Outcomes In Cost-effectiveness Analyses Research Articles

Measuring the Vaccine Success Index: A Framework for Long-Term Economic Evaluation and Monitoring in the Case of Rotavirus Vaccination

New vaccination programs measure economic success through cost-effectiveness analysis (CEA) based on an outcome evaluated over a certain time frame. The reimbursement price of the newly approved vaccine is then often reliant on a simulated ideal effect projection because of limited long-term data availability. This optimal cost-effectiveness result is later rarely adjusted to the observed effect measurements, barring instances of market competition-induced price erosion through the tender process. However, comprehensive and systematic monitoring of the vaccine effect (VE) for the evaluation of the real long-term economic success of vaccination is critical. It informs expectations about vaccine performance with success timelines for the investment. Here, an example is provided by a 15-year assessment of the rotavirus vaccination program in Belgium (RotaBIS study spanning 2005 to 2019 across 11 hospitals). The vaccination program started in late 2006 and yielded sub-optimal outcomes. Long-term VE surveillance data provided insights into the infection dynamics, disease progression, and vaccine performance. The presented analysis introduces novel conceptual frameworks and methodologies about the long-term economic success of vaccination programs. The CEA evaluates the initial target vaccination population, considering vaccine effectiveness compared with a historical unvaccinated group. Cost-impact analysis (CIA) covers a longer period and considers the whole vaccinated and unvaccinated population in which the vaccine has direct and indirect effects. The economic success index ratio of CIA over CEA outcomes evaluates long-term vaccination performance. Good performance is close to the optimal result, with an index value ≤1, combined with a low CEA. This measurement is a valuable aid for new vaccine introductions. It supports the establishment of robust monitoring protocols over time.

Cost-effectiveness of a mindful yoga intervention added to treatment as usual for young women with major depressive disorder versus treatment as usual only

In a randomized controlled trial in the Netherlands, we studied the (cost)effectiveness of adding a mindful yoga intervention (MYI+TAU) to treatment as usual (TAU) for young women with major depressive disorder (MDD). In this paper, we present the results of the economic analyses. Societal costs and health outcomes were prospectively assessed during 15 months for all randomized participants (n = 171). Symptoms of depression (Depression Anxiety and Stress Scales; DASS) and quality adjusted life years (QALYs) were used as health outcomes in the economic analyses. Mean total societal costs during the 15 months of the study were €11.966 for the MYI+TAU group and €13.818 for the TAU group, differences in mean total societal costs were not statistically significant. Health outcomes (DASS and QALY) were slightly in favour of MYI+TAU, but differences between groups were not statistically significant. Combining costs and health outcomes in cost-effectiveness analyses indicated that MYI+TAU is likely to be cost-effective compared to TAU which was confirmed by sensitivity analyses. Although there were limitations in the cost-effectiveness analysis, findings from this study suggest that MYI+TAU warrants future attention for the potential to be cost-effective compared to TAU for young women with MDD.

Impact of direct-acting antiviral treatment on health utility in patients with chronic hepatitis C in hospital and community settings.

Direct-acting antiviral agents (DAAs) have transformed chronic hepatitis C (CHC) treatment. Continued affordable access to DAAs requires updated cost-effectiveness analyses (CEA). Utility is a preference-based measure of health-related quality of life (HRQoL) used in CEA. This study evaluated the impact of DAAs on utilities for patients with CHC in two clinical settings. This prospective longitudinal study included patients aged ≥18 years, diagnosed with CHC and scheduled to begin DAA treatment, from two tertiary care hospital clinics and four community clinics in Toronto, Calgary, and Montreal. Patients completed two utility instruments (EQ-5D-5L and Health Utilities Index 2/3 (HUI2/3)) before treatment, 6 weeks after treatment initiation, and 12 weeks and 1 year after treatment completion. We measured utilities for all patients, and for hospital-based and community-based groups. Between 2017 and 2020, 209 patients (126 hospital-based, 83 community-based; average age 53 years; 65% male) were recruited, and 143 completed the 1-year post-treatment assessment. Pre-treatment, utilities were (mean ± standard deviation) 0.77 ± 0.21 (EQ-5D-5L), 0.69 ± 0.24 (HUI2) and 0.58 ± 0.34 (HUI3). The mean changes at 1-year post-treatment were 0.035, 0.038 and 0.071, respectively. While utilities for hospital-based patients steadily improved, utilities for the community-based cohort improved between baseline and 12-weeks post-treatment, but decreased thereafter. This study suggests that utilities improve after DAA treatment in patients with CHC in a variety of settings. However, community-based patients may face challenges related to comorbid health and social conditions that are not meaningfully addressed by treatment. Our study is essential for valuing health outcomes in CHC-related CEA.

