Populating an economic model with health state utility values: moving towards better practice

Background: When estimating health state utility values (HSUV) for multiple health conditions, the alternative models used to combine these data can produce very different values. Results generated using a baseline of perfect health are not comparable with those generated using a baseline adjusted for not having the health condition taking into account age and gender. Despite this, there is no guidance on the preferred techniques that should be used and very little research describing the effect on cost per QALY results. Methods: Using a cardiovascular disease (CVD) model and cost per QALY thresholds, we assess the consequence of using different baseline health state utility profiles (perfect health, individuals with no history of CVD, general population) in conjunction with three models (minimum, additive, multiplicative) frequently used to estimate proxy scores for multiple health conditions. Results: Assuming a baseline of perfect health ignores the natural decline in quality of life associated with co-morbidities, over-estimating the benefits of treatment to such an extent it could potentially influence a threshold policy decision. The minimum model biases results in favour of younger aged cohorts, while the additive and multiplicative technique produces similar results. Although further research in additional health conditions is required to support our findings, this pilot study highlights the urgent need for analysts to conform to an agreed reference case and provides initial recommendations for better practice. We demonstrate that in CVD, if data are not available from individuals without the health condition, HSUVs from the general population provide a reasonable approximation.health-state utility; health economics methods; methodology; decision models; health surveys

Using Health State Utility Values in Models Exploring the Cost-Effectiveness of Health Technologies

AbstractBackgroundTo improve comparability of economic data used in decision making, some agencies recommend that a particular instrument should be used to measure health state utility values (HSUVs) used in decision-analytic models. The methods used to incorporate HSUVs in models, however, are often methodologically poor and lack consistency. Inconsistencies in the methodologies used will produce discrepancies in results, undermining policy decisions informed by cost per quality-adjusted life-years.ObjectiveTo provide an overview of the current evidence base relating to populating decision-analytic models with HSUVs.FindingsResearch exploring suitable methods to accurately reflect the baseline or counterfactual HSUVs in decision-analytic models is limited, and while one study suggested that general population data may be appropriate, guidance in this area is poor. Literature describing the appropriateness of different methods used to estimate HSUVs for combined conditions is growing, but there is currently no consensus on the most appropriate methodology. While exploratory analyses suggest that a statistical regression model might improve accuracy in predicted values, the models require validation and testing in external data sets. Until additional research has been conducted in this area, the current evidence suggests that the multiplicative method is the most appropriate technique. Uncertainty in the HSUVs used in decision-analytic models is rarely fully characterized in decision-analytic models and is generally poorly reported.ConclusionsA substantial volume of research is required before definitive detailed evidence-based practical advice can be provided. As the methodologies used can make a substantial difference to the results generated from decision-analytic models, the differences and lack of clarity and guidance will continue to lead to inconsistencies in policy decision making

Issues encountered when using health state utility values in decision analytic models

The publications used in this thesis consist of eight first author publications (2008 to 2013) exploring three technical issues that arise when using health state utility values (HSUVs) in decision analytic models in health care. The research provides analysts with HSUVs that may be used to describe the health-related quality of life (HRQoL) associated with not having particular conditions (i.e., the baseline evidence), recommendations on the preferred method to estimate HSUVs for comorbidities when the required evidence is not available, functions that can be used to map between two of the most commonly used HRQoL instruments (EQ-5D and SF-6D) using individual level patient data, and showed how well these functions estimate mean HSUVs from non-preference-based mean scores. The collection of work provides a unique and original contribution to the evidence base by bringing several previously overlooked issues into the public domain. This was an underdeveloped area of health economics and the lack of methodological guidelines in this area could result in sub-optimal allocation of scarce resources. This undermines the rationale behind the use of the quality adjusted life-year (QALY) and decision-making informed by cost per QALY thresholds. The publications received positive comments during the peer-review process and have been cited in approximately 280 journal articles. The condition-specific and general population HSUVs, and the mapping functions, have been used to inform the HSUVs used in 146 different decision analytic models. The mapping functions have also been used to predict HSUVs in an additional 37 articles. The remaining articles use the research to either support their choice of methods or to support or compare the results with their own results. The body of work has informed Technical Support Documents commissioned by the National Institute for Health and Care Excellence, a book chapter, methodological reports and training courses provided to the pharmaceutical industry, lectures on a post-graduate degree course, and an annual short-course at the University of Sheffield

