A. Simon Pickard

Estimation of minimally important differences in EQ-5D utility and VAS scores in cancer

BackgroundUnderstanding what constitutes an important difference on a HRQL measure is critical to its interpretation. The aim of this study was to provide a range of estimates of minimally important differences (MIDs) in EQ-5D scores in cancer and to determine if estimates are comparable in lung cancer.MethodsA retrospective analysis was conducted on cross-sectional data collected from 534 cancer patients, 50 of whom were lung cancer patients. A range of minimally important differences (MIDs) in EQ-5D index-based utility (UK and US) scores and VAS scores were estimated using both anchor-based and distribution-based (1/2 standard deviation and standard error of the measure) approaches. Groups were anchored using Eastern Cooperative Oncology Group performance status (PS) ratings and FACT-G total score-based quintiles.ResultsFor UK-utility scores, MID estimates based on PS ranged from 0.10 to 0.12 both for all cancers and for lung cancer subgroup. Using FACT-G quintiles, MIDs were 0.09 to 0.10 for all cancers, and 0.07 to 0.08 for lung cancer. For US-utility scores, MIDs ranged from 0.07 to 0.09 grouped by PS for all cancers and for lung cancer; when based on FACT-G quintiles, MIDs were 0.06 to 0.07 in all cancers and 0.05 to 0.06 in lung cancer. MIDs for VAS scores were similar for lung and all cancers, ranging from 8 to 12 (PS) and 7 to 10 (FACT-G quintiles).DiscussionImportant differences in EQ-5D utility and VAS scores were similar for all cancers and lung cancer, with the lower end of the range of estimates closer to the MID, i.e. 0.08 for UK-index scores, 0.06 for US-index scores, and 0.07 for VAS scores.

Comparison of the EQ-5D and SF-12 health surveys in a general population survey in Alberta, Canada.

OBJECTIVES The purposes of this analysis were to evaluate the construct validity of the EQ-5D and compare responses on the EQ-5D with the Physical Component Summary (PCS-12) and Mental Component Summary (MCS-12) scores of the SF-12 Health Survey. METHODS Data were collected via a survey instrument mailed to 4,200 randomly selected subjects in the province of Alberta, Canada. The instrument contained the EQ-5D and SF-12 health surveys, with additional questions eliciting clinical and demographic information from the respondents. RESULTS 1,555 respondents returned mailed questionnaires; 606 questionnaires were returned undeliverable. The SF-12 summary scores were calculated for 1,380 respondents. Analysis of the EQ-5D responses by demographic variables found significant differences among categories of age, gender, and self-reported chronic medical conditions. Corresponding dimensions and summary scores were more strongly related (eg, mobility and PCS-12; F ratio = 598.3, P < 0.001) than dissimilar dimensions (eg, mobility and MCS-12; F ratio = 18.8, P < 0.001). The EQ-5D index scores were moderately correlated with SF-12 summary scores (r = 0.41 for MCS-12 and r = 0.68 for PCS-12). For subjects reporting no problems on the EQ-5D, PCS-12 and MCS-12 scores were significantly lower for people reporting medical problems or feelings of depression. CONCLUSIONS The results of this investigation generally supported the validity of the EQ-5D. However, an important ceiling effect was observed for the EQ-5D in this sample. The combination of the EQ-5D and SF-12 provides relatively broad coverage of important health domains and scores for various purposes.

Risk for Death Associated with Medications for Recently Diagnosed Chronic Obstructive Pulmonary Disease