The assessment of psychometric properties for the subjective wellbeing-5 dimensions (SWB-5D) questionnaire in the general Dutch population

PurposeFinancial resources for health care are limited, so assessment of intervention effectiveness in terms of health in relation to its costs is important. Measuring health outcomes in cost-effectiveness analyses is usually done by health-related quality of life measures, like the EQ-5D. However, over the past decade, innovations on the conceptual level of health have evolved and novel approaches are rising such as the capability approach, subjective wellbeing, and Positive Health. This study assesses the psychometric properties of the subjective wellbeing-5 dimension (SWB-5D) outcome measure.MethodsA quantitative, cross-sectional study design was used to determine the concurrent and construct (convergent and known group) validity for the SWB-5D. Concurrent and convergent validity were estimated as correlations between the SWB-5D and the Dutch version of the EQ-5D, ICECAP-A, and PH-17. Assessment of known-groups validity was based on the variables illness, education, and the overall happiness (Cantril Ladder) and overall health scale (EQ-5D VAS).ResultsA representative sample of 1016 respondents of the Dutch population completed an online questionnaire. The SWB-5D showed reasonable concurrent validity and showed good convergent and known-group validity. The SWB-5D had a lower ceiling effect compared to the EQ-5D and ICECAP-A.ConclusionCompared to traditional health measurement approaches, novel approaches are more focused on the mental and social pillars of health. The SWB-5D shows psychometric feasibility of comprehensive measurement of health, as indicated by a range of validity measures in a large representative sample of the Dutch population.

Trends in Author-Reported Cost-Effectiveness Thresholds in the United States from 1995 to 2018: Implications for Discount Rates.

Decisions based on cost-effectiveness analyses (CEAs) using equal discount rates for health and cost outcomes are consistent with using a constant cost-effectiveness threshold over time. We sought to analyze trends in author-reported cost per quality-adjusted life-year (QALY) thresholds from CEAs published for the US setting over 24 y to retrospectively assess whether the recommended equal discount rates for costs and health were consistent with trends in the CEA literature. We used the Tufts CEA Registry to assess whether author-reported cost-effectiveness thresholds changed in CEAs published for the US setting between 1995 and 2018 and back-calculated the implied discount rate for health based on these trends for inflation-adjusted cost-effectiveness thresholds and an annual discount rate for costs of 3%. We found 1995 CEAs published for the US setting and found that average nominal and inflation-adjusted cost-effectiveness thresholds increased over that time period. The discount rate for health would need to equal 2.43% to 2.48% (depending on the subset of CEAs analyzed) to be consistent with the observed trends in inflation-adjusted author-reported cost-effectiveness thresholds. We also found that restricting our analysis to currency years between 1995 and 2014 would result in a back-calculated discount rate for health of 2.99% to 3.28%. We found that CEA researchers have implicitly assumed that inflation-adjusted cost-effectiveness thresholds in the United States have been increasing over time (1995-2018), which is inconsistent with the recommended and prevailing choice of equal discount rates for health and cost outcomes. Our results are sensitive to the cutoff year used in the analysis. We show visually and through equations that the recommended and prevailing practice of using equal discount rates for cost and health outcomes in cost-effectiveness analyses (CEAs) logically implies a constant inflation-adjusted cost-effectiveness threshold over time.Using data from the Tufts CEA Registry, we found that author-reported cost-effectiveness thresholds used in CEAs published for the US setting with currency years between 1995 and 2018 increased over time (both with and without adjustment for inflation).Assuming an annual discount rate for costs equal to 3%, the discount rate for health would need to equal approximately 2.5% to preserve consistency across decisions taken at different dates given the observed trends in inflation-adjusted author-reported cost-effectiveness thresholds.This finding depends on the cutoff year used in the analysis (data from currency years 1995-2014 would support use of equal discount rates, whereas data after 2014 would suggest a sharper trend toward increasing cost-effectiveness thresholds).

