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Background: The Pediatric Quality of Life Inventory™ 4.0 Short Form 15 Generic Core Scales (hereafter the PedsQL) and the Child Health Utility-9 Dimensions (CHU9D) are two generic instruments designed to measure health-related quality of life in children and adolescents in the general population and paediatric patient groups living with specific health conditions. Although the PedsQL is widely used among paediatric patient populations, presently it is not possible to directly use the scores from the instrument to calculate quality-adjusted life-years (QALYs) for application in economic evaluation because it produces summary scores which are not preference-based.Objective: This paper examines different econometric mapping techniques for estimating CHU9D utility scores from the PedsQL for the purpose of calculating QALYs for cost-utility analysis.Methods: The PedsQL and the CHU9D were completed by a community sample of 755 Australian adolescents aged 15-17 years. Seven regression models were estimated: ordinary least squares estimator, generalised linear model, robust MM estimator, multivariate factorial polynomial estimator, beta-binomial estimator, finite mixture model and multinomial logistic model. The mean absolute error (MAE) and the mean squared error (MSE) were used to assess predictive ability of the models.Results: The MM estimator with stepwise-selected PedsQL dimension scores as explanatory variables had the best predictive accuracy using MAE and the equivalent beta-binomial model had the best predictive accuracy using MSE.Conclusions: Our mapping algorithm facilitates the estimation of health-state utilities for use within economic evaluations where only PedsQL data is available and is suitable for use in community-based adolescents aged 15-17 years. Applicability of the algorithm in younger populations should be assessed in further research. [ABSTRACT FROM AUTHOR]

Introduction: Mapping algorithms are increasingly being used to predict health-utility values based on responses or scores from non-preference-based measures, thereby informing economic evaluations.Objectives: We explored whether predictions in the EuroQol 5-dimension 3-level instrument (EQ-5D-3L) health-utility gains from mapping algorithms might differ if estimated using differenced versus raw scores, using the Roland-Morris Disability Questionnaire (RMQ), a widely used health status measure for low back pain, as an example.Methods: We estimated algorithms mapping within-person changes in RMQ scores to changes in EQ-5D-3L health utilities using data from two clinical trials with repeated observations. We also used logistic regression models to estimate response mapping algorithms from these data to predict within-person changes in responses to each EQ-5D-3L dimension from changes in RMQ scores. Predicted health-utility gains from these mappings were compared with predictions based on raw RMQ data.Results: Using differenced scores reduced the predicted health-utility gain from a unit decrease in RMQ score from 0.037 (standard error [SE] 0.001) to 0.020 (SE 0.002). Analysis of response mapping data suggests that the use of differenced data reduces the predicted impact of reducing RMQ scores across EQ-5D-3L dimensions and that patients can experience health-utility gains on the EQ-5D-3L 'usual activity' dimension independent from improvements captured by the RMQ.Conclusion: Mappings based on raw RMQ data overestimate the EQ-5D-3L health utility gains from interventions that reduce RMQ scores. Where possible, mapping algorithms should reflect within-person changes in health outcome and be estimated from datasets containing repeated observations if they are to be used to estimate incremental health-utility gains. [ABSTRACT FROM AUTHOR]

Background: Prognostic classification approaches are commonly used in clinical practice to predict health outcomes. However, there has been limited focus on use of the general approach for predicting costs. We applied a grouping algorithm designed for large-scale data sets and multiple prognostic factors to investigate whether it improves cost prediction among older Medicare beneficiaries diagnosed with prostate cancer.Methods: We analysed the linked Surveillance, Epidemiology and End Results (SEER)-Medicare data, which included data from 2000 through 2009 for men diagnosed with incident prostate cancer between 2000 and 2007. We split the survival data into two data sets (D0 and D1) of equal size. We trained the classifier of the Grouping Algorithm for Cancer Data (GACD) on D0 and tested it on D1. The prognostic factors included cancer stage, age, race and performance status proxies. We calculated the average difference between observed D1 costs and predicted D1 costs at 5 years post-diagnosis with and without the GACD.Results: The sample included 110,843 men with prostate cancer. The median age of the sample was 74 years, and 10% were African American. The average difference (mean absolute error [MAE]) per person between the real and predicted total 5-year cost was US$41,525 (MAE US$41,790; 95% confidence interval [CI] US$41,421-42,158) with the GACD and US$43,113 (MAE US$43,639; 95% CI US$43,062-44,217) without the GACD. The 5-year cost prediction without grouping resulted in a sample overestimate of US$79,544,508.Conclusion: The grouping algorithm developed for complex, large-scale data improves the prediction of 5-year costs. The prediction accuracy could be improved by utilization of a richer set of prognostic factors and refinement of categorical specifications. [ABSTRACT FROM AUTHOR]

Background: Cytomegalovirus (CMV) disease may occur following renal transplantation and has been shown to have health and cost consequences in this setting.Objective: To compare the cost effectiveness of different CMV management strategies for renal transplant patients: prophylaxis with (i) oral valaciclovir or (ii) intravenous ganciclovir; viral testing for CMV followed by (iii) pre-emptive therapy with intravenous ganciclovir or (iv) adjustment of immunosuppression and intensive monitoring; or (v) waiting to treat when CMV disease develops.Methods: A decision-tree model was constructed that included the different management strategies for the donor seropositive/recipient seronegative (D+R-) population. Clinical outcomes for the D+R- population came from clinical trials. Treatment algorithms and costs for CMV syndrome and tissue invasive disease were developed from published literature and UK physician interviews. One- and 2-way sensitivity analyses were performed.Study Perspective: UK National Health Service.Results: Prophylaxis with either oral valaciclovir or intravenous ganciclovir dominated (lower costs and fewer cases of CMV disease) the pre-emptive treatment and wait-and-treat strategies. The cost per patient was from 157 Pounds to 438 Pounds higher with oral valaciclovir prophylaxis compared with intravenous ganciclovir prophylaxis and the incremental cost per case of CMV disease avoided with valaciclovir prophylaxis ranged from 2243 Pounds to 8111 Pounds (1996 values). These results are sensitive to the efficacy of intravenous ganciclovir prophylaxis and CMV management costs.Conclusions: For D+R- renal transplant patients, prophylaxis is the dominant (more effective and less costly) management strategy compared with pre-emptive and wait-and-treat strategies. The cost per patient with oral valaciclovir prophylaxis compared with intravenous ganciclovir prophylaxis is slightly higher in our base case scenario, but may be lower under reasonable alternative assumptions. [ABSTRACT FROM AUTHOR]