Determining value in health technology assessment: Stay the course or tack away?

The economic evaluation of new health technologies to assess whether the value of the expected health benefits warrants the proposed additional costs has become an essential step in making novel interventions available to patients. This assessment of value is problematic because there exists no natural means to measure it. One approach is to assume that society wishes to maximize aggregate health, measured in terms of quality-adjusted life-years (QALYs). Commonly, a single 'cost-effectiveness' threshold is used to gauge whether the intervention is sufficiently efficient in doing so. This approach has come under fire for failing to account for societal values that favor treating more severe illness and ensuring equal access to resources, regardless of pre-existing conditions or capacity to benefit. Alternatives involving expansion of the measure of benefit or adjusting the threshold have been proposed and some have advocated tacking away from the cost per QALY entirely to implement therapeutic area-specific efficiency frontiers, multicriteria decision analysis or other approaches that keep the dimensions of benefit distinct and value them separately. In this paper, each of these alternative courses is considered, based on the experiences of the authors, with a view to clarifying their implications

Biomedical cloud computing with Amazon Web Services.

In this overview to biomedical computing in the cloud, we discussed two primary ways to use the cloud (a single instance or cluster), provided a detailed example using NGS mapping, and highlighted the associated costs. While many users new to the cloud may assume that entry is as straightforward as uploading an application and selecting an instance type and storage options, we illustrated that there is substantial up-front effort required before an application can make full use of the cloud's vast resources. Our intention was to provide a set of best practices and to illustrate how those apply to a typical application pipeline for biomedical informatics, but also general enough for extrapolation to other types of computational problems. Our mapping example was intended to illustrate how to develop a scalable project and not to compare and contrast alignment algorithms for read mapping and genome assembly. Indeed, with a newer aligner such as Bowtie, it is possible to map the entire African genome using one m2.2xlarge instance in 48 hours for a total cost of approximately $48 in computation time. In our example, we were not concerned with data transfer rates, which are heavily influenced by the amount of available bandwidth, connection latency, and network availability. When transferring large amounts of data to the cloud, bandwidth limitations can be a major bottleneck, and in some cases it is more efficient to simply mail a storage device containing the data to AWS (http://aws.amazon.com/importexport/). More information about cloud computing, detailed cost analysis, and security can be found in references

A summary of AWS pricing for basic computation, storage, and data transfer.

<p>Prices are current as of 7/05/11.</p>1<p>CPUs are in terms of a 1-GHz Opteron 2007 processor, unless otherwise noted. For example, a machine with four 1-GHz processors would be listed as 4×1.</p>1a<p>CPU is a quad-core Xeon X5570, i.e., two quad-core CPUs, where each core is 4.19 GHz.</p>1b<p>CPU is a quad-core Xeon X5570, and instance includes two NVIDIA Tesla "Fermi" M2050 GPUs.</p>2<p>Costs reflect standard EC2 use with Linux OS. Costs increase when using Windows and decrease when using Reserved Instances (up-front payment) or Spot Instances (user-specified price on unused EC2 capacity).</p>3<p>Within same AWS availability region (e.g., AWS US-East).</p>4<p>Request costs are more difficult to estimate, and are usually more pertinent when databases and other similar services are involved. Programs like IOSTAT can be used to estimate EBS requests.</p

Step-wise framework for creating a scalable NGS computing application.

<p>Using your local computer, ssh into an instance running in AWS. The costs are representative of actual development time, data transfer into and out of the cloud, and the compute time using AWS (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002147#pcbi-1002147-t001" target="_blank">Table 1</a>). The costs presented may vary, as AWS frequently updates their pricing structure. (A) An additional 3 hours were included for installing programs and testing the instance for the prototyping phase. (B) An additional 2 hours were included in developing the scalable application to learn how to use the cluster management software. (C) For the final scaled application, we used a 38-instance cluster.</p