QALY-time: experts\u2019 view on the use of the quality-adjusted life year in cost-effectiveness analysis in palliative care

BackgroundThe Quality-Adjusted Life Year (QALY) is internationally recognized as standard metric of health outcomes in cost-effectiveness analyses (CEAs) in healthcare. The ongoing debate concerning the appropriateness of its use for decision-making in palliative care has been recently mapped in a review. The aim was to report on and draw conclusions from two expert meetings that reflected on earlier mapped issues in order to reach consensus, and to advise on the QALY’s future use in palliative care.MethodsA nominal group approach was used. In order to facilitate group decision making, three statements regarding the use of the QALY in palliative care were discussed in a structured way. Two groups of international policymakers, healthcare professionals and researchers participated. Data were analysed qualitatively using inductive coding.Results1) Most experts agreed that the recommended measurement tool for the QALYs ‘Q’ component, the EuroQol-5D (EQ-5D), is inappropriate for palliative care. A more sensitive tool, which might be based on the capabilities approach, could be used or developed. 2) Valuation of time should be incorporated in the ‘Q’ part, leaving the linear clock time in the ‘LY’ component. 3) Most experts agreed that the QALY, in its current shape, is not suitable for palliative care.Conclusions1) Although the EQ-5D does not suffice, a generic tool is needed for the QALY. As long as no suitable alternative is available, other tools can be used besides or serve as basis for the EQ-5D because of issues in conceptual overlap. 2) Future research should further investigate the valuation of time issue, and how best to integrate it in the ‘Q’ component. 3) A generic outcome measure of effectiveness is essential to justly allocate healthcare resources. However, experts emphasized, the QALY is and should be one of multiple criteria for choices in the healthcare insurance package.

Developing a preference-based utility scoring algorithm for the Psoriasis Area Severity Index (PASI)

Introduction: It is challenging to identify health state utilities associated with psoriasis because generic preference-based measures may not capture the impact of dermatological symptoms. The Psoriasis Area Severity Index (PASI) is one of the most commonly used psoriasis rating scales in clinical trials. The purpose of this study was to develop a utility scoring algorithm for the PASI.Methods: Forty health states were developed based on PASI scores of 40 clinical trial patients. Health states were valued in time trade-off interviews with UK general population participants. Regression models were conducted to crosswalk from PASI scores to utilities (e.g. OLS linear, random effects, mean, robust, spline, quadratic).Results: A total of 245 participants completed utility interviews (51.4% female; mean age = 45.3 years). Models predicting utility based on the four PASI location scores (head, upper limbs, trunk, lower limbs) had better fit/accuracy (e.g. R2, mean absolute error [MAE]) than models using the PASI total score. Head/upper limb scores were more strongly associated with utility than trunk/lower limb. The recommended model is the OLS linear model based on the four PASI location scores (R2 = 0.13; MAE = 0.03). An alternative is recommended for situations when it is necessary to estimate utility based on the PASI total score.Conclusions: The derived scoring algorithm may be used to estimate utilities based on PASI scores of any treatment group with psoriasis. Because the PASI is commonly used in psoriasis clinical trials, this scoring algorithm greatly expands options for quantifying treatment outcomes in cost-effectiveness analyses of psoriasis therapies. Results indicate that psoriasis of the head/upper limbs could be more important than trunk/lower limbs, suggesting reconsideration of the standard PASI scoring approach.

