scholarly journals CHEERS to Updated Guidelines for Reporting Health Economic Evaluations!

2022 ◽  
Vol 25 (1) ◽  
pp. 1-2
Author(s):  
Richard J. Willke ◽  
Laura T. Pizzi
Keyword(s):  
Health Economic ◽  
Economic Evaluations

scholarly journals Health economic measures in schizophrenia research

2007 ◽  
Vol 191 (S50) ◽  
pp. s42-s45 ◽  
Author(s):  
Paul McCrone
Keyword(s):  
Health Economic ◽  
Economic Evaluation ◽  
Cost Utility ◽  
Outcome Data ◽  
Cost Utility Analysis ◽  
Economic Evaluations ◽  
Utility Analysis ◽  
Short Term ◽  
Effectiveness Analysis

BackgroundIt is essential in economic evaluations of schizophrenia interventions that all relevant costs are identified and measured appropriately Also of importance is the way in which cost data are combined with information on outcomesAimsTo examine the use of health economicsin evaluations of interventions for schizophreniaMethodsAreview of the key methods used to estimate costs and to link costs and outcomes was conductedResultsCosts fall on a number of different agencies and can be short term or long term. Cost-effectiveness analysis and cost-utility analysis are the most appropriate methods for combing cost and outcome dataConclusionsSchizophrenia poses a number of challenges for economic evaluation

scholarly journals Standard Costs for Health Economic Evaluations: An International Comparison

2014 ◽  
Vol 17 (7) ◽  
pp. A427 ◽  
Author(s):  
S. Mostardt ◽  
F.G. Sandmann ◽  
A. Seidl ◽  
M. Zhou ◽  
A.U. Gerber-Grote
Keyword(s):  
Health Economic ◽  
International Comparison ◽  
Economic Evaluations

scholarly journals Noncompliance and Missing Data in Health Economic Evaluation

2019 ◽  
Author(s):  
Karla DiazOrdaz ◽  
Richard Grieve
Keyword(s):  
Health Economic ◽  
Economic Evaluation ◽  
Missing Data ◽  
Causal Effect ◽  
Health Economic Evaluation ◽  
Specific Treatment ◽  
Model Specification ◽  
Sensitivity Analyses ◽  
Economic Evaluations ◽  
Random Assignment

Health economic evaluations face the issues of noncompliance and missing data. Here, noncompliance is defined as non-adherence to a specific treatment, and occurs within randomized controlled trials (RCTs) when participants depart from their random assignment. Missing data arises if, for example, there is loss-to-follow-up, survey non-response, or the information available from routine data sources is incomplete. Appropriate statistical methods for handling noncompliance and missing data have been developed, but they have rarely been applied in health economics studies. Here, we illustrate the issues and outline some of the appropriate methods with which to handle these with application to health economic evaluation that uses data from an RCT. In an RCT the random assignment can be used as an instrument-for-treatment receipt, to obtain consistent estimates of the complier average causal effect, provided the underlying assumptions are met. Instrumental variable methods can accommodate essential features of the health economic context such as the correlation between individuals’ costs and outcomes in cost-effectiveness studies. Methodological guidance for handling missing data encourages approaches such as multiple imputation or inverse probability weighting, which assume the data are Missing At Random, but also sensitivity analyses that recognize the data may be missing according to the true, unobserved values, that is, Missing Not at Random. Future studies should subject the assumptions behind methods for handling noncompliance and missing data to thorough sensitivity analyses. Modern machine-learning methods can help reduce reliance on correct model specification. Further research is required to develop flexible methods for handling more complex forms of noncompliance and missing data.

scholarly journals Strategies for newborn screening for cystic fibrosis: A systematic review of health economic evaluations

2018 ◽  
Vol 17 (3) ◽  
pp. 306-315 ◽  
Author(s):  
Masja Schmidt ◽  
Amber Werbrouck ◽  
Nick Verhaeghe ◽  
Elke De Wachter ◽  
Steven Simoens ◽  
...  
Keyword(s):  
Systematic Review ◽  
Cystic Fibrosis ◽  
Health Economic ◽  
Newborn Screening ◽  
Economic Evaluations

Sponsorship Bias in Base-Case Values and Uncertainty Bounds of Health Economic Evaluations? A Systematic Review of Herpes Zoster Vaccination

2018 ◽  
Vol 38 (6) ◽  
pp. 730-745 ◽  
Author(s):  
Joke Bilcke ◽  
Frederik Verelst ◽  
Philippe Beutels
Keyword(s):  
Health Economic ◽  
Herpes Zoster ◽  
Incremental Cost ◽  
Quality Score ◽  
Data Sources ◽  
Economic Evaluations ◽  
Base Case ◽  
Study Quality ◽  
Vaccine Protection ◽  
Utility Loss

Background. New health technologies are more likely adopted when they have lower incremental cost-effectiveness ratios (ICERs) and/or when their ICER is presented with more certainty. Industry-funded (IF) health economic evaluations use often more favorable base-case values, leading to more favorable conclusions. Purpose. To study whether IF health economic evaluations of varicella-zoster virus vaccination in the elderly use more favorable base-case values and account for less uncertainty than non–industry-funded (NIF) evaluations. Methods. Data source: PubMed. Data extracted: funding source; incremental cost per quality-adjusted life year (QALY) gained; vaccine price; study quality score; base-case values, uncertainty ranges, and data sources for influential parameters: duration of vaccine protection, utility loss due to herpes zoster (HZ) disease, percentage of HZ patients developing postherpetic neuralgia (PHN), and duration of PHN. Data synthesis: qualitative comparisons; Fisher exact test for differences in study quality score and 1-sided Mann-Whitney U tests for differences in base-case values and uncertainty ranges. Results. Despite using the same data sources, IF studies ( n = 10) assume a longer duration of vaccine protection ( U = 56, P = 0.03), have a higher percentage of HZ patients developing PHN ( U = 22/33, P = 0.02/0.03 for ages 60–64/65–69), and tend to use higher HZ utility loss than NIF studies ( n = 11) for their baseline. IF studies show lower ICERs given similar or even higher vaccine prices than NIF studies, consider less uncertainty around the duration of vaccine protection ( U = 8, P < 0.001), and tend to use less uncertainty around the duration of PHN. Yet their quality has been rated equally well, using current standard quality rating tools. Conclusion. Researchers and decision makers should be aware of potential sponsorship bias in health economic evaluations, especially in the way source data are used to specify base-case values and uncertainty ranges.

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