The Use of Health State Utility Values In Decision Models

Methodological issues of how to use health state utility values (HSUVs) in decision models arise frequently, including the most appropriate evidence to use as the baseline (e.g. the baseline HSUVs associated with avoiding a particular health condition or event), how to capture changes due to adverse events and how to appropriately capture uncertainty in progressive conditions where the expected change in quality of life is likely to be monotonically decreasing over time. As preference-based measures provide different values when collected from the same patient, it is important to ensure that all HSUVs used within a single model are obtained from the same instrument where ever possible. When people enter the model without the condition of interest (e.g. primary prevention of cardiovascular disease, screening or vaccination programmes), appropriate age- and gender-adjusted HSUVs from people without the particular condition should be used as the baseline. General population norms may be used as a proxy if the exact condition-specific evidence is not available. Individual discrete health states should be used for serious adverse reactions to treatment and the corresponding HSUVs sourced as normal. Care should be taken to avoid double counting when capturing the effects for both less severe adverse reactions (e.g. itchy skin rash or dry cough) and more severe adverse events (e.g. fatigue in oncology). Transparency in reporting standards for both the justification of the evidence used and any ‘adjustments’ is important to increase readers’ confidence that the evidence used is the most appropriate available

The Use Of Mapping To Estimate Health State Utility Values

Mapping functions are estimated using regression analyses and are frequently used to predict health state utility values (HSUVs) in decision analytic models. Mapping functions are used when evidence on the required preference-based measure (PBM) is not available, or where modelled values are required for a decision analytic model, for example to control for important sociodemographic variables (such as age or gender). This article provides an overview of the latest recommendations including pre-mapping considerations, the mapping process including data requirements for undertaking the estimation of mapping functions, regression models for estimating mapping functions, assessing performance and reporting standards for mapping studies. Examples in rheumatoid arthritis are used for illustration. When reporting the results of mapping standards the following should be reported: a description of the dataset used (including distributions of variables used) and any analysis used to inform the selection of the model type and model specification. The regression method and specification should be justified, and as summary statistics may mask systematic bias in errors, plots comparing observed and predicted HSUVs. The final model (coefficients, error term(s), variance and covariance) should be reported together with a worked example. It is important to ensure that good practice is followed as any mapping functions will only be as appropriate and accurate as the method used to obtain them; for example, mapping should not be used if there is no overlap between the explanatory and target variables

Recommended Methods for the Collection of Health State Utility Value Evidence in Clinical Studies

A conceptual model framework and an initial literature review are invaluable when considering what health state utility values (HSUVs) are required to populate health states in decision models. They are the recommended starting point early within a research and development programme, and before development of phase III trial protocols. While clinical trials can provide an opportunity to collect the required evidence, their appropriateness should be reviewed against the requirements of the model structure taking into account population characteristics, time horizon and frequency of clinical events. Alternative sources such as observational studies or registries may be more appropriate when evidence describing changes in HSUVs over time or rare clinical events is required. Phase IV clinical studies may provide the opportunity to collect additional longitudinal real-world evidence. Aspects to consider when designing the collection of the evidence include patient and investigator burden, whom to ask, the representativeness of the population, the exact definitions of health states within the economic model, the timing of data collection, sample size, and mode of administration. Missing data can be an issue, particularly in longitudinal studies, and it is important to determine whether the missing data will bias inferences from analyses. For example, respondents may fail to complete follow-up questionnaires because of a relapse or the severity of their condition. The decision on the preferred study type and the particular quality of life measure should be informed by any evidence currently available in the literature, the design of data collection, and the exact requirements of the model that will be used to support resource allocation decisions (e.g. reimbursement)

