill M. The estimation of a preference-based measure of health from the SF-36 Journal of Health Economics Volume 21, Issue 2, March 2002, Pages Feeny, D. Furlong, W. Saigal S and Suna J. Comparing directly measured standard gamble scores to HUI2 and HUI3 utility scores: group- and individual-level comparisons, Social Science & Medicine Volume 58, Issue 4, February 2004, Pages Cobb FR, Kraus WE, Root M, et. al. Assessing risk for coronary heart disease: Beyond Framingham. Am Heart J 2003; 146:

16 Figure 1: Estimated cumulative hazard curves showing proportion having events by EQ-5D index score level Vascular events Diabetic complications All cause mortality Cumulative hazard (%) p< p< p< Time (years) since EQ5D survey Key: Index Score < 75 dotted line; Index score >=75 and < 1 dashed line; Index score =1 Unbroken line 16

17 Figure 2: Hazard ratios for having problems by dimensions of the EQ-5D* Level of Problems Dimensions in one dimension in 2 or more dimensions extreme in any dimension mobility self care pain usual activities anxiety / depression Vascular events HR (95% CI) Complications HR (95% CI) All cause mortality No prior events With prior events Level of Problems in one dimension in 2 or more dimensions extreme in any dimension Dimensions mobility self care pain usual activities anxiety / depression HR (95% CI) HR (95% CI) Note: Hazard ratios (HR) are expressed relative to the response category No problem. 17

18 Table 1: Characteristics of patients completing the EQ-5D questionnaire Characteristics N 7348 Demographic and clinical Female 2794 (38.0%) Age at first EQ-5D survey (years, mean (SD)) 66.0 (6.9) Duration diabetes (years, mean (SD)) 10.1 (5.9) Risk factors Hba 1c (%, mean (SD)) 7.0 (1.2) Total cholesterol:ldl ratio (mean (SD)) 4.6 (1.0) Body mass index (kg/m 2, mean (SD)) 29.8 ( ) Systolic blood pressure (mmhg, mean (SD)) (13.0) Current Smoker 624 (8.5%) Health-related quality of life Proportion of responses by EQ-5D dimension # : Mobility 69%/ 31%/ <1% Self-care 94%/ 5% /<1% Usual activities 74%/ 24%/ 2% Pain/ Discomfort 49%/ 47%/ 4% Anxiety and depression 77%/ 22%/ 1% EQ-5D index score UK mean (SD) 0.81 (0.22) EQ-5D index score US mean (SD) 0.85 (0.15) Clinical history prior to 1 st EQ-5D questionnaire* Prior CVD MI 476 (6.5%) Stroke 342 (4.7%) Angina 927 (12.6%) Prior complications Amputation 40 (0.5%) Heart failure 83 (1.1%) Dialysis 5 (0.1%) Lower extremity ulcer 45(0.6%) Prior vascular events 1386 (18.9%) Prior diabetic complications 143 (2.0%) Cancer diagnosis 299 (4.1%) # Proportion for each response category: Level 1 (i.e. no problems)/ Level 2 (some problems)/ Level 3 (extreme problems). *Includes clinical history prior to start of main trial and events that occurred between randomization and the first EQ-5D questionnaire. 18

19 Table 2: Hazard Ratios of risk factors and EQ-5D index score for vascular events, other complications of diabetes and all cause mortality based on multivariate proportional hazard models. Vascular events Diabetic complications All cause mortality All individuals All individuals With prior complications or cancer Without prior complications or cancer Number of Individuals Number of events PH test: χ statistic (P value) (0.25) (0.30) 9.70 (0.21) 3.31 (0.65) Variable HR P value HR P value HR P value HR P value EQ-5D index score per 0.10 point 0.93 < < < <0.001 Female < Age per 10 years 1.47 < < < <0.001 Diabetes duration per 10 years Hba 1c per 1% increase 1.19 < < Total/HDL cholesterol ratio per 1% Body Mass Index < Systolic blood pressure 1.17 < Current smoker < <0.001 Prior Vascular events 3.06 < < Prior Diabetic complications 2.36 < < < Cancer 3.75 < Note: Hazard ratios (HR) for variables that were not significant at P<0.1 have been omitted from the table 19

20 Table 3: Estimated average (95% CI) life years between the age of 60 to 80 years by gender, smoking status and index score level Smokers Non-smokers Men Women Men Women Level of index score (10.29,13.55) (12.89,15.84) (15.31,16.74) (16.96, 17.98) (12.15, 15.00) (14.24,17.02) (16.50,17.59) (17.74, 18.59) (13.89, 16.48) ,17.91) (17.42,18.34) (18.36,19.03) Difference 0.60 vs (1.05, 2.67) 1.39 (-0.68, 3.28) 1.05 (0.14,1.95) 0.69 (0.01, 1.39) 0.60 vs (1.23, 5.28) 2.50 (0.70, 4.31) 1.85 (1.05, 2.67) 1.21 (0.57, 1.84) 0.80 vs (-0.29,3.45) 1.11 (-0.43, 2.88) 0.80 (0.07,1.53) 0.52 (0.00, 1.05) 20