Measuring Weight-Specific Quality of Life in Adolescents: An Examination of the Concurrent Validity and Test-Retest Reliability of the WAItE

ObjectivesTo examine the concurrent validity of the Weight-specific Adolescent Instrument for Economic evaluation (WAItE) as compared with the generic, preference-based Child Health Utility 9D (CHU-9D) and the weight-specific Youth Quality of Life—Weight (YQOL-W) and also to examine the test-retest reliability of the WAItE. MethodsAn online survey was used to administer the 3 instruments on a sample of adolescents (aged 11-18 years). Individual responses were converted into either utility scores (CHU-9D) or health-related quality-of-life scores (WAItE and YQOL-W). A 10% subsample of the respondents also completed the WAItE 1 week after completion to assess test-retest reliability. ResultsOne thousand adolescents completed the online survey. There was a strong correlation between the WAItE and both the CHU-9D (0.731; P<.001) and the YQOL-W (0.747; P<.001). All 3 instruments were able to discriminate according to different weight status categories and a measure of self-assessed health. Unlike the CHU-9D or the YQOL-W, the WAItE did not show a substantial ceiling effect. The WAItE also showed acceptable levels of test-retest reliability. ConclusionsThe study results are encouraging, and illustrate that the WAItE can be used to reliably and accurately measure weight-specific outcomes in the younger population. The WAItE can also be used to assess outcomes in cost-effectiveness analysis of weight management interventions for young people, given the instrument is less likely to display ceiling effects and may thus be more sensitive in measuring change that results from interventions developed for this population.

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

BackgroundConventionally, models used for health state valuation data have been frequentists. Recently a number of researchers have investigated the use of Bayesian methods in this area. The aim of this paper is to put on the map of modelling a new approach to estimating SF-6D health state utility values using Bayesian methods. This will help health care professionals in deriving better health state utilities of the original UK SF-6D for their specialized applications.MethodsThe valuation study is composed of 249 SF-6D health states valued by a representative sample of the UK population using the standard gamble technique. Throughout this paper, we present four different models, including one simple linear regression model and three random effect models. The predictive ability of these models is assessed by comparing predicted and observed mean SF-6D scores, R2/adjusted R2 and RMSE. All analyses were carried out using Bayesian Markov chain Monte Carlo (MCMC) simulation methods freely available in the specialist software WinBUGS.ResultsThe random effects model with interaction model performs best under all criterions, with mean predicted error of 0.166, R2/adjusted R2 of 0.683 and RMSE of 0.218.ConclusionsThe Bayesian models provide flexible approaches to estimate mean SF-6D utility estimates, including characterizing the full range of uncertainty inherent in these estimates. We hope that this work will provide applied researchers with a practical set of tools to appropriately model outcomes in cost-effectiveness analysis.

PRM75 - Use Of Model Averaging Techniques in Cost-Effectiveness Analysis in Oncology

Often in cost-effectiveness analysis (CEA) of oncologic drugs, survival data from a randomized controlled trial are extrapolated to a lifetime horizon using parametric regression techniques. To capture parameter uncertainty in the analysis, regression parameters along with other model parameters are varied in probabilistic sensitivity analysis. However, structural uncertainty in the choice of regression model is rarely investigated. This study discusses the use of model averaging and provides an example to address structural uncertainty in CEA. Using a cohort partition model, the numbers of patients in “progression-free”, “progressed”, and “dead” health states were calculated directly from progression-free survival (PFS) and overall survival (OS) curves. Weibull, exponential, lognormal, log-logistic, generalized gamma, and Gompertz parametric models were used to extrapolated these curves to a lifetime horizon. Total costs, life year (LY), and quality adjusted life year (QALY) for each regression model were estimated. Weighted results across all models were calculated, based on weights that were derived from Akaike’s or Bayesian Information Criterion (AIC or BIC) parameters. Evaluating solely on BIC values, the lognormal distribution was identified as the best model for both survival curves. This resulted in the lowest observed ICERs. When model selection was based on considerations involving the log-cumulative hazard plots, clinical plausibility, and AIC/ BIC for each distribution, the Weibull distribution was selected for both curves, resulting in a 29% and 27% increase in the ICER for QALY and LY, respectively. Similar increases were observed when model averaging was applied using BIC-derived weights. In this case, model averaging produced results that were similar to those where model selection was based on multiple criteria. Choice of parametric models often has the biggest impact on the outcomes in CEAs in oncology. Model averaging takes into account the structural uncertainty surrounding the choice of parametric models.

