Target Product Profiles (TPPs) outline the characteristics that new health technologies require to address an unmet clinical need. To date, published TPPs for medical tests have focused on infectious diseases, mostly in the context of low- and middle-income countries. Recently, there have been calls for a broader use of TPPs as a mechanism to ensure that diagnostic innovation is aligned with clinical needs, yet the methodology underpinning TPP development remains suboptimal. Here, we propose that early economic evaluation (EEE) should be integrated within the TPP methodology to create a more rigorous framework for the development of “fit-for-purpose” tests. We discuss the potential benefits that EEE could bring to the core activities underpinning TPP development—scoping, drafting, consensus building, and updating—and argue that using EEE to help inform TPPs provides a more objective, evidence-based, and transparent approach to defining test specifications.

The notion of “economic development” dominates aid policy. A nation’s per capita Gross Domestic Product (gdp) determines whether that country is considered developed or less developed, and the standard measure of any developmental progress is gdp growth. This article investigates the evolution of the concept of economic development, as it emerged from a specific combination of modeling, statistics and political circumstances. In this story, Arthur Lewis played a decisive role, but only by building upon Colin Clark’s first global national income statistics, an indispensible foundation for Lewis’s seminal model of economic development. This model was embraced by policy makers longing for a theoretical framework to clarify and operationalize the hitherto vague concept of development. More importantly, however, the statistical indicator on which Lewis based his theory had already been universally accepted. In other words: statistics came before theory. This holds important lessons for alternative development ideas. It explains why the idea of economic development remains so firmly entrenched and suggests the conditions that might be necessary for an alternative theory to take hold.

Objectives: When incorporating treatment effect estimates derived from a random-effect meta-analysis it is tempting to use the confidence bounds to determine the potential range of treatment effect. However, prediction intervals reflect the potential effect of a technology rather than the more narrowly defined average treatment effect. Using a case study of robot-assisted radical prostatectomy, this study investigates the impact on a cost-utility analysis of using clinical effectiveness derived from random-effects meta-analyses presented as confidence bounds and prediction intervals, respectively.

Methods: To determine the cost-utility of robot-assisted prostatectomy, an economic model was developed. The clinical effectiveness of robot-assisted surgery compared with open and conventional laparoscopic surgery was estimated using meta-analysis of peer-reviewed publications. Assuming treatment effect would vary across studies due to both sampling variability and differences between surgical teams, random-effects meta-analysis was used to pool effect estimates.

Results: Using the confidence bounds approach the mean and median ICER was €24,193 and €26,731/QALY (95%CI: €13,752 to €68,861/QALY), respectively. The prediction interval approach produced an equivalent mean and median ICER of €26,920 and €26,643/QALY (95%CI: -€135,244 to €239,166/QALY), respectively. Using prediction intervals, there is a probability of 0.042 that robot-assisted surgery will result in a net reduction in QALYs.

Conclusions: Using prediction intervals rather than confidence bounds does not affect the point estimate of the treatment effect. In meta-analyses with significant heterogeneity, the use of prediction intervals will produce wider ranges of treatment effect, and hence result in greater uncertainty, but a better reflection of the effect of the technology.

Optimization models have often been useful in attempting to understand the adaptive significance of behavioral traits. Originally such models were applied to isolated aspects of behavior, such as foraging, mating, or parental behavior. In reality, organisms live in complex, ever-changing environments, and are simultaneously concerned with many behavioral choices and their consequences. This target article describes a dynamic modeling technique that can be used to analyze behavior in a unified way. The technique has been widely used in behavioral studies of insects, fish, birds, mammals, and other organisms. The models use biologically meaningful parameters and variables, and lead to testable predictions. Limitations arise because nature's complexity always exceeds our modeling capacity.

Aims – To obtain a new, well-balanced mental health funding system, through the creation of i) a list of psychiatric interventions provided by Italian Community-based Psychiatric Services (CPS), and associated costs; ii) a new prospective funding system for patients with a high use of resources, based on packages of care. Methods – Five Italian Community-based Psychiatric Services collected data from 1250 patients during October 2002. Socio-demographical and clinical characteristics and GAF scores were collected at baseline. All psychiatric contacts during the following six months were registered and categorised into 24 service contact types. Using elasticity equation and contact characteristics, we estimate the costs of care. Cluster analysis techniques identified packages of care. Logistic regression defined predictive variables of high use patients. Multinomial Logistic Model assigned each patient to a package of care. Results – The sample's socio-demographic characteristics are similar, but variations exist between the different CPS. Patients were then divided into two groups, and the group with the highest use of resources was divided into three smaller groups, based on number and type of services provided. Conclusions – Our findings show how is possible to develop a cost predictive model to assign patients with a high use of resources to a group that can provide the right level of care. For these patients it might be possible to apply a prospective per-capita funding system based on packages of care.