Context Many think we need more information about the safety of respiratory medications for chronic obstructive pulmonary disease (COPD). Contribution This large casecontrol study examined associations between medications and risk for death in veterans with newly diagnosed COPD. Inhaled corticosteroids were associated with decreased risk for death. Theophylline and ipratropium were associated with increased risk for respiratory and cardiovascular death, respectively. Caution Potential confounders, such as smoking status and disease severity, were not known. Associations may not reflect causal relationships. Implication Additional research about the safety of ipratropium, one of the most commonly prescribed medications for COPD, is needed. The Editors Chronic obstructive pulmonary disease (COPD) is associated with substantial burden in terms of prevalence of disease (1), death and disability risk (2, 3), and health care costs (4). Despite recent interest in examining long-term outcomes associated with medications in patients with COPD (5, 6), some issues are not easily addressed by using randomized clinical trials. From a pharmacovigilance perspective, relatively rare adverse eventssuch as death associated with medication usemay not be detected in the short term. The patients who receive a medication may not be similar to those participating in clinical trials (7, 8) and may be more vulnerable to such events. Thus, evidence of longer-term benefits and harms associated with medicationsparticularly in patients with COPD, who tend to be elderly and have multiple comorbid conditions (9)can be informed by research that relies on observational data. Potential safety concerns with medications used to manage COPD may be substantial. A recent meta-analysis (10) showed a nearly 2.5-fold increase in respiratory deaths among patients receiving long-acting -agonists compared with those receiving placebo. In the Lung Health Study (11), the group randomly assigned to ipratropium bromide had more than twice as many cardiovascular deaths as those receiving placebo. In addition, the U.S. Food and Drug Administration recently issued a notice regarding the potential for an increased risk for stroke associated with tiotropium use in patients with COPD (12). The extent to which these safety concerns exist and can be generalized to patients with COPD outside the context of clinical trials is unclear. Therefore, we sought to examine the association between medication use and risk for death, including respiratory and cardiovascular deaths, in a large population of patients with recently diagnosed COPD. Methods We conducted this nested casecontrol study in patients with recently diagnosed COPD by using national Veterans Affairs inpatient, outpatient, pharmacy, and mortality databases, supplemented with data from the Centers for Medicare & Medicaid Services. Our sample comprised U.S. veterans who used the U.S. Veterans Health Administration health care system. The Hines Veterans Affairs Hospital, Hines, Illinois, institutional review board approved our research. Cohort Patients were eligible for inclusion if they received a diagnosis of COPD (International Classification of Diseases, 9th Revision [ICD-9], codes 491.x, 492.x, or 496) between 1 October 1999 and 30 September 2003 at 2 or more outpatient visits within 12 months or were admitted to the hospital with a primary diagnosis of COPD. Patients had to be 45 years of age or older when they received their first eligible diagnosis, have used Veterans Health Administration health care services for at least 1 year before their first COPD diagnosis, and have received respiratory medications. We excluded patients with a diagnosis of asthma. We followed patients from the date of their second eligible outpatient visit or their inpatient visit until death or 30 September 2004. Case Patients We identified all deaths that occurred during follow-up by using the Veterans Affairs Vital Status database, a combination of Veterans Affairs, Medicare, and Social Security Administration mortality data that captures approximately 98% of veteran deaths (13). Of these, 40% was randomly sampled and we attempted to determine cause of death. This sample was estimated to provide more than 80% power to detect odds ratios of 0.85 or lower or 1.15 or higher for each medication class. We ascertained cause of death by using National Death Index Plus data from the National Center for Health Statistics. We defined 4 groups of case patients on the basis of cause of death: respiratory, cardiovascular, respiratory or cardiovascular, and all-cause mortality. We defined respiratory as death due to a respiratory system disease (ICD-10 codes J00 to J99) and cardiovascular as death due to ischemic heart disease (ICD-10 codes I20 to I25), cardiomyopathy, cardiac arrest, or arrhythmias (ICD-10 codes I42 to I51). The index date for case patients was their death date. Control Participants Selecting more than 5 control participants per case patient can yield limited gains in efficiency; however, because we were assessing several medications simultaneously, we selected up to 10 control participants per case patient (14). We randomly selected control participants for each case patient from eligible patients who were alive at the time of the case event (15, 16). We matched control participants to case patients individually on the basis of sex, age category (45 to 54 years, 55 to 64 years, 65 to 74 years, 75 to 84 years, and 85 years of age), region of the country, and year of diagnosis. We assigned control participants the same index date as their matched case patients. Exposure We defined exposure to respiratory medications as having received medications in the 180 days preceding each patients index date. We identified medication exposure to inhaled corticosteroids, ipratropium, long-acting -agonists, theophylline, and short-acting -agonists. We defined primary exposure as any exposure in the 180-day period before the index date. We created mutually exclusive medication regimens on the basis of medication exposure. Exposure to short-acting -agonists was not considered as part of the regimen but was included as a covariate in the analysis. Covariates We identified covariates by using data from the year before diagnosis date until the index date. We used pharmacy data to identify medication use, including exposure to systemic steroids, antihypertensives, lipid-lowering medications, antiarrhythmics, and diabetes medications. We used inpatient and outpatient diagnoses to identify comorbid conditions. We measured health care utilization as the annual number of hospitalizations and outpatient physician visits. We identified COPD exacerbations during follow-up and whether they were inpatient or outpatient by using a previously described algorithm (17). Statistical Analysis We performed separate analyses for respiratory-specific, cardiovascular-specific, and all-cause mortality. We used conditional logistic regression to estimate adjusted odds ratios (ORs) and 95% CIs. We included the variables that we considered clinically important in each of the regression models. Specifically, we included measures of COPD-related severity in all of the models and included markers of cardiovascular disease in the models for cardiovascular and all-cause mortality. We included any remaining variables that changed OR estimates for respiratory medications by more than 10% in the final models (18). We assessed model fit by using the Bayesian information criterion and the Wald test of likelihood ratios and through examination of outlier effects with leverage and fit diagnostics (19). Adjusted odds ratios represented risk for events in patients receiving medication compared with those who had not received inhaled corticosteroids, ipratropium, long-acting -agonists, or theophylline in the previous 6 months. We performed all analyses with Stata/MP 10.0 for Windows (StataCorp, College Station, Texas). We conducted several sensitivity analyses to evaluate the robustness of our results. First, we restricted the comparison group to patients who were actively treated with a short-acting -agonist in the 180 days preceding the index date. Second, because veterans may use health care services outside the Veterans Health Administration system, we restricted the analysis to patients 65 years of age or older. We used Medicare health care utilization data on these patients to capture health care utilization outside of the Veterans Health Administration system. Third, we examined dose response by classifying those in the highest quartile of average daily dose into a high-dose group and the rest of those exposed into a low-dose group. Fourth, to observe the effects of ipratropium independent of short-acting -agonist exposure, we excluded patients who received a combination of ipratropium and short-acting -agonists in a single inhaler. Fifth, to address the imbalance in prevalence of chronic heart failure between case patients and control participants, we created analytic cohorts by matching on presence of chronic heart failure and repeated our analyses. Finally, we used the array approach to estimate the effect that unmeasured confounding could have had on point estimates of the association between medications and mortality (20). We varied the level of risk associated with the unmeasured confounder and the prevalence in the medication groups relative to the no-treatment groups to determine what level of differential exposure would change the conclusions from the primary analysis. We focused on current smoking rates and COPD severity because we considered these to be 2 of the most important and influential unmeasured confounders. We compared rates of smoking status and COPD severity across treatment groups by using data from a recently published study (21). Role of the Funding Source This research was funded by the U.S. Department of Veterans Affairs Health Services Research

Health Utilities Using the EQ-5D in Studies of Cancer

Cancer is one of the most frequent disease-specific applications of the EQ-5D. The objective of this review was to summarise evidence to support the validity and reliability of the EQ-5D in cancer, and to provide a catalogue of utility scores based on the use of the EQ-5D in clinical trials and in studies of patients with cancer.A structured literature search was conducted in EMBASE and MEDLINE to identify papers using key words related to cancer and the EQ-5D. Original research studies of patients with cancer that reported EQ-5D psychometric properties, responses and/or summary scores were included.Of 57 identified articles, 34 were selected for inclusion, where 12 studies reported evidence of validity or reliability and 31 reported EQ-5D responses or summary scores. The majority of investigations using the EQ-5D concerned patients with prostate cancer (n = 4), breast cancer (n = 4), cancers of the digestive system (n = 7) and Hodgkin and/or non-Hodgkin lymphoma (n = 3). Mean index based scores ranged from 0.33 (SD 0.4) to 0.93 (SD 0.12) and visual analogue scale scores ranged from 43 (SD 13.3) to 84 (SD 12.0) across subtypes of cancer.A substantial and growing body of literature using the EQ-5D in cancer that supports the validity and reliability of EQ-5D in cancer has emerged. This review provides utility estimates for cancer patients across a wide range of cancer subtypes, treatment regimens and tumour stage(s) that may inform the modelling of outcomes in economic evaluations of cancer treatment.

