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Background: Value of information analysis has been proposed as an alternative to the standard hypothesis testing approach, which is based on type I and type II errors, in determining sample sizes for randomized clinical trials. However, in addition to sample size calculation, value of information analysis can optimize other aspects of research design such as possible comparator arms and alternative follow-up times, by considering trial designs that maximize the expected net benefit of research, which is the difference between the expected cost of the trial and the expected value of additional information. Purpose: To apply value of information methods to the results of a pilot study on catheter securement devices to determine the optimal design of a future larger clinical trial. Methods: An economic evaluation was performed using data from a multi-arm randomized controlled pilot study comparing the efficacy of four types of catheter securement devices: standard polyurethane, tissue adhesive, bordered polyurethane and sutureless securement device. Probabilistic Monte Carlo simulation was used to characterize uncertainty surrounding the study results and to calculate the expected value of additional information. To guide the optimal future trial design, the expected costs and benefits of the alternative trial designs were estimated and compared. Results: Analysis of the value of further information indicated that a randomized controlled trial on catheter securement devices is potentially worthwhile. Among the possible designs for the future trial, a four-arm study with 220 patients/arm would provide the highest expected net benefit corresponding to 130% return-on-investment. The initially considered design of 388 patients/arm, based on hypothesis testing calculations, would provide lower net benefit with return-on-investment of 79%. Limitations: Cost-effectiveness and value of information analyses were based on the data from a single pilot trial which might affect the accuracy of our uncertainty estimation. Another limitation was that different follow-up durations for the larger trial were not evaluated. Conclusion: The value of information approach allows efficient trial design by maximizing the expected net benefit of additional research. This approach should be considered early in the design of randomized clinical trials.

Background: Value of information (VOI) analysis quantifies the value of additional research in reducing decision uncertainty. It addresses adoption and research decisions simultaneously by comparing the expected benefits and costs of research studies. Nevertheless, the application of this approach in practice remains limited. Objectives: To apply VOI analysis in health care interventions to guide adoption decisions, optimize trial design, and prioritize research. Methods: The analysis was from the perspective of Queensland Health, Australia. It included four interventions: clinically indicated catheter replacement, tissue adhesive for securing catheters, negative pressure wound therapy (NPWT) in caesarean sections, and nutritional support for preventing pressure ulcers. For each intervention, cost-effectiveness analysis was performed, decision uncertainty characterized, and VOI calculated using Monte Carlo simulations. The benefits and costs of additional research were considered together with the costs and consequences of acting now versus waiting for more information. All values are reported in 2014 Australian dollars (AU$). Results: All interventions were cost-effective, but with various levels of decision uncertainty. The current evidence is sufficient to support the adoption of clinically indicated catheter replacement. For the tissue adhesive, an additional study before adoption is worthwhile with a four-arm trial of 220 patients per arm. Additional research on NPWT before adoption is worthwhile with a two-arm trial of 200 patients per arm. Nutritional support should be adopted with a two-arm trial of 1200 patients per arm. Based on the expected net monetary benefits, the studies were ranked as follows: 1) NPWT (AU$1.2 million), 2) tissue adhesive (AU$0.3 milliion), and 3) nutritional support (AU$0.1 million). Conclusions: VOI analysis is a useful and practical approach to inform adoption and research decisions. Efforts should be focused on facilitating its integration into decision making frameworks.

Objectives:The composition of the medical costs incurred by people treated for basal cell and squamous cell carcinomas (hereafter keratinocyte cancers) is not adequately understood. We sought to compare the medical costs of individuals with or without keratinocyte cancers.Methods:We used national health insurance data to analyze the direct medical costs of 2000 cases and 2000 controls nested within the QSkin prospective cohort study (n = 43,794) conducted in Australia. We reconstructed the medical history of patients using medical and pharmaceutical item codes and then compared the health service costs of individuals treated for keratinocyte cancers with those not treated for keratinocyte cancers.Results:Individuals treated for keratinocyte cancers consumed on average AUD$1320 per annum more in medical services than those without keratinocyte cancers. Only 23.2% of costs were attributed to the explicit treatment of keratinocyte cancers. The principal drivers of the residual costs were medical attendances, surgical procedures on the skin, and histopathology services. We found significant positive associations between history of treatment for keratinocyte cancers with treatments for other health conditions, including melanoma, cardiovascular disease, lipidemia, osteoporosis, rheumatoid arthritis, colorectal cancer, prostate cancer, and tuberculosis.Conclusion:Individuals treated for keratinocyte cancers have substantially higher medical costs overall than individuals without keratinocyte cancers. The direct costs of skin cancer excision account for only one-fifth of this difference.

Objective: To assess the evidence for the cost-effectiveness of health behaviour interventions that address the major behavioural risk factors for chronic disease, including smoking, physical inactivity, poor diet, and alcohol misuse. Methods: Medical and economic databases were searched for relevant economic evaluations. Studies were critically appraised using a published 35-point checklist, and the results are described using a narrative approach, noting methodological limitations. The review included 64 studies from 1995—2005, including 17 reports on multiple behaviour interventions. Results: There was considerable variation among the studies by target populations, intervention components, primary outcomes, and economic methods, but the reported incremental cost-effectiveness ratios were consistently low (e.g. Discussion: In general, the results of this review demonstrate favourable cost-effectiveness for smoking interventions, physical activity interventions and multiple behaviour interventions in high-risk groups. Although alcohol and dietary interventions appeared to be economically favourable, it is difficult to draw conclusions because of the variety in study outcomes. However, methodological limitations weaken the generalizability of findings, and suggest that the results of any given study should be considered carefully when being used to inform resource allocation.

Background: We describe an approach to estimating the cost-effectiveness of an intervention that changes health behaviour. The method captures the lifetime costs and benefits incurred by participants in an ongoing cluster-randomized controlled trial of an intervention that aims to change health behaviour. The existing literature only captures short-term economic and health outcomes. Methods: We develop a state-transition Markov model of how individuals move between different health behaviour states over time. We simulate hypothetical data to describe the costs and health benefits of the intervention, illustrate how the data collected in the ongoing randomized controlled trial can be used and demonstrate how incremental cost-effectiveness ratios are estimated. Results: On the basis of the simulated (i.e. hypothetical) data, we estimate the cost per quality-adjusted life year. The estimate reflects the lifetime health and economic consequences of the intervention. Discussion: The method used for the cost-effectiveness analysis described in this paper is appropriate for investigating whether interventions that change health behaviour in relation to chronic diseases represent good value for money as compared to alternative uses of scarce healthcare resources.