Declaration of Interest: None

Aim – To develop predictive models to allocate patients into frequent and low service users groups within the Italian Community-based Mental Health Services (CMHSs). To allocate frequent users to different packages of care, identifing the costs of these packages. Methods – Socio-demographic and clinical data and GAF scores at baseline were collected for 1250 users attending five CMHSs. All psychiatric contacts made by these patients during six months were recorded. A logistic regression identified frequent service users predictive variables. Multinomial logistic regression identified variables able to predict the most appropriate package of care. A cost function was utilised to estimate costs. Results – Frequent service users were 49%, using nearly 90% of all contacts. The model classified correctly 80% of users in the frequent and low users groups. Three packages of care were identified: Basic Community Treatment (4,133 Euro per six months); Intensive Community Treatment (6,180 Euro) and Rehabilitative Community Treatment (11,984 Euro) for 83%, 6% and 11% of frequent service users respectively. The model was found to be accurate for 85% of users. Conclusion – It is possible to develop predictive models to identify frequent service users and to assign them to pre-defined packages of care, and to use these models to inform the funding of psychiatric care.

Background: Clearly the benefits of a treatment must not be outweighed by the adverse effects. If researchers fail to incorporate adverse effects adequately in models, this could limit the validity of the results obtained. In the worst case, interventions that are cost-effective may be shown not to be. The aim of this research was to review current practice when incorporating adverse effects in economic models.

Methods: A survey of HTA reports commissioned by the National Institute for Health Research (NIHR) Health Technology Assessment programme, published between 2004 and 2007 was conducted. All reports which investigated the clinical and cost-effectiveness of a health technology using a systematic review and an economic model framework were included.

Results: A total of eighty reports met the inclusion criteria. Of the models including adverse effects (43/80), 67 percent used a clinical adverse effects parameter, 79 percent a cost of adverse effects parameter, 86 percent used one of these, and 60 percent used both. Of the thirty-seven models that did not include adverse effects, eighteen justified this omission, most commonly lack of data; nineteen appeared to make no explicit consideration of adverse effects in the model.

Conclusions: In many cases, poor reporting made it difficult to ascertain if there had been any consideration of adverse effects. We suggest that the findings of this survey support a call for much clearer and explicit reporting of adverse effects, or their exclusion, in decision models and for explicit recognition in future guidelines that “all relevant outcomes” should include some consideration of adverse events.

Maximization theory, which is borrowed from economics, provides techniques for predicing the behavior of animals - including humans. A theoretical behavioral space is constructed in which each point represents a given combination of various behavioral alternatives. With two alternatives - behavior A and behavior B - each point within the space represents a certain amount of time spent performing behavior A and a certain amount of time spent performing behavior B. A particular environmental situation can be described as a constraint on available points (a circumscribed area) within the space. Maximization theory assumes that animals always choose the available point with the highest numerical value. The task of maximization theory is to assign to points in the behavioral space values that remain constant across various environmental situations; as those situations change, the point actually chosen is always the one with the highest assigned value.

Maximization theory is an alternative to reinforcement theory as a description of steady-state behavior. Situations to which reinforcement theory has been directly applied (such as reinforcement of rats pressing levers and pigeons pecking keys in Skinner boxes) and situations to which reinforcement theory has occasionally been extended (such as human economic behavior and human self-control) can be described by maximization theory. This approach views behavior as a quantitative outcome of the interaction of the putative instrumental response, the reinforcer, and the other activities available in the situation. It provides new insight into these situations and, because it takes context into account, has greater predictive power than reinforcement theory.

Many new hip prostheses are now available for use in total hip replacement. The majority remain untested relative to standard prostheses; however, many new prostheses are substantially more costly. We examine how much more effective new prostheses must be, in terms of reducing the need for revision operations, in order to justify this increased cost.

Seasonality has been a major research area in economics for several decades. The paper assesses the recent development in the literature on the treatment of seasonality in economics, and divides it into three interrelated groups. The first group, pure noise models, consists of methods based on the view that seasonality is noise contaminating the data or, more correctly, contaminating the information of interest for the economists. The second group, time-series models, treats seasonality as a more integrated part of the modeling strategy, with the choice of model being data driven. The third group, economic models of seasonality, introduces economic theory, that is, optimizing behavior, into the modeling of seasonality.