Informing the efficient use of health care and health care research resources - the case of screening for abdominal aortic aneurysm in Sweden

Computing Expected Value of Partial Sample Information from Probabilistic Sensitivity Analysis Using Linear Regression Metamodeling

Decision makers often desire both guidance on the most cost-effective interventions given current knowledge and also the value of collecting additional information to improve the decisions made (i.e., from value of information [VOI] analysis). Unfortunately, VOI analysis remains underused due to the conceptual, mathematical, and computational challenges of implementing Bayesian decision-theoretic approaches in models of sufficient complexity for real-world decision making. In this study, we propose a novel practical approach for conducting VOI analysis using a combination of probabilistic sensitivity analysis, linear regression metamodeling, and unit normal loss integral function--a parametric approach to VOI analysis. We adopt a linear approximation and leverage a fundamental assumption of VOI analysis, which requires that all sources of prior uncertainties be accurately specified. We provide examples of the approach and show that the assumptions we make do not induce substantial bias but greatly reduce the computational time needed to perform VOI analysis. Our approach avoids the need to analytically solve or approximate joint Bayesian updating, requires only one set of probabilistic sensitivity analysis simulations, and can be applied in models with correlated input parameters.

A practical guide to value of information analysis

Value of information analysis is a quantitative method to estimate the return on investment in proposed research projects. It can be used in a number of ways. Funders of research may find it useful to rank projects in terms of the expected return on investment from a variety of competing projects. Alternatively, trialists can use the principles to identify the efficient sample size of a proposed study as an alternative to traditional power calculations, and finally, a value of information analysis can be conducted alongside an economic evaluation as a quantitative adjunct to the 'future research' or 'next steps' section of a study write up. The purpose of this paper is to present a brief introduction to the methods, a step-by-step guide to calculation and a discussion of issues that arise in their application to healthcare decision making. Worked examples are provided in the accompanying online appendices as Microsoft Excel spreadsheets.

A systematic and critical review of the evolving methods and applications of value of information in academia and practice

OBJECTIVE

This article provides a systematic and critical review of the evolving methods and applications of value of information (VOI) in academia and practice and discusses where future research needs to be directed.

METHODS

Published VOI studies were identified by conducting a computerized search on Scopus and ISI Web of Science from 1980 until December 2011 using pre-specified search terms. Only full-text papers that outlined and discussed VOI methods for medical decision making, and studies that applied VOI and explicitly discussed the results with a view to informing healthcare decision makers, were included. The included papers were divided into methodological and applied papers, based on the aim of the study.

RESULTS

A total of 118 papers were included of which 50 % (n = 59) are methodological. A rapidly accumulating literature base on VOI from 1999 onwards for methodological papers and from 2005 onwards for applied papers is observed. Expected value of sample information (EVSI) is the preferred method of VOI to inform decision making regarding specific future studies, but real-life applications of EVSI remain scarce. Methodological challenges to VOI are numerous and include the high computational demands, dealing with non-linear models and interdependency between parameters, estimations of effective time horizons and patient populations, and structural uncertainties.

CONCLUSION

VOI analysis receives increasing attention in both the methodological and the applied literature bases, but challenges to applying VOI in real-life decision making remain. For many technical and methodological challenges to VOI analytic solutions have been proposed in the literature, including leaner methods for VOI. Further research should also focus on the needs of decision makers regarding VOI.

[Using value of information analysis in decision making about applied research. The case of genetic screening for hemochromatosis in Germany]

Public decision makers face demands to invest in applied research in order to accelerate the adoption of new genetic tests. However, such an investment is profitable only if the results gained from further investigations have a significant impact on health care practice. An upper limit for the value of additional information aimed at improving the basis for reimbursement decisions is given by the expected value of perfect information (EVPI). This study illustrates the significance of the concept of EVPI on the basis of a probabilistic cost-effectiveness model of screening for hereditary hemochromatosis among German men. In the present example, population-based screening can barely be recommended at threshold values of 50,000 or 100,000 Euro per life year gained and also the value of additional research which might cause this decision to be overturned is small: At the mentioned threshold values, the EVPI in the German public health care system was ca. 500,000 and 2,200,000 Euro, respectively. An analysis of EVPI by individual parameters or groups of parameters shows that additional research about adherence to preventive phlebotomy could potentially provide the highest benefit. The potential value of further research also depends on methodological assumptions regarding the decision maker's time horizon as well as on scenarios with an impact on the number of affected patients and the cost-effectiveness of screening.

Cost-effective diagnostic cardiovascular imaging: when does it provide good value for the money?

To summarize the results of all original cost-utility analyses (CUAs) in diagnostic cardiovascular imaging (CVI) and characterize those technologies by estimates of their cost-effectiveness. We systematically searched the literature for original CVI CUAs published between 2000 and 2008. Studies were classified according to several variables including anatomy of interest (e.g. cerebrovascular, aorta, peripheral) and imaging modality under study (e.g. angiography, ultrasound). The results of each study, expressed as cost of the intervention to number of quality-adjusted life years saved ratio (cost/QALY) were additionally classified as favorable or not using $20,000, $50,000, and $100,000 per QALY thresholds. The distribution of results was assessed with Chi Square or Fisher exact test, as indicated. Sixty-nine percent of all cardiovascular imaging CUAs were published between 2000 and 2008. Thirty-two studies reporting 82 cost/QALY ratios were included in the final sample. The most common vascular areas studied were cerebrovascular (n = 9) and cardiac (n = 8). Sixty-six percent (21/32) of studies focused on sonography, followed by conventional angiography and CT (25%, n = 8, each). Twenty-nine (35.4%), 42 (51.2%), and 53 (64.6%) ratios were favorable at WTP $20,000/QALY, $50,000/QALY, and $100,000/QALY, respectively. Thirty (36.6%) ratios compared one imaging test versus medical or surgical interventions; 26 (31.7%) ratios compared imaging to a different imaging test and another 26 (31.7%) to no intervention. Imaging interventions were more likely (P < 0.01) to be favorable when compared to observation, medical treatment or non-intervention than when compared to a different imaging test at WTP $100,000/QALY. The diagnostic cardiovascular imaging literature has growth substantially. The studies available have, in general, favorable cost-effectiveness profiles with major determinants relating to being compared against observation, medical or no intervention instead of other imaging tests.

A typology of health marketing research methods--combining public relations methods with organizational concern

Research plays a critical role throughout virtually every conduit of the health services industry. The key terms of research, public relations, and organizational interests are discussed. Combining public relations as a strategic methodology with the organizational concern as a factor, a typology of four different research methods emerges. These four health marketing research methods are: investigative, strategic, informative, and verification. The implications of these distinct and contrasting research methods are examined.