SUCCESSIONAL PATTERN AND PROCESS IN SECONDARY FORESTS OF DIFFERENT AGES IN THE EASTERN AMAZON

A maioria dos estudos de sucess\ue3o em florestas secund\ue1rias na Amaz\uf4nia avalia s\uedtios de diferentes idades, que representam uma cronoseq\ufc\ueancia sucessional, em vez de monitorar um \ufanico s\uedtio por v\ue1rios anos pelo invent\ue1rio cont\uednuo. Este estudo comparou a composi\ue7\ue3o e estrutura flor\uedsticas de esp\ue9cies arb\uf3reas com di\ue2metro a 1,3 m de altura (DAP) 65 1 cm, em s\uedtios com 4 e 12 anos na Amaz\uf4nia Oriental, e avaliou a mortalidade e o recrutamento em ambos os s\uedtios baseados em dados de invent\ue1rio cont\uednuo durante 4 anos de estudo. As \ue1reas de estudo foram abandonadas ap\uf3s m\ufaltiplos ciclos de uso agr\uedcola de 7 a 10 anos, desde ~1940. Ambos os s\uedtios s\ue3o dominados pelas esp\ue9cies arb\uf3reas Lacistema pubescens e Vismia guianensis , com densidade de indiv\uedduos, di\ue2metro, altura, \ue1rea basal e riqueza de esp\ue9cies significativamente maiores no s\uedtio de 12 anos. A densidade de indiv\uedduos, ao longo do tempo, foi crescente no s\uedtio de 4 anos e decrescente no de 12 anos; o di\ue2metro, a altura e a \ue1rea basal aumentaram nos dois s\uedtios. No s\uedtio de 4 anos, foi constatada uma taxa de recrutamento l\uedquido crescente entre 2000-2001 e 2001-2002, que diminuiu entre 2002-2003, indicando redu\ue7\ue3o gradual na coloniza\ue7\ue3o. No s\uedtio de 12 anos, foi observada alta mortalidade l\uedquida (13 e 11%), sobretudo nas duas primeiras avalia\ue7\uf5es, indicando o processo de autodesbaste. A combina\ue7\ue3o dos m\ue9todos de cronoseq\ufc\ueancia e invent\ue1rio cont\uednuo aumenta substancialmente o entendimento do desenvolvimento sucessional.Most published studies of secondary forest succession in the Amazon examine stands of different ages that represent a successional chronosequence, rather than monitoring a single stand over the long-term. This study compares floristic composition and structure of tree species with diameter at 1.3 m height (DBH) 65 1 cm in a 4-year-old and a 12-year-old re-growth stand in the Eastern Amazon, and examines mortality and recruitment occurring within both stands based on repeated sampling carried out annually for four years. The study areas were abandoned after multiple agricultural cicles that lasted 7 to 10 years, beginning in ~1940. Both stands are largely dominated by the same tree species Lacistema pubescens and Vismia guianensis , with significantly higher stem density, diameter, height, basal area and species richness in the 12-year-old stand. In the 4-year-old stand there were measured an increase in annual net recruitment during the first two data collection periods but relatively lower net recruitment during the last evaluation period, indicating on-going but gradually weakening colonization. There were registered a high net mortality during the first two data collection periods in the 12-years-old stand with a relatively lower net mortality during the last evaluation indicating rapid self thinning. When used in combination, the chronosequence and the longitudinal approaches significantly strengthen the understanding of successional development

Predicting Productivity Losses from Health-Related Quality of Life Using Patient Data.

OBJECTIVE: This paper estimates productivity loss using the health of the patient in order to allow indirect estimation of these costs for inclusion in economic evaluation. METHODS: Data from two surveys of inpatients [Health outcomes data repository (HODaR) sample (n = 42,442) and health improvement and patient outcomes (HIPO) sample (n = 6046)] were used. The number of days off paid employment or normal activities (excluding paid employment) was modelled using the health of the patients measured by the EQ-5D, international classification of diseases (ICD) chapters, and other health and sociodemographic data. Two-part models (TPMs) and zero-inflated negative binomial (ZINB) models were identified as the most appropriate specifications, given large spikes at the minimum and maximum days for the dependent variable. Analysis was undertaken separately for the two datasets to account for differences in recall period and identification of those who were employed. RESULTS: Models were able to reflect the large spike at the minimum (zero days) but not the maximum, with TPMs doing slightly better than the ZINB model. The EQ-5D was negatively associated with days off employment and normal activities in both datasets, but ICD chapters only had statistically significant coefficients for some chapters in the HODaR. CONCLUSIONS: TPMs can be used to predict productivity loss associated with the health of the patient to inform economic evaluation. Limitations include recall and response bias and identification of who is employed in the HODaR, while the HIPO suffers from a small sample size. Both samples exclude some patient groups

Azimuthal anisotropy of charged jet production in root s(NN)=2.76 TeV Pb-Pb collisions

We present measurements of the azimuthal dependence of charged jet production in central and semi-central root s(NN) = 2.76 TeV Pb-Pb collisions with respect to the second harmonic event plane, quantified as nu(ch)(2) (jet). Jet finding is performed employing the anti-k(T) algorithm with a resolution parameter R = 0.2 using charged tracks from the ALICE tracking system. The contribution of the azimuthal anisotropy of the underlying event is taken into account event-by-event. The remaining (statistical) region-to-region fluctuations are removed on an ensemble basis by unfolding the jet spectra for different event plane orientations independently. Significant non-zero nu(ch)(2) (jet) is observed in semi-central collisions (30-50% centrality) for 20 <p(T)(ch) (jet) <90 GeV/c. The azimuthal dependence of the charged jet production is similar to the dependence observed for jets comprising both charged and neutral fragments, and compatible with measurements of the nu(2) of single charged particles at high p(T). Good agreement between the data and predictions from JEWEL, an event generator simulating parton shower evolution in the presence of a dense QCD medium, is found in semi-central collisions. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe