Dealing with Differences: Different populations, data sources and countries in HTA modelling

__Abstract__ At its heart, health technology assessment (HTA) is very simple. It compares two or more alternative courses of action, often pharmaceutical interventions, in terms of both their costs and health outcomes.1 One of the interventions will have better health outcomes, for example fewer number of exacerbations, longer survival or a better quality of life. This usually comes at an extra cost, often in the way of a higher price for the intervention. HTA makes this exchange between costs and effects explicit. The idea that costs are an important element to take into account, does not come naturally to many health care workers. Doctors, nurses, and other health care workers do everything they can to help patients improve their lives. The interventions these patients need are provided in a large part by companies developing and producing the necessary drugs and devices. Health care scientists and epidemiologists try to make sense of what constitutes health, what illness is and how disease is spread. Their focus is purely on the patient: what does he or she need? Choices between treatment options are usually a consideration between availability, possible side effects, and patient characteristics. If a new medication comes on the market, doctors are often eager to treat patients with this newest treatment option. With the focus on the patient in front of them, health care workers usually do not look beyond the operating room or treatment room. An oncologist wants to treat all patients to the best of his or her ability, no matter the costs of the intervention. Budgetary constraints are not, and should not, be part of the decision making process of a health care worker when dealing with an individual patient. Cost considerations should be taken into account at a more aggregate level in the clinical guidelines, written by their organizations. In this way, HTA separates health care workers from these concerns in their daily practice, which are in the public and political domain

Mix and match. A simulation study on the impact of mixed-Treatment comparison methods on health-economic outcomes

Background Decision-Analytic cost-effectiveness (CE) models combine many parameters, often obtained after meta-Analysis. Aim We compared different methods of mixed-Treatment comparison (MTC) to combine transition and event probabilities derived from several trials, especially with respect to health-economic (HE) outcomes like (quality adjusted) life years and costs. Methods Trials were drawn from a simulated reference population, comparing two of four fictitious interventions. The goal was to estimate the CE between two of these. The amount of heterogeneity between trials was varied in scenarios. Parameter estimates were combined using direct comparison, MTC methods proposed by Song and Puhan, and Bayesian generalized linear fixed effects (GLMFE) and random effects models (GLMRE). Parameters were entered into a Markov model. Parameters and HE outcomes were compare

AdViSHE: A Validation-Assessment Tool of Health-Economic Models for Decision Makers and Model Users

Background: A trade-off exists between building confidence in health-economic (HE) decision models and the use of scarce resources. We aimed to create a practical tool provid

Let's go back to work: survival analysis on the return-to-work after depression

Absence from work due to mental disorders is substantial. Additionally, long-term absence from work is associated with a reduced probability of return-to-work (RTW). Major depressive disorder (MDD) is a prevalent condition in Dutch occupational health care settings. An early estimate of the prognosis regarding RTW in patients with MDD could serve both as a point of departure for the identification of high-risk cases and as an instrument to monitor the course of the disorder and of RTW. In the current study, we aimed to assess the added value of health-related quality of life (HRQoL) and severity of depression to predict the time to RTW

The Missing Stakeholder Group: Why Patients Should be Involved in Health Economic Modelling

Evaluations of healthcare interventions, e.g. new drugs or other new treatment strategies, commonly include a cost-effectiveness analysis (CEA) that is based on the application of health economic (HE) models. As end users, patients are important stakeholders regarding the outcomes of CEAs, yet their knowledge of HE model development and application, or their involvement therein, is absent. This paper considers possible benefits and risks of patient involvement in HE model development and application for modellers and patients. An exploratory review of the literature has been performed on stakeholder-involved modelling in various disciplines. In addition, Dutch patient experts have been interviewed about their experience in, and opinion about, the application of HE models. Patients have little to no knowledge of HE models and are seldom involved in HE model development and application. Benefits of becoming involved would include a greater understanding and possible acceptance by patients of HE model application, improved model validation, and a more direct infusion of patient expertise. Risks would include patient bias and increased costs of modelling. Patient involvement in HE modelling seems to carry several benefits as well as risks. We claim that the benefits may outweigh the risks and that patients should become involved

Research Costs Investigated: A Study Into the Budgets of Dutch Publicly Funded Drug-Related Research

Background: The costs of performing research are an important input in value of information (VOI) analyses but are difficult to assess. Objective: The aim of this study was to investigate the costs of research, serving two purposes: (1) estimating research costs for use in VOI analyses; and (2) developing a costing tool to support reviewers of grant proposals in assessing whether the proposed budget is realistic. Methods: For granted study proposals from the Netherlands Organization for Health Research and Development (ZonMw), type of study, potential cost drivers, proposed budget, and general characteristics were extracted. Regression analysis was conducted in an attempt to generate a ‘predicted budget’ for certain combinations of cost drivers, for implementation in the costing tool. Results: Of 133 drug-related research grant proposals, 74 were included for complete data extraction. Because an association between cost drivers and budgets was not confirmed, we could not generate a predicted budget based on regression analysis, but only historic reference budgets given certain study characteristics. The costing tool was designed accordingly, i.e. with given selection criteria the tool returns the range of budgets in comparable studies. This range can be used in VOI analysis to estimate whether the expected net benefit of sampling will be positive to decide upon the net value of future research. Conclusion: The absence of association between study characteristics and budgets may indicate inconsistencies in the budgeting or granting process. Nonetheless, the tool generates useful information on historical budgets, and the option to formally relate VOI to budgets. To our knowledge, this is the first attempt at creating such a tool, which can be complemented with new studies being granted, enlarging the underlying database and keeping estimates up to date