Recent Developments in Decision-Analytic Modelling for Economic Evaluation

The past few years have seen rapid changes in the methods of decision-analytic modelling of healthcare programmes for the purposes of economic evaluation. This paper focuses on four developments in modelling that have emerged over the past few years or have become more widely used.First, no one optimal method for extrapolating outcomes from clinical trials has yet been established. Modellers may draw from a set of varied assumptions about survival extrapolation that encompass a range of possibilities from highly optimistic to extremely cautious.Secondly, the practicality and appeal of microsimulation as a method for analysing healthcare decision problems has increased dramatically with the speed of computing technology. Individual instantiations of a system are generated by using a random process to draw from probability distributions a large number of times (also known as Monte Carlo or probabilistic simulation). Microsimulation is moving in new directions, such as discrete-event simulations that simulate sequences of events by drawing directly from probability distributions of event times; this approach is now being broadly applied to model situations where populations of patients interact with healthcare delivery systems. Microsimulation modelling of transmission systems at the population level is also rapidly developing.Thirdly, model calibration is emerging as a new tool that may offer health scientists a means of generating important fundamental knowledge about disease processes. Model calibration allows evidence synthesis in which observations on observable quantities are used to draw inferences about unobservable quantities. The methodology of model calibration has advanced considerably, drawing on theories of numerical analysis and mathematical programming such as gradient methods, intelligent grid search algorithms, and many more.As a fourth issue, an area of extraordinary activity is in the use of transmission models to analyse interventions for infectious diseases, including population-wide effects of vaccination. Transmission models use differential equations to simulate, deterministically for the most part, transitions among infection-related health states. Only recently have modelling methodologies been combined so that cost-effectiveness analyses can consider explicitly not only the patient-level benefits of interventions but also the secondary benefits through transmission dynamics.Advances in technology allow more realistic and complex healthcare models to be simulated more rapidly. However, decision makers will not readily accept results from models unless they can understand them intuitively and explain them to others in relatively simple terms. The challenge for the next generation of modellers is not only to harness the power available from these newly accessible methods, but also to extract from the new generation of models the insights that will have the power to influence decision makers.

[1]  Natasha K. Stout,et al.  Chapter 7: The Wisconsin Breast Cancer Epidemiology Simulation Model , 2006 .

[2]  Milton C Weinstein,et al.  Expanded screening for HIV in the United States--an analysis of cost-effectiveness. , 2005, The New England journal of medicine.

[3]  M J Buxton,et al.  Modelling in economic evaluation: an unavoidable fact of life. , 1997, Health economics.

[4]  Elena Losina,et al.  Use of Genotypic Resistance Testing To Guide HIV Therapy: Clinical Impact and Cost-Effectiveness , 2001, Annals of Internal Medicine.

[5]  Milton C Weinstein,et al.  Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR Task Force on Good Research Practices--Modeling Studies. , 2003, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[6]  M. Weinstein,et al.  Should resistance testing be performed for treatment-naive HIV-infected patients? A cost-effectiveness analysis. , 2005, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[7]  S. Shavell,et al.  Benefits due to immunization against measles. , 1969, Public health reports.

[8]  D J Nokes,et al.  Evaluating the cost-effectiveness of vaccination programmes: a dynamic perspective. , 1999, Statistics in medicine.

[9]  Amit Bar-Or,et al.  Cost-effectiveness of interferon beta-1a, interferon beta-1b, and glatiramer acetate in newly diagnosed non-primary progressive multiple sclerosis. , 2004, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[10]  M C Weinstein,et al.  Pertussis Vaccine: An Analysis of Benefits, Risks, and Costs , 1979, The New England journal of medicine.

[11]  M. Drummond,et al.  Economic Evaluation in Health Care: Merging Theory with Practice , 2002 .

[12]  Milton C Weinstein,et al.  Empirically calibrated model of hepatitis C virus infection in the United States. , 2002, American journal of epidemiology.

[13]  L. Jönsson,et al.  Cost-utility of interferon β1b in the treatment of patients with active relapsing-remitting or secondary progressive multiple sclerosis , 2003, The European Journal of Health Economics.

[14]  M. Weinstein,et al.  Cost–effectiveness of Coronary Artery Bypass Surgery , 1982, Circulation.

[15]  A E Ades,et al.  Markov Chain Monte Carlo Estimation of a Multiparameter Decision Model: Consistency of Evidence and the Accurate Assessment of Uncertainty , 2002, Medical decision making : an international journal of the Society for Medical Decision Making.

[16]  L. Goldman,et al.  The recent decline in mortality from coronary heart disease, 1980-1990. The effect of secular trends in risk factors and treatment. , 1997, JAMA.

[17]  Paul C Langley Modeling for health care and other policy decisions: uses, roles, and validity. , 2002, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[18]  Douglas K Owens,et al.  Cost-effectiveness of screening for HIV in the era of highly active antiretroviral therapy. , 2005, The New England journal of medicine.

[19]  Karl Claxton,et al.  Probabilistic analysis and computationally expensive models: Necessary and required? , 2006, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[20]  L. Goldman,et al.  Forecasting coronary heart disease incidence, mortality, and cost: the Coronary Heart Disease Policy Model. , 1987, American journal of public health.

[21]  John B. Wong,et al.  Decision making in health and medicine: Integrating evidence and values, second edition , 2014 .

[22]  Anthony O'Hagan,et al.  Modelling the cost effectiveness of interferon beta and glatiramer acetate in the management of multiple sclerosis. Commentary: evaluating disease modifying treatments in multiple sclerosis. , 2003, BMJ : British Medical Journal.

[23]  M. Buxton,et al.  Economic Evaluation and Decision Making in the UK , 2012, PharmacoEconomics.

[24]  Milton C Weinstein,et al.  Cost-Effectiveness of Treating Multidrug-Resistant Tuberculosis , 2006, PLoS medicine.

[25]  M. Sculpher,et al.  Using Value of Information Analysis to Prioritise Health Research , 2012, PharmacoEconomics.

[26]  M C Weinstein,et al.  The cost effectiveness of combination antiretroviral therapy for HIV disease. , 2001, The New England journal of medicine.

[27]  M. Weinstein,et al.  Foundations of cost-effectiveness analysis for health and medical practices. , 1977, The New England journal of medicine.

[28]  R. May,et al.  Infectious Diseases of Humans: Dynamics and Control , 1991, Annals of Internal Medicine.