Modeling Good Research Practices—Overview <s> ORIENTATION TO THE SERIES <s> State-transition modeling (STM) is an intuitive, flexible, and transparent approach of computer-based decision-analytic modeling, including both Markov model cohort simulation as well as individual-based (first-order Monte Carlo) microsimulation. Conceptualizing a decision problem in terms of a set of (health) states and transitions among these states, STM is one of the most widespread modeling techniques in clinical decision analysis, health technology assessment, and health-economic evaluation. STMs have been used in many different populations and diseases, and their applications range from personalized health care strategies to public health programs. Most frequently, state-transition models are used in the evaluation of risk factor interventions, screening, diagnostic procedures, treatment strategies, and disease management programs. <s> BIB001 </s> Modeling Good Research Practices—Overview <s> ORIENTATION TO THE SERIES <s> Discrete event simulation (DES) is a form of computer-based modeling that provides an intuitive and flexible approach to representing complex systems. It has been used in a wide range of health care applications. Most early applications involved analyses of systems with constrained resources, where the general aim was to improve the organization of delivered services. More recently, DES has increasingly been applied to evaluate specific technologies in the context of health technology assessment. The aim of this article was to provide consensus-based guidelines on the application of DES in a health care setting, covering the range of issues to which DES can be applied. The article works through the different stages of the modeling process: structural development, parameter estimation, model implementation, model analysis, and representation and reporting. For each stage, a brief description is provided, followed by consideration of issues that are of particular relevance to the application of DES in a health care setting. Each section contains a number of best practice recommendations that were iterated among the authors, as well as among the wider modeling task force. <s> BIB002 </s> Modeling Good Research Practices—Overview <s> ORIENTATION TO THE SERIES <s> The transmissible nature of communicable diseases is what sets them apart from other diseases modeled by health economists. The probability of a susceptible individual becoming infected at any one point in time (the force of infection) is related to the number of infectious individuals in the population, will change over time, and will feed back into the future force of infection. These nonlinear interactions produce transmission dynamics that require specific consideration when modeling an intervention that has an impact on the transmission of a pathogen. Best practices for designing and building these models are set out in this paper. <s> BIB003
The papers generally follow the same structure. After an introduction, the key concepts and definitions pertinent to each topic are laid out, followed by presentation of the recommendations and their rationale. Each group was given leeway, however, to approach their subject in the way they felt was best. A reader wishing to have a comprehensive view of the recommendations should approach the papers in order: first the one dealing with conceptualizing a model, 1 then the three addressing specific techniques, 2-4 followed by the paper on parameter estimation and uncertainty, 5 and concluding with the one on transparency and validation. 6 If the detailed explanations are not required, then this overview paper can be consulted for the list of recommendations. The conceptualization paper 1 begins by defining the term model. In the sense used by the task force, a model is a mathematical framework representing some aspects of reality at a sufficient level of detail to inform a clinical or policy decision. The paper then outlines the modeling process, which begins with carefully examining the decision problem and laying out the elements required. Along the way, the designers make many decisions. They select what aspects of reality to include and the extent to which they are detailed, driven by who their audience is (the ''perspective'') and the imperative to sufficiently reflect reality. They choose the time span that the model is to cover (''time horizon''), its target population (or populations), which interventions to consider, how to structure the model, which outcomes to report, and many other features. The paper underscores that conceptualizing the model should precede examination of the available data to avoid designing a model that lacks key components and thus poorly represents the decision problem. It also emphasizes the importance of understanding the policy context and broad consultation with experts. This paper concludes with guidance on choosing a technique for implementing the model. The three papers on modeling techniques begin with definition of the technique and details of the key elements that characterize it. BIB001 BIB002 BIB003 Indications on when to use the technique are given, and structuring of the model using that technique is described. All three conclude with suggestions on how to communicate that type of model. The state-transition paper also deals briefly with decision trees, a simpler technique that is sometimes adequate for the decision problem at hand. 2 The paper on DES spends somewhat more effort orienting readers to the technique, given that today it is less commonly used in medical contexts. 3 The paper on dynamic transition models devotes more space to the parameters involved because these are a crucial and prominent component of this type of model. 4 The paper regarding parameter estimation and uncertainty discusses each topic first and then provides the recommendation that emerges. 5 Significant effort is made to bring order to the unruly terminology pertaining to uncertainty (though, undoubtedly, practitioners will still want to cling to their favorite terms). The connection between estimation and subsequent uncertainty analyses is emphasized, and the choices of distributions are described. Calibration methods and structural uncertainty are briefly addressed, and the paper concludes with extensive guidance on the reporting of uncertainty. The final paper in the series-also giving recommendations after each topic-deals with the twin aspects of transparency and validation. 6 It begins with the thorny topic of trusting the results of a model enough to allow them to guide a decision and how this can be achieved. It discusses both technical and nontechnical documentation of a model, designed to achieve the required transparency, emphasizing that it is inappropriate to require that a model be understandable at full depth by someone without the necessary technical know-how. The types of validation, their necessity and sufficiency, and interpretation are then addressed in detail.