SURROGATE, FRIEND OR FOE? THE NEED FOR CASE STUDIES OF THE USE OF SURROGATE OUTCOMES IN COST‐EFFECTIVENESS ANALYSES

We comment on a recent article by Hawkins et al. presenting different analytic approaches for the inclusion of surrogate and final outcomes in cost-effectiveness analyses. This methodological case study provides a timely and valuable contribution to build on the debate about the use of evidence based on surrogate outcomes in health technology assessment.

Cost-effectiveness of greenhouse gas mitigation in transport: A review of methodological approaches and their impact

A review is given of methodological practices for ex ante cost-effectiveness analysis (CEA) of transport greenhouse gas (GHG) mitigation measures, e.g. fuel economy and CO 2 standards for road vehicles in the US and EU. Besides the fundamental differences between different types of policies and abatement options which inherently result in different CEA outcomes, differences in methodological choices and assumptions are another important source of variation in CEA outcomes. Fourteen methodological issues clustered into six groups are identified on which thirty-three selected studies are systematically reviewed. The potential variation between lower and upper cost-effectiveness estimates for GHG mitigation measures in transport, resulting from different methodological choices and assumptions, lies in the order of $400 per tonne CO 2-eq. The practise of using CEA for policy-making could improve considerably by clearly indicating the specific purpose of the CEA and its strengths and limitations for policy decisions. Another improvement is related to the dominant approach in transport GHG mitigation studies: the bottom-up financial technical approach which assesses isolated effects, implying considerable limitations for policy-making. A shift to welfare-economic approaches using a hybrid model has the potential to establish an improved assessment of transport GHG mitigation measures based on realistic market responses and behavioural change.

Regression Estimators for Generic Health-Related Quality of Life and Quality-Adjusted Life Years

To develop regression models for outcomes with truncated supports, such as health-related quality of life (HRQoL) data, and account for features typical of such data such as a skewed distribution, spikes at 1 or 0, and heteroskedasticity. Regression estimators based on features of the Beta distribution. First, both a single equation and a 2-part model are presented, along with estimation algorithms based on maximum-likelihood, quasi-likelihood, and Bayesian Markov-chain Monte Carlo methods. A novel Bayesian quasi-likelihood estimator is proposed. Second, a simulation exercise is presented to assess the performance of the proposed estimators against ordinary least squares (OLS) regression for a variety of HRQoL distributions that are encountered in practice. Finally, the performance of the proposed estimators is assessed by using them to quantify the treatment effect on QALYs in the EVALUATE hysterectomy trial. Overall model fit is studied using several goodness-of-fit tests such as Pearson's correlation test, link and reset tests, and a modified Hosmer-Lemeshow test. The simulation results indicate that the proposed methods are more robust in estimating covariate effects than OLS, especially when the effects are large or the HRQoL distribution has a large spike at 1. Quasi-likelihood techniques are more robust than maximum likelihood estimators. When applied to the EVALUATE trial, all but the maximum likelihood estimators produce unbiased estimates of the treatment effect. One and 2-part Beta regression models provide flexible approaches to regress the outcomes with truncated supports, such as HRQoL, on covariates, after accounting for many idiosyncratic features of the outcomes distribution. This work will provide applied researchers with a practical set of tools to model outcomes in cost-effectiveness analysis.