Responsiveness of generic health-related quality of life measures in stroke

AbstractObjective: To compare five preference-based generic measures of health-related quality of life (HRQOL) in terms of change scores, correlations among change scores, responsiveness, and quality adjusted life-years (QALYs) gained. Design: Observational longitudinal cohort study where clinical measures and self-assessed HRQOL measures were administered to stroke patients at baseline and at 6 months. Patients were categorized as ‘stable’, ‘some improvement’ and ‘large improvement’ using the Barthel Index, Modified Rankin Scale (MRS), and Center for Epidemiologic Studies Depression Scale (CES-D). For each group, paired t -tests and variants of effect size were used to compare the responsiveness of preference-based HRQOL summary scores, including the EQ-5D VAS and index-based score, SF-6D, and Health Utilities Index (HUI) Mark 2 (HUI2) and Mark 3 (HUI3) overall utility scores. Results: Ninety-eight of 124 (79%) patients completed the 6-month follow-up. Change scores of the EQ-Index, HUI2, and HUI3 were strongly correlated with changes in the Barthel Index and MRS, while the EQ-5D VAS had higher correlation with CES-D change scores than the other measures. The SF-6D, HUI3, and EQ-Index were generally more responsive than the HUI2 and EQ-5D Visual analogue scale (EQ-VAS). QALY estimates based on the EQ-5D index and HUI3 were twice as large as estimates based on the SF-6D and HUI2. Conclusions : The results of this study may assist in informing the selection of a preference-based generic HRQOL measure, although choice will also depend on study goals and context. We would caution against the generalization of the study results on responsiveness to conditions when more subtle change is expected.

Agreement Between Patient and Proxy Assessments of Health-Related Quality of Life After Stroke Using the EQ-5D and Health Utilities Index

Background and Purpose— Proxy informants can provide information on patients who are limited in ability to self-assess health-related quality of life (HRQL) after stroke. One alternative is to exclude assessments of such patients and attenuate generalizability. The purpose of this study was to examine patient-proxy agreement on the domains and summary scores of the EQ-5D and Health Utilities Index Mark 3 (HUI3) after stroke. Methods— An observational longitudinal cohort of 124 patients hospitalized after ischemic stroke and their family caregivers completed the HRQL measures at baseline and were followed up for 6 months. Patient and proxy agreement was assessed by use of weighted &kgr; or the intraclass correlation coefficient (ICC). Results— At baseline, the more observable domains of HRQL demonstrated greater agreement than the more subjective components. Cross-sectional point estimates of agreement were generally acceptable (ICC >0.70) for the EQ-5D Index and HUI3 summary scores when assessed ≥1 month after baseline. Agreement between change scores was generally poor to fair (ICC <0.60), but systematic bias was not observed for the indirect preference-based summary scores between baseline and 6 months. Conclusions— Results suggest that proxy assessments obtained 6 months after stroke are more reliable than those obtained within 2 to 3 weeks after stroke. Although proxy-assessed change scores for indirect preference-based summary scores of the EQ-5D and HUI3 provided suboptimal agreement with patient assessment, limited systematic bias may support their consideration as alternatives to missing data or statistical imputation. Further research into the validity and reliability of proxy assessments is suggested.

Proxy evaluation of health-related quality of life: a conceptual framework for understanding multiple proxy perspectives.

Proxy assessment of health-related quality of life (HRQL) may be sought to substitute for, or to complement, patient self-assessment. The viewpoint from which the proxy is asked to assess the patient is a subtle yet important aspect of proxy assessment. Proxy assessments can be elicited by asking a proxy to assess the patient as they think the patient would respond (ie, proxy-patient perspective) or for the proxy to provide their own perspective on the patients HRQL (ie, proxy-proxy perspective). In this article, we introduce a framework for differentiating between and understanding HRQL assessments according to rater viewpoint. The difference between patient self-assessment and the proxy-patient perspective is defined as the inter-rater gap, whereas the difference between the proxy-patient and proxy-proxy perspective is described as the intra-proxy gap. The inter-rater gap represents the difference between patient self-assessed HRQL and the proxy ability to comprehend the patient view. The extent to which the proxy-proxy perspective is informative will depend upon the proxys ability to provide reinforcing or complementary information, ie, represented by the intra-proxy gap, on the HRQL of the patient. We refer to the framework to emphasize the importance of delineating between proxy perspectives in study design and HRQL measurement and to guide inquiries into the validity and interpretation of the meaningfulness of the proxy HRQL assessments from each viewpoint. Future research and use of proxy raters of HRQL in clinical trials, population health monitoring, resource allocation, and clinical management can be informed by explicit consideration of the suggested framework.

