SF-6D(SF-12v2XSF-36) health utility

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Sample sizes for the SF 6D preference based measure of health from the SF 36: a practical guide

Sample sizes for the SF 6D preference based measure of health from the SF 36: a practical guide

The increasing use of economic evaluation in the assessment of health care interventions has resulted in a growing demand for methods of measuring and valuing health that can be readily used in clinical trials. However, many conventional health-related quality of life measures are not suitable for use in economic evaluation. 3 These measures of health status or health related quality of life (HRQoL) are standardised questionnaires used to assess patient health across broad areas such as symptoms, physical functioning, work and social activities, and mental well- being. Responses to items are combined into either a single index or a profile of several sub-indices of scores. Most of these measures of HRQoL are scored using a summation of coded responses to the items. Such instruments have become widely used by clinical researchers and can provide useful descriptive information on the effectiveness of health care interventions. The main shortcoming of using such instruments in economic evaluation is that they do not explicitly incorporate preferences into their scoring algorithms. Another type of instrument is the utility or preference-based measure of health, that combine a descriptive system with preference weights obtained from members of the general population, such as the EQ-5D 4 and the Health Utility Index (HUI) 5 .

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Sample sizes for the SF-6D preference based measure of health from the SF-36: a practical guide

Sample sizes for the SF-6D preference based measure of health from the SF-36: a practical guide

The increasing use of economic evaluation in the assessment of health care interventions has resulted in a growing demand for methods of measuring and valuing health that can be readily used in clinical trials. However, many conventional health-related quality of life measures are not suitable for use in economic evaluation. 3 These measures of health status or health related quality of life (HRQoL) are standardised questionnaires used to assess patient health across broad areas such as symptoms, physical functioning, work and social activities, and mental well- being. Responses to items are combined into either a single index or a profile of several sub-indices of scores. Most of these measures of HRQoL are scored using a summation of coded responses to the items. Such instruments have become widely used by clinical researchers and can provide useful descriptive information on the effectiveness of health care interventions. The main shortcoming of using such instruments in economic evaluation is that they do not explicitly incorporate preferences into their scoring algorithms. Another type of instrument is the utility or preference-based measure of health, that combine a descriptive system with preference weights obtained from members of the general population, such as the EQ-5D 4 and the Health Utility Index (HUI) 5 .

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A systematic review of the validity and responsiveness of EQ-5D and SF-6D for depression and anxiety

A systematic review of the validity and responsiveness of EQ-5D and SF-6D for depression and anxiety

The EQ-5D questionnaire comprises a five dimensions: mobility, self-care, usual activities, pain and anxiety/depression. Respondents are asked to report their level of problems (no problems, some/moderate problems or severe/extreme problem) on each dimension to provide a position on the EQ-5D health state classification. Responses can be converted into one of 243 different health state descriptions (ranging from no problems on any of the dimensions [11111] to severe problems on all five dimensions [33333]) which each have their own preference-based score. Preference-based scores are determined by eliciting preferences i.e. establishing which health states are preferred from a population sample. To derive preferences a method such as time trade off (TTO) is used which involves asking participants to consider the relative amounts of time (for example, number of life-years) they would be willing to sacrifice to avoid a certain poorer health state. Utility values for each state have been elicited from respondents in various countries (see

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Are decisions using cost-utility analyses robust to choice of SF-36/SF-12 preference-based algorithm?

Are decisions using cost-utility analyses robust to choice of SF-36/SF-12 preference-based algorithm?

ping if the SF-12/SF-36 algorithm was based upon scores from an indirect preference-based HRQL measure, such as the EQ-5D. Note that these algorithms relate to the most recently advocated algorithms, as several authors pub- lished earlier algorithms and subsequently published updates (e.g. Shmueli) [16]. For brevity, each published algorithm is identified by the name of the first author. Brazier and colleagues constructed an econometric model for predicting health state valuations by first revising the SF-36 into a health status measure with 6 domains called the SF-6D [10]. Using a variant on the standard gamble, 249 health states defined by the SF-6D were valued by a representative sample of the UK general population. Ordi- nary least squares (OLS) models were estimated to predict all 18,000 SF-6D health states. The Brazier (SF-36) algo- rithm used for the present study was based on the parsi- monious consistent model, the preferred specification for model 10. The same data and a similar approach was used to estimate an algorithm based on the SF-12 [17]. Fryback and colleagues predicted Quality of Well-being Index (QWB) scores from SF-36 domain scores using data collected from the Beaver Dam Health Outcomes Study [12]. A six-variable regression model with three main effects (PF, MH, and BP) and three interaction terms (GH*RP, PF*BP, and MH*BP) is used to estimate preferences.

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What is the relationship between the minimally important difference and health state utility values? The case of the SF-6D

What is the relationship between the minimally important difference and health state utility values? The case of the SF-6D

In clinical trials, where HRQoL instruments are being in- creasingly used as primary outcome measures, it is simple to determine the statistical significance of a change in HR- QoL, but placing the magnitude of these changes in a con- text that is meaningful for health professionals, patients and other stakeholders (Pharmaceutical and Medical De- vice Developers, Insurance Payers, Regulators, Govern- ments) has not been so easy. Ascertaining the magnitude of change that corresponds to a minimal important differ- ence would help address this problem. [11] So when de- termining an important change standard the perspective can influence the assessment approach and the way in which an important difference is determined. [5] The minimal important difference (MID), from the patient perspective, can be defined as "the smallest difference in score in the domain of interest which patients perceive as bene- ficial and which would mandate, in the absence of troublesome side effects and excessive cost, a change in the patient's management". [9]

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What is the relationship between the minimally important difference and health state utility values? The case of the SF-6D

What is the relationship between the minimally important difference and health state utility values? The case of the SF-6D

Anchor-based methods examine the relationship between an health-related quality of life (HRQoL) measure and an independent measure (or anchor) to elucidate the meaning of a particular degree of change. One anchor-based approach uses an estimate of the MID, the difference in the QoL scale corresponding to a self-reported small but important change on a global scale. Patients were followed for a period of time, then asked, using question 2 of the SF-36 as our global rating scale, (which is not part of the SF-6D), if there general health is much better (5), somewhat better (4), stayed the same (3), somewhat worse (2) or much worse (1) compared to the last time they were assessed. We considered patients whose global rating score was 4 or 2 as having experienced some change equivalent to the MID. In patients who reported a worsening of health (global change of 1 or 2) the sign of the change in the SF-6D score was reversed (i.e. multiplied by minus one). The MID was then taken as the mean change on the SF-6D scale of the patients who scored (2 or 4). Results: This paper describes the MID for the SF-6D from seven longitudinal studies that had previously used the SF-36.

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Health utilities using SF-6D scores in Japanese patients with chronic hepatitis C treated with sofosbuvir-based regimens in clinical trials

Health utilities using SF-6D scores in Japanese patients with chronic hepatitis C treated with sofosbuvir-based regimens in clinical trials

is calculated by applying a preference-based weight to each patient’s health status; those preference-based weights are typically calculated in an a priori development or validation study for a specific health utility metric. There exist a num- ber of instruments designed specifically for calculation of health utility scores (HUI-2, EQ-5D, etc.) Notably, since most popular HRQL instruments, including SF-36, do not include any patients’ preferences to their scoring algorithms, the HRQL scores cannot be directly used in the role of util- ity scores. Thus, when specific health utility instruments are not available, regression-based methods for estimating health utility scores from the HRQL scores are typically used. One of such widely used approximations of health utility scores is SF-6D metric which can be calculated from SF-36; a number of parametric and non-parametric algo- rithms have been published and validated [22–24].

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Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D

Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D

FACT-G: Functional assessment of cancer therapy - general; EQ-5D: EuroQol 5D; SF-6D: Short form 36 health survey; OLS: Ordinary least squares; GLM: Generalized linear models; CLAD: Censored least absolute deviations; MAE: Mean absolute error; RMSE: Root mean squared error; CUA: Cost-utility analysis; QALY: Quality-adjusted life years; HRQoL: Health-related quality of life; CADTH: Canadian agency for drugs and technologies in health; ECOG: Eastern cooperative oncology group; PWB: Physical well-being; SWB: Social/family well-being; EWB: Emotional well-being; FWB: Functional well-being; VAS: Visual analogue scale; TTO: Time trade-off; SG: Standard gamble.

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The estimation of a preference-based measure of health from the SF-36

The estimation of a preference-based measure of health from the SF-36

The results of this study offer a method for analysing existing SF-36 data from trials and other sources of evidence where there is no other means of estimating the preference-based health values for generating QALYs. It also provides an alternative to existing preference-based measures of health for use in cost utility analysis. Two of the leading preference-based measures are the EQ-5D (Brooks, 1996) and the Health Utility Index (Torrance et al, 1995). Whether or not the SF-6D offers an improvement on these existing measures depends on one’s view of the appropriate definition of health, the valuation techniques and the best method for modelling health state values (Brazier et al, 1999). There is insufficient space in this paper to go into these issues. However, one of the advantages of the SF-6D over the EQ- 5D could come from the much larger size of its descriptive system and hence a possibly greater degree of sensitivity. This must be weighed against the inconsistencies between the coefficients at the upper levels of some SF-6D dimensions. The sensitivity of the new index needs to be compared to other preference-basecd measures before drawing any conclusion on this point. Any greater sensitivity would be most likely in groups experiencing mild to

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Estimation of minimally important differences in the EQ-5D and SF-6D indices and their utility in stroke

Estimation of minimally important differences in the EQ-5D and SF-6D indices and their utility in stroke

General and clinical characteristics, the Modified Rankin Scale (MRS) and the Barthel ADL index (BI) were gath- ered by trained interviewers who were registered nurses. The EQ-5D and the SF-36 v2 were self-administered with or without assistant. The EQ-5D is a generic preference-based measure that health status describes in terms of five dimensions: mobility, self-care, usual activ- ities, pain discomfort and anxiety/depression. Each di- mension has three levels, indicating no problems, some or moderate problems and extreme problems [15]. The EQ-5D index of health state was calculated using the valuation set of the Korean population [21]. Therefore, the possible range of EQ-5D scores was from −0.171 to 1.0, with 1.0 denoting full health (11111 state), and 0.0 denoting as bad as being dead. The SF-6D utility score could be calculated using Brazier’s et al’s algorithm, which was recommended by authors (model 10) [22]. The SF-6D consists of six dimensions (i.e., physical func- tioning, role limitations, social functioning, pain, mental health and vitality) and each dimension can be ranked in terms of between four and six levels. The SF-6D index was elicited from a preference-based algorithm, which was developed by the standard gamble method for the population of the United Kingdom [22] because a Korean valuation set for SF-6D was not available. There- fore, the possible range of the SF-6D is from 0.296–1.0.

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Exploring the validity of estimating EQ-5D and SF-6D utility values from the health assessment questionnaire in patients with inflammatory arthritis

Exploring the validity of estimating EQ-5D and SF-6D utility values from the health assessment questionnaire in patients with inflammatory arthritis

Although Scott et al ., reporting that the EQ-5D and HAQ were unrelated in measuring change (r = 0.08) [20], we found correlations of change scores to be con- siderably higher (EQ-5D and HAQ: 0.33 - 0.58). The data in this study suggest that, in certain situations, mapping from the HAQ to the EQ-5D or SF-6D may be acceptable. The results suggest that the mean EQ-5D for a group of patients predicted from the HAQ is bet- ter estimate than the mean SF-6D predicted from the HAQ than the SF-6D when using the methods of Bans- back, et al. [8]. In previous studies in RA using direct measurement, the EQ-5D has been shown to correlate more strongly with measures of functional disability and damage than the SF-6D [21-23]. Although the moderate to high correlations of the HAQ and SF-6D and higher R 2 for the relationship between observed and predicted SF-6D scores, suggesting the potential for mapping between the HAQ and SF-6D, the systematic differences between observed and predicted SF-6D scores are wor- rying since they suggest that the mapping function

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Assessment of health utilities and quality of life in patients with non alcoholic fatty liver disease

Assessment of health utilities and quality of life in patients with non alcoholic fatty liver disease

In multiple regression analysis, after adjustment for age, gender and ethnicity, having cirrhosis was inde- pendently associated with lower HRQL and utility scores in patients with NAFLD. In particular, the β values were as follows: − 32.6±6.2 ( p<0.0001) for PF, − 34.9±10.1 ( p=0.0009) for RP, − 13.0±6.4 ( p=0.0467) for BP, − 24.0 ±5.0 ( p<0.0001) for GH, − 15.5±5.3 ( p=0.0049) for VT, − 28.4±6.6 ( p<0.0001) for SF, − 34.4±10.3 ( p=0.0012) for RE, not signi fi cant for MH ( p=0.17), − 11.7±2.5 ( p<0.0001) for PCS, − 6.1±2.7 ( p=0.0267) for MCS, and − 0.102±0.030 ( p=0.0009) for SF-6D utilities. There were no other independent predictors of HRQL metrics and utilities in patients with NAFLD that would reach statis- tical signi fi cance (all p>0.05). In particular, no inde- pendent associations with HRQL scores were found for BMI, history of CVD, and type 2 diabetes (all p>0.10). In patients with cirrhosis, we similarly did not fi nd any asso- ciation of HRQL score with the CTP class, probably due to limited sample size .

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Measurement of SF 6D utility among patients with active tuberculosis

Measurement of SF 6D utility among patients with active tuberculosis

The rationale of this study is to measure Sf-6D utility scores of patients with active T.B. this is also the aim of the study to finds the factors that affect the HRQOL of patients with active T.B of Sargodha district. 120 active T.B patients are interviewed by visiting District T.B hospital of Sargodha district. Results shows that Utility scores of female patients are better than male patients, while patients belong to urban areas have better utility scores as compared to rural patients of T.B. Multiple regression analysis results show that indoor patient’s utility scores are better than outdoor patients. Disease severity, use of drugs, depression, pain and death threat are the factors that negatively affect the patients HRQOL, while opportunity of leisure and income level increase patients HRQOL. The study gives several suggestions on the basis of present analysis. With the advancement of medical technologies the treatment also should focus on those aspects that increase patients health related quality of life, like by giving the opportunity of leisure to patients their health related quality of life may be maximized. Giving them the financial assistance will also help in removing their financial hindrances. Government and concerning authorities should focus on controlling drugs among the people. Death threat and depression may be control by teaching the patients and by giving them cognitive behavioral therapy.

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A view from the Bridge: agreement between the SF-6D utility algorithm and the Health utilities Index

A view from the Bridge: agreement between the SF-6D utility algorithm and the Health utilities Index

There are two general approaches to the empirical bridging between SF-36 and utility. The first is to use regression analysis on a dataset where subjects have completed both SF-36 and a utility measure. Nichol [4] used this method with the Health Utilities Index (HUI) and Fryback [5] with the Quality of Well Being Index (QWB); both studies fit linear additive models by ordinary least squares with dimension scores of SF-36 as independent variables explaining between 50 to 60% of the variance in utility score. The second approach is to define and value a series of health states using combinations of response levels (e.g. ‘a little of the time’, ‘most of the time’) over SF-36 dimensions. This approach draws directly from the conceptual and empirical logic of multi-attribute utility theory (MAUT) [6] used in the construction of HUI [7] and EQ5D [8] where an additive or multiplicative utility function is estimated based on a fractional factorial design from the universe of all possible health states. The ‘bridge’ back to SF-36 is formed via the beta coefficients on the utility scoring formula and the corresponding levels on SF-36 dimensions.

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Use of Bayesian methods to model the SF-6D health state preference based data

Use of Bayesian methods to model the SF-6D health state preference based data

The model has been tested in terms of its predictive ability, where the predicted and actual mean values for the 249 health states valued by the representative popu- lation have been plotted with health states ordered by predicted health state values. 3 Figure 1a represents the resulting predicted mean health state valuation, solid line, alongside the actual mean health state valuations, represented by the dotted line. Additionally, the dashed line represents the errors obtained by the difference between the two valuations. It is clear that the model predicts the data quite well for all heath states. For com- parison, Fig. 1b presents the corresponding plots for the frequentist model (final column of Table 2). Although the results of the frequentist model 4 are comparable to the results of Bayesian model 4 in Fig. 1, the Bayesian model has the advantage of providing full probability distributions of the 249 health state utilities as a direct output from the modeling process rather than simply providing the mean value and/or standard deviation as is the case with the frequentist model. It may be argued that the frequentist model can do this, but only after producing the distribution of the betas and then using a Cholesky approach to derive health states distributions. These distributions are hugely important to capture the full range of uncertainty inherent in these utility esti- mates -- an increasingly important input to cost effect- iveness analyses for health technology assessment. More about this point will be discussed later in the article.

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Health-related quality of life in persons with West Nile virus infection: a longitudinal cohort study

Health-related quality of life in persons with West Nile virus infection: a longitudinal cohort study

Methods: Short Form 36 questionnaire data came from a Canadian WNV cohort (Loeb 2008) of 154 patients followed for up to three years. We generated health utilities using the SF-6D. We calculated mean utility scores throughout follow-up and examined predictors using a linear mixed-effects model. We summarized HRQoL post- acute infection as: (i) long-term utility (mean of scores one year onward); (ii) area under the curve (AUC) one year onward. We examined predictors using beta regression. We used multiple imputation for sensitivity analysis. Results: Mean utility scores improved from 0.59 (95% CI: 0.38, 0.93) at baseline to 0.77 (0.53, 1) at six months, before plateauing for the remaining two years. Mean long-term utility was 0.81 (0.78, 0.85) and mean AUC was 0.80 (0.76, 0.84). Patients with neuroinvasive disease had consistently worse scores than their non-neuroinvasive counterparts, with the gap nearly closed after six months. After adjusting for confounding, neuroinvasive disease was not a significant predictor of HRQoL either throughout follow-up or post-acute infection. Rather, number of comorbidities and baseline utility scores were. Sensitivity analysis showed similar findings.

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Effect of adaptive abilities on utilities, direct or mediated by mental health?

Effect of adaptive abilities on utilities, direct or mediated by mental health?

This study included patients with RA who had been diagnosed on average 13 years before. First, it can be questioned if patients still need to adapt to their illness so many years after diagnosis. It seems evident that adaptation takes place in the initial phase of the illness. However, the disabling, often progressive and uncontrol- lable characteristics of RA might result in adaptive pro- cesses, even after so many years. The results of this study indicate that adaptive abilities indirectly explain health state utilities, so this result might become more distinct when examining patients in the initial phase of their illness. Secondly, RA is a chronic illness character- ized by pain and deformity of the joints, leading to phy- sical limitations. There is evidence suggesting that

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The Self-Perception and Relationships Tool (S-PRT): A novel approach to the measurement of subjective health-related quality of life

The Self-Perception and Relationships Tool (S-PRT): A novel approach to the measurement of subjective health-related quality of life

Patients across seven participating service areas (i.e., renal, cancer, cardiology, mental health, chronic pain, diabetes, and alternative medicine) took part in an item Q-sort that was designed to reduce the item pool to contain items that were important to a majority of respondents. Twenty to twenty-five patients from each service area (n = 150) took part in this phase of the study. In some service areas where implementation of the Q-sort method was difficult or impractical (e.g., in waiting rooms with short waits), an Item Importance Checklist was employed. For each of the six HRQL domains, participants either sorted item cards or rated the relative importance of each item based on their illness experiences. A comparison of the distribu- tions of Q-sort and Item Importance Checklist data reveal no difference between the two methods, although the Q- sort allowed for more meaningful interaction between participants and research assistants.

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Properties of patient reported outcome measures in individuals following acute whiplash injury

Properties of patient reported outcome measures in individuals following acute whiplash injury

With the increasing need for quantitative assessment of the impact of preventive or treatment interventions, it is important to identify appropriate outcome measures for use in patients with whiplash injuries. Furthermore, these measures should ideally possess properties, such as internal consistency and construct validity, that satisfy broader regulatory and reimbursement requirements [9]. The MINT study included two generic instruments, the Short-Form Health Survey version 1 (SF-12) [10] and the preference-based EQ-5D-3 L [11], and one neck injury specific measure, the Neck Disability Index (NDI) [12]. Generic instruments are designed to be applicable across a range of health conditions and patient populations, and can be useful to detect unexpected outcomes or side-effects of interventions, which may not be picked up by condition specific measures designed to capture the predicted health status changes. Conversely, more narrowly targeted condi- tion specific measures can provide outputs with a greater clinical relevance, and are often associated with an in- creased responsiveness compared to generic measures [13]. These differing properties have led to the recommendation for the joint use of generic and condition specific measures in clinical trials [7].

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Properties of patient-reported outcome measures in individuals following acute whiplash injury

Properties of patient-reported outcome measures in individuals following acute whiplash injury

periods, with the EQ-5D asking specifically about an indi- vidual’s health ‘today’ , the version of the SF-12 in MINT using a one-week recall period and the NDI asking about current capabilities without specifying a time frame. Scales and the outcome space of possible answers also differ, a problem that can be partially, though not entirely, ad- dressed by standardisation (i.e. effect sizes or standardised response means), and the directions of values for better health are not always the same, with higher NDI scores corresponding to worse health, the reverse being the case for the other measures. When considering effect sizes and standardised response means, it is important to remember that differences between measures can be driven by differences in magnitude, differences in variability or both, which can make interpretations of these statistics more difficult.

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