Posaconazole

Posaconazole (Noxafil®) is an oral, second-generation, extended-spectrum triazole whose approved indications include prophylaxis of invasive fungal disease (IFD) in immunocompromised patients. In pivotal head-to-head trials, posaconazole was significantly more effective in preventing IFD than standard azole therapy (i.e. oral fluconazole or itraconazole) in chemotherapy-induced neutropenic patients with acute myelogenous leukaemia (AML) or myelodysplastic syndromes (MDS) and was noninferior to treatment with fluconazole in patients with graft-versus-host disease (GVHD) who were receiving intensive immunosuppressive therapy following haematopoietic stem cell transplantation. In both indications, prophylactic posaconazole was associated with significantly lower rates of IFD-related mortality. The overall tolerability profile of posaconazole was generally similar to that of the other prophylactic treatments. The large body of modelled cost-effectiveness analyses from a healthcare payer perspective on the use of prophylactic posaconazole suggest that it is a dominant or cost-effective option relative to prophylaxis with standard azole therapy in neutropenic patients with AML/MDS, and fluconazole in patients with GVHD. Based on clinical trial data in these patient groups, antifungal prophylaxis with posaconazole was predicted to be a dominant or cost-effective option relative to prophylaxis with standard oral azoles, with regard to the incremental cost per QALY gained, life-year (LY) gained and/or other outcomes in cost-effectiveness analyses in numerous countries. In those analyses in which posaconazole did not dominate the comparator, posaconazole was considered cost effective, as the incremental cost per QALY or LY gained with posaconazole was lower than assumed willingness-to-pay thresholds. Sensitivity analyses consistently demonstrated that these results were robust to plausible changes in key model assumptions. In conclusion, prophylactic treatment with posaconazole is clinically effective in preventing IFD in neutropenic patients with AML/MDS and patients with GVHD. Available pharmacoeconomic data from several countries, despite some inherent limitations, support the use of posaconazole as a dominant or cost-effective prophylactic antifungal treatment relative to prophylaxis with standard oral azoles in these patient populations at high risk of developing IFD.

Cost-effectiveness of varicella and zoster vaccination in England&Wales: importance measures for correlated parameters

This study aims to explore irregular relationships between input and outcomes in cost-effectiveness analysis, accounting for interdependencies between many input parameters. We used a dynamic infectious disease transmission model investigating the cost-effectiveness of varicella and zoster vaccination as a case study. Incremental costs and effects were used separately as the outcomes of interest, rather than the ratio between these, and a single R2 was calculated for groups of interdependent input parameters. Partial R2 was obtained as measure for the marginal proportion of variance explained. The most important input parameters were related to zoster epidemiology and disease transmission. In case of linear relationships, R2 is an easy to obtain importance measure, also in the presence of interdependent input parameters.

Measure-dependent variation in burden of disease estimates: implications for policy.

Health adjusted life years (HALYs) are used for estimating burden of disease and as outcomes in cost-effectiveness analyses of medical care and public health interventions. The impact of use of health-related quality of life (HRQL) scores associated with the disability-adjusted life year (DALY), the quality-adjusted life year (QALY), and the years of healthy life (YHL) measure on burden of disease estimates by income and race for five illnesses was studied. Abridged life tables were constructed using 1997 death certificate data from the National Center for Health Statistics. These tables were then quality-adjusted using prevalence data from the National Health Interview Survey and HRQL scores obtained using the Quality of Well-Being scale, the DALY, and the YHL measure to estimate burden of disease for five common diseases. Separate estimates were made for low and higher income families as well as black persons and white persons. Measure-related burden of disease estimates differed substantially from one another. Rank order of disease burden was not maintained across measures. Discrepancies in the rank order of disease were greater when different sociodemographic groups were examined. Diseases and demographic groups will receive differing priorities for intervention or research depending on which measurement system is used to inform decision-making. Refinement and standardization of measures is necessary to enhance their utility for medical care and public health policy applications.

