Surgical prioritization based on decision model outcomes is not sensitive to differences between the health-related quality of life values estimates of physicians and citizens

Purpose: Decision models can be used to support allocation of scarce surgical resources. These models incorporate health-related quality of life (HRQoL) values that can be determined using physician panels. The predominant opinion is that one should use values obtained from citizens. We investigated whether physicians give different HRQoL values to citizens and evaluate whether such differences impact decision model outcomes. Methods: A two-round Delphi study was conducted. Citizens estimated HRQoL of pre- and post-operative health states for ten surgeries using a visual analogue scale. These values were compared using Bland–Altman analysis with HRQoL values previously obtained from physicians. Impact on decision model outcomes was evaluated by calculating the correlation between the rankings of surgeries established using the physicians’ and the citizens’ values.Results: A total of 71 citizens estimated HRQoL. Citizens’ values on the VAS scale were − 0.07 points (95% CI − 0.12 to − 0.01) lower than the physicians’ values. The correlation between the rankings of surgeries based on citizens’ and physicians’ values was 0.96 (p &lt; 0.001). Conclusion: Physicians put higher values on health states than citizens. However, these differences only result in switches between adjacent entries in the ranking. It would seem that HRQoL values obtained from physicians are adequate to inform decision models during crises.</p

Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial

Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling. It has the capacity to perform microsimulation models more efficiently than software commonly used for decision modeling, incorporate statistical analyses within decision models, and produce more transparent models and reproducible results. However, no clear guidance for the implementation of microsimulation models in R exists. In this tutorial, we provide a step-by-step guide to build microsimulation models in R and illustrate the use of this guide on a simple, but transferable, hypothetical decision problem. We guide the reader through the necessary steps and provide generic R code that is flexible and can be adapted for other models. We also show how this code can be extended to address more complex model structures and provide an efficient microsimulation approach that relies on vectorization solutions

A Multidimensional Array Representation of State-Transition Model Dynamics

Cost-effectiveness analyses often rely on cohort state-transition models (cSTMs). The cohort trace is the primary outcome of cSTMs, which captures the proportion of the cohort in each health state over time (state occupancy). However, the cohort trace is an aggregated measure that does not capture information about the specific transitions among health states (transition dynamics). In practice, these transition dynamics are crucial in many applications, such as incorporating transition rewards or computing various epidemiological outcomes that could be used for model calibration and validation (e.g., disease incidence and lifetime risk). In this article, we propose an alternative approach to compute and store cSTMs outcomes that capture both state occupancy and transition dynamics. This approach produces a multidimensional array from which both the state occupancy and the transition dynamics can be recovered. We highlight the advantages of the multidimensional array over the traditional cohort trace and provide potential applications of the proposed approach with an example coded in R to facilitate the implementation of our method

A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling

The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and shareability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research question, with model sharing and transparency relegated to being secondary goals. Consequently, code is often neither well documented nor systematically organized in a comprehensible and shareable approach. Moreover, many decision modelers are not formally trained in computer programming and may lack good coding practices, further compounding the problem of model transparency. To address these challenges, we propose a high-level framework for model-based decision and cost-effectiveness analyses (CEA) in R. The proposed framework consists of a conceptual, modular structure and coding recommendations for the implementation of model-based decision analyses in R. This framework defines a set of common decision model elements divided into five components: (1) model inputs, (2) decision model implementation, (3) model calibration, (4) model validation, and (5) analysis. The first four components form the model development phase. The analysis component is the application of the fully developed decision model to answer the policy or the research question of interest, assess decision uncertainty, and/or to determine the value of future research through value of information (VOI) analysis. In this framework, we also make recommendations for good coding practices specific to decision modeling, such as file organization and variable naming conventions. We showcase the framework through a fully functional, testbed decision model, which is hosted on GitHub for free download and easy adaptation to other applications. The use of this framework in decision modeling will improve code readability and model sharing, paving the way to an ideal, open-source world

Open-Source Modeling and Resource Prioritization in Healthcare

Decisions in healthcare often have to do with the optimal allocation of scarce resources under conditions of uncertainty. To maximize the value that can be obtained from the available resources, healthcare decisions can be supported by decision-analytic models. These models provide an analytic framework to synthesize data from various sources to quantify the consequences of a decision regarding both health outcomes and resource utilization. Although decision-analytic models are considered the most suitable approach to inform decision-making in healthcare, they tend to be complex and cumbersome to review. Therefore, these models are sometimes referred to a “black-box”, a weakness of the field, which begs for more transparency. Scripts from open-source programming languages, such as R or Python, have the potential to increase transparency. However, no clear guidance for the implementation of decision-analytic models in open-source languages exists. This thesis aimed to develop a structured programming method that is generic, uses an open-source programming language, and can be applied to a wide range of topics. To demonstrate how open-source decision-analytic models can be used for resource prioritization we used two COVID-19 related decision problems. One topic related to prioritization of semielective surgical procedures. The other focused on treatment implementation and research prioritization decisions regarding emerging COVID-19 therapies. The open-source programming language used in this thesis is R. Nevertheless, the developed methodological modeling approaches can be generalized to other open-source programming languages. This thesis has shown that R can be used to develop transparent decision-analytic models for healthcare decision making. We have also demonstrated that R-based decision-analytic models can be used for a dynamic value-based allocation of scarce resources, which is especially relevant in times of rapidly changing conditions, such as a pandemic

Prioritisation and design of clinical trials

Clinical trials require participation of numerous patients, enormous research resources and substantial public funding. Time-consuming trials lead to delayed implementation of beneficial interventions and to reduced benefit to patients. This manuscript discusses two methods for the allocation of research resources and reviews a framework for prioritisation and design of clinical trials. The traditional error-driven approach of clinical trial design controls for type I and II errors. However, controlling for those statistical errors has limited relevance to policy makers. Therefore, this error-driven approach can be inefficient, waste research resources and lead to research with limited impact on daily practice. The novel value-driven approach assesses the currently available evidence and focuses on designing clinical trials that directly inform policy and treatment decisions. Estimating the net value of collecting further information, prior to undertaking a trial, informs a decision maker whether a clinical or health policy decision can be made with current information or if collection of extra evidence is justified. Additionally, estimating the net value of new information guides study design, data collection choices, and sample size estimation. The value-driven approach ensures the efficient use of research resources, reduces unnecessary burden to trial participants, and accelerates implementation of beneficial healthcare interventions

An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example

Decision models can combine information from different sources to simulate the long-term consequences of alternative strategies in the presence of uncertainty. A cohort state-transition model (cSTM) is a decision model commonly used in medical decision making to simulate the transitions of a hypothetical cohort among various health states over time. This tutorial focuses on time-independent cSTM, in which transition probabilities among health states remain constant over time. We implement time-independent cSTM in R, an open-source mathematical and statistical programming language. We illustrate time-independent cSTMs using a previously published decision model, calculate costs and effectiveness outcomes, and conduct a cost-effectiveness analysis of multiple strategies, including a probabilistic sensitivity analysis. We provide open-source code in R to facilitate wider adoption. In a second, more advanced tutorial, we illustrate time-dependent cSTMs