Estimation of a Preference-Based Index from a Condition-Specific Measure: The King's Health Questionnaire

Brazier, John; Czoski-Murray, Carolyn; Roberts, Jennifer; Brown, Martin C.; Symonds, Tara; Kelleher, Con

doi:10.1177/0272989x07301820

Cited by 121 publications

(135 citation statements)

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“…Some preference-based measures have been derived from HRQOL profile measures [14][15][16][17], typically in two stages. First, a subset of dimensions and items is selected from the HRQOL measure to form a health state classification system (HSCS).…”

Section: Introductionmentioning

confidence: 99%

QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30

et al. 2016

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Abstract:Purpose: To derive a health state classification system (HSCS) from the cancerspecific quality of life questionnaire, the EORTC QLQ-C30, as the basis for a multiattribute utility instrument.Methods: The conceptual model for the HSCS was based on the established domain structure of the QLQ-C30. Several criteria were considered to select a subset of dimensions and items for the HSCS. Expert opinion and patient input informed a priori Powered by Edit orial Manager® and ProduXion Manager® from Aries Syst em s Corporat ionselection of key dimensions. Psychometric criteria were assessed via secondary analysis of a pooled dataset comprising HRQOL and clinical data from 2616 patients from eight countries and a range of primary cancer sites, disease stages, and treatments. We used confirmatory factor analysis (CFA) to assess the conceptual model's robustness and generalisability. We assessed item floor effects (>75% observations at lowest score), disordered item response thresholds, coverage of the latent variable and differential item function (DIF) using Rasch analysis. We calculated effect sizes for known group comparisons based on disease stage and responsiveness to change. Seventy-nine cancer patients assessed the relative importance of items within dimensions.Results: CFA supported the conceptual model and its generalizability across primary cancer sites. After considering all criteria, 12 items were selected representing 10 dimensions: physical functioning (mobility), role functioning, social functioning, emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems.

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Section: Introductionmentioning

confidence: 99%

QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30

et al. 2016

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“…84 Utility values based on current smoking status (current or former smoker) were derived from the Health Survey for England adjusted for relevant covariates including disease prevalence. 85 The EuroQol-5 Dimensions, a validated instrument for measuring health-related quality of life, was used for both sets of utility values and UK tariffs were used for calculating utility scores 86 (see Appendix 1, Table 38). …”

Section: Utility Valuesmentioning

confidence: 99%

Nicotine preloading for smoking cessation: the Preloading RCT

Aveyard

Lindson

Tearne

et al. 2018

Health Technol Assess

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BackgroundNicotine preloading means using nicotine replacement therapy prior to a quit date while smoking normally. The aim is to reduce the drive to smoke, thereby reducing cravings for smoking after quit day, which are the main cause of early relapse. A prior systematic review showed inconclusive and heterogeneous evidence that preloading was effective and little evidence of the mechanism of action, with no cost-effectiveness data.ObjectivesTo assess (1) the effectiveness, safety and tolerability of nicotine preloading in a routine NHS setting relative to usual care, (2) the mechanisms of the action of preloading and (3) the cost-effectiveness of preloading.DesignOpen-label randomised controlled trial with examination of mediation and a cost-effectiveness analysis.SettingNHS smoking cessation clinics.ParticipantsPeople seeking help to stop smoking.InterventionsNicotine preloading comprised wearing a 21 mg/24 hour nicotine patch for 4 weeks prior to quit date. In addition, minimal behavioural support was provided to explain the intervention rationale and to support adherence. In the comparator group, participants received equivalent behavioural support. Randomisation was stratified by centre and concealed from investigators.Main outcome measuresThe primary outcome was 6-month prolonged abstinence assessed using the Russell Standard. The secondary outcomes were 4-week and 12-month abstinence. Adverse events (AEs) were assessed from baseline to 1 week after quit day. In a planned analysis, we adjusted for the use of varenicline (Champix®; Pfizer Inc., New York, NY, USA) as post-cessation medication. Cost-effectiveness analysis took a health-service perspective. The within-trial analysis assessed health-service costs during the 13 months of trial enrolment relative to the previous 6 months comparing trial arms. The base case was based on multiple imputation for missing cost data. We modelled long-term health outcomes of smoking-related diseases using the European-study on Quantifying Utility of Investment in Protection from Tobacco (EQUIPT) model.ResultsIn total, 1792 people were eligible and were enrolled in the study, with 893 randomised to the control group and 899 randomised to the intervention group. In the intervention group, 49 (5.5%) people discontinued preloading prematurely and most others used it daily. The primary outcome, biochemically validated 6-month abstinence, was achieved by 157 (17.5%) people in the intervention group and 129 (14.4%) people in the control group, a difference of 3.02 percentage points [95% confidence interval (CI) –0.37 to 6.41 percentage points; odds ratio (OR) 1.25, 95% CI 0.97 to 1.62;p = 0.081]. Adjusted for use of post-quit day varenicline, the OR was 1.34 (95% CI 1.03 to 1.73;p = 0.028). Secondary abstinence outcomes were similar. The OR for the occurrence of serious AEs was 1.12 (95% CI 0.42 to 3.03). Moderate-severity nausea occurred in an additional 4% of the preloading group compared with the control group. There was evidence that reduced urges to smoke and reduced smoke inhalation mediated the effect of preloading on abstinence. The incremental cost-effectiveness ratio at the 6-month follow-up for preloading relative to control was £710 (95% CI –£13,674 to £23,205), but preloading was dominant at 12 months and in the long term, with an 80% probability that it is cost saving.LimitationsThe open-label design could partially account for the mediation results. Outcome assessment could not be blinded but was biochemically verified.ConclusionsUse of nicotine-patch preloading for 4 weeks prior to attempting to stop smoking can increase the proportion of people who stop successfully, but its benefit is undermined because it reduces the use of varenicline after preloading. If this latter effect could be overcome, then nicotine preloading appears to improve health and reduce health-service costs in the long term. Future work should determine how to ensure that people using nicotine preloading opt to use varenicline as cessation medication.Trial registrationCurrent Controlled Trials ISRCTN33031001.FundingThis project was funded by the NIHR Health Technology Assessment programme and will be published in full inHealth Technology Assessment; Vol. 22, No. 41. See the NIHR Journals Library website for further project information.

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“…Some of this work has involved the development of entirely new measures (e.g. Revicki et al, 1998a and1998b;Torrance et al, 2004), whilst others have developed preference-based measures from existing instruments (Kok and McDonnell, 2002;Brazier et al, 2005;Brazier et al, 2008;Yang et al, 2006). The main approach has been to develop a health state classification (like the EQ-5D) from the health status measure using psychometric methods (see Brazier et al, 2009 for more details).…”

Section: Introductionmentioning

confidence: 99%

Preference‐based condition‐specific measures of health: what happens to cross programme comparability?

Brazier

Tsuchiya

2010

Health Economics

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Estimation of a Preference-Based Index from a Condition-Specific Measure: The King's Health Questionnaire

Abstract: The recommended model for estimating a preference-based measure from the condition-specific KHQ is presented.

Cited by 121 publications

References 32 publications

QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30

QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30

Nicotine preloading for smoking cessation: the Preloading RCT

Preference‐based condition‐specific measures of health: what happens to cross programme comparability?

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