Evaluating Quality-Adjusted Life Years: Estimation of the Health Utility Index (HUI2) from the SF-36

Quality-adjusted life years (QALYs) are well recognized as a valid measure for outcomes in cost-effectiveness analyses. A summary health utility score is necessary to evaluate QALYs. The objective of this study was to predict a summary utility score (represented by the Health Utility Index [HUI2]) from scores on the SF-36. A structural equation framework was applied to longitudinal data collected from 1992 to 1995 on a sample of patients insured by Southem California Kaiser Permanente (N = 6921). An ordinary least squares (OLS) method was used to estimate the HUI2. The OLS model on cross-sectional data predicted 50.5% of the observed variance in HUI2 scores. Parameter estimates of all SF-36 components showed statistical significance at the P < 0.05 level. Results of this study provide a quantitative link between two important measures of health status. The present model can be used to estimate health utility summary scores in studies that have collected SF-36 data.

MGA4 A Retrospective Approach to Evaluate Qaly: Estimation of Health Utility Index from the SF-36 in A Managed Care Population

Quality adjusted life years (QALYs) are well recognized as a valid measure for outcomes in cost-effectiveness analyses. However, it is difficult to obtain a summary utility score from health status measure such as the SF-36. OBJECTIVE: To predict a summary utility score (represented by HUI) from the scores on the SF-36. METHODS: A structural equation framework was applied to data collected from 1992 to 1995 on the Southern California Kaiser Permanente population (n = 5,794). An instrumental variable (IV) method mitigated the endogeneity in estimating the HUI(MarkII). Socioeconomic and disease variables were used as covariates. A split-sample analysis provided cross-validation. RESULT: This model predicted 33.68% of the observed variance in HUI index scores with an adjusted R2of 0.3335. Observed HUI index scores were distributed with a mean of 0.7963 and std. deviation of 0.1796. Parameter estimates of most of the SF-36 components (except General Health & Social Functioning) showed statistical significance at α= 0.05 level. People with high chronic disease scores were found to have low SF-36 scores, and parameter estimates of this covariate were also found statistically significant at α= 0.05 level in all structural equations. However, all the socioeconomic variables showed statistical insignificance. Comparison of “Forecasting” and “Estimation” sub-samples showed satisfactory results during cross-validation. CONCLUSION: Result of this study provides a quantitative link between two important measures of health status. The present model can be used to estimate overall health utility summary scores from previous studies using the SF-36.

Recommendations of the Panel on Cost-Effectiveness in Health and Medicine

WEINSTEIN, MILTON C.; SIEGEL, JOANNA E.; GOLD, MARTHE R.; KAMLET, MARK S.; RUSSELL, LOUISE B. Author Information

Recommendations of the Panel on Cost-Effectiveness in Health and Medicine

To develop consensus-based recommendations for the conduct of cost-effectiveness analysis (CEA). This article, the second in a 3-part series, describes the basis for recommendations constituting the reference case analysis, the set of practices developed to guide CEAs that inform societal resource allocation decisions, and the content of these recommendations. The Panel on Cost-Effectiveness in Health and Medicine, a nonfederal panel with expertise in CEA, clinical medicine, ethics, and health outcomes measurement, was convened by the US Public Health Service (PHS). The panel reviewed the theoretical foundations of CEA, current practices, and alternative methods used in analyses. Recommendations were developed on the basis of theory where possible, but tempered by ethical and pragmatic considerations, as well as the needs of users. The panel developed recommendations through 2 1/2 years of discussions. Comments on preliminary drafts prepared by panel working groups were solicited from federal government methodologists, health agency officials, and academic methodologists. The panel's methodological recommendations address (1) components belonging in the numerator and denominator of a cost-effectiveness (C/E) ratio; (2) measuring resource use in the numerator of a C/E ratio; (3) valuing health consequences in the denominator of a C/E ratio; (4) estimating effectiveness of interventions; (5) incorporating time preference and discounting; and (6) handling uncertainty. Recommendations are subject to the ¿rule of reason,¿ balancing the burden engendered by a practice with its importance to a study. If researchers follow a standard set of methods in CEA, the quality and comparability of studies, and their ultimate utility, can be much improved.