Model Validation: Has it's Time Come?

Mathematical models and analysis tools for risk assessment of unnatural epidemics: a scoping review

Predicting, issuing early warnings, and assessing risks associated with unnatural epidemics (UEs) present significant challenges. These tasks also represent key areas of focus within the field of prevention and control research for UEs. A scoping review was conducted using databases such as PubMed, Web of Science, Scopus, and Embase, from inception to 31 December 2023. Sixty-six studies met the inclusion criteria. Two types of models (data-driven and mechanistic-based models) and a class of analysis tools for risk assessment of UEs were identified. The validation part of models involved calibration, improvement, and comparison. Three surveillance systems (event-based, indicator-based, and hybrid) were reported for monitoring UEs. In the current study, mathematical models and analysis tools suggest a distinction between natural epidemics and UEs in selecting model parameters and warning thresholds. Future research should consider combining a mechanistic-based model with a data-driven model and learning to pursue time-varying, high-precision risk assessment capabilities.

Four Aspects Affecting Health Economic Decision Models and Their Validation

Health care decision makers in many jurisdictions use cost-effectiveness analysis based on health economic decision models for policy decisions regarding coverage and price negotiation for medicines and medical devices. While validation of health economic decision models has always been considered important, many reviews of model-based cost-effectiveness studies report limitations regarding their validation. The current opinion paper discusses four aspects of current health economic decision modeling with relevance for future directions in model validation: increased use of complex models, international cooperation, open-source modeling, and stakeholder involvement. First, new, more complex clinical study designs and treatment strategies may require relatively complex model structures and/or input data analyses. Simultaneously, more widespread technical knowledge along with wider data availability have led to a broader range of model types. This puts extra requirements on model validation and transparency. Second, increased international cooperation of policy makers and, in particular, health technology assessment (HTA) authorities in performing model assessments is discussed in relation to the repeated use of health economic models (multi-use disease models). We argue such coordinated efforts may benefit model validity. Third, open-source modeling is discussed as one possible answer to increased transparency requirements. Finally, involvement of all relevant stakeholders throughout the whole decision process is an ongoing development that necessarily also includes health economic modeling. We argue this implies that model validity should be considered in a broader perspective, with more focus on conceptual modeling, model transparency, accuracy requirements, and choice of relevant model outcomes than previously.

Trusting the Results of Model-Based Economic Analyses: Is there a Pragmatic Validation Solution?

Models have become a nearly essential component of health technology assessment. This is because the efficacy and safety data available from clinical trials are insufficient to provide the required estimates of impact of new interventions over long periods of time and for other populations and subgroups. Despite more than five decades of use of these decision-analytic models, decision makers are still often presented with poorly validated models and thus trust in their results is impaired. Among the reasons for this vexing situation are the artificial nature of the models, impairing their validation against observable data, the complexity in their formulation and implementation, the lack of data against which to validate the model results, and the challenges of short timelines and insufficient resources. This article addresses this crucial problem of achieving models that produce results that can be trusted and the resulting requirements for validation and transparency, areas where our field is currently deficient. Based on their differing perspectives and experiences, the authors characterize the situation and outline the requirements for improvement and pragmatic solutions to the problem of inadequate validation.