Psychometric comparison of the standard EQ-5D to a 5 level version in cancer patients

Objective:The objectives of this study were: 1) to determine whether expanding the number of levels (ie, response categories) on the standard 3 level EQ-5D (EQ-5D-3L) to 5-levels (EQ-5D-5L) would improve the descriptive richness and ability of the measure to discriminate among different levels of health, and 2) to examine the psychometric properties of each EQ-5D version in patients with cancer. Methods:U.S.-based cancer patients self-assessed their health using EQ-5D-3L and EQ-5D-5L. These versions were compared in terms of ceiling effects, convergent validity based on correlations with ECOG performance status and FACT-G, discriminative ability using Rasch analysis, and informational richness using Shannons Evenness Index (J′). Results:A ceiling effect was observed among a greater proportion of respondents to EQ-5D-3L, n = 74/424 (17%), compared with EQ-5D-5L, n = 47/424 (11%). Within the midlevel of EQ-5D-3L (some problems), substantial partitioning of the sample into the 3 nonextreme levels of the EQ-5D-5L was observed across dimensions. EQ-5D-5L demonstrated a trend towards slightly stronger correlations with ECOG performance status compared with EQ-5D-3L for all dimensions of health, ie, rs (5L/3L): rmobility = 0.38/0.31; rself-care = 0.35/0.31; rusual activities = 0.55/0.47; rpain/discomfort = 0.43/0.37; ranxiety/depression = 0.23/0.16; rcrude summary score = 0.56/0.49. EQ-5D-5L demonstrated a greater relative efficiency and ability to discriminate different levels of health. Informational richness and evenness of EQ-5D-5L was slightly higher (J′5L = 0.75) than EQ-5D-3L (J′3L = 0.69). Conclusion:Evidence supported the validity of both EQ-5D-3L and EQ-5D-5L in cancer. However, results suggest a 5-level classifier system has less ceiling effect and greater discriminative ability with potentially more power to detect differences between groups compared with EQ-5D-3L.

Use of a preference-based measure of health (EQ-5D) in COPD and asthma.

BACKGROUND EQ-5D is a generic preference-based measure of health that can help to understand the impact of asthma and chronic obstructive pulmonary disease (COPD). The purpose of this paper was to synthesize literature on the validity and reliability of EQ-5D use in studies of asthma and COPD, and estimate EQ-5D utility scores associated with stage of disease. METHODS A structured search was conducted in EMBASE and MEDLINE (1988-2007) using keywords relevant to respiratory disease and EQ-5D. Original research studies in asthma or COPD that reported EQ-5D results and/or psychometric properties were included. RESULTS Studies that reported psychometric properties supported the construct validity, test-retest reliability, and responsiveness of EQ-5D in asthma (seven studies) and COPD (nine studies), although some evidence of ceiling effects were observed in asthma studies. In asthma studies that reported summary scores (n=11), EQ-5D index-based scores ranged from 0.42 (SD 0.30) to 0.93 (SD not reported). In COPD studies (n=8), scores ranged from 0.52 (SD 0.16) to 0.84 (SD 0.15). While few asthma studies reported scores by severity level, sufficient studies in COPD were available to calculate pooled mean utility scores according to GOLD stage: stage I=0.74 (0.62-0.87), stage II=0.74 (0.66-0.83), stage III=0.69 (0.60-0.78) and stage IV=0.61 (0.44-0.77) (most severe). CONCLUSIONS Evidence generally supported the validity and reliability of EQ-5D in asthma and COPD. Utility scores associated with COPD stage may be useful for modeling health outcomes in economic evaluations of treatments for COPD.

Evaluation of documented drug interactions and contraindications associated with herbs and dietary supplements: a systematic literature review

Background and Aims:  The use of herbs and dietary supplements (HDS) alone or concomitantly with medications can potentially increase the risk of adverse events experienced by the patients. This review aims to evaluate the documented HDS‐drug interactions and contraindications.