Medical Decision Making最新文献

Highlights: A Markov model simulates the average experience of a cohort of patients.Monte Carlo simulation, the standard approach for estimating the variance, is computationally expensive.A multinomial distribution provides an exact representation of a Markov model.Using the known formulas of a multinomial distribution, the mean and variance of a Markov model can be readily calculated.

Background: Lung volume reduction surgery (LVRS) and medical therapy are 2 available treatment options in dealing with severe emphysema, which is a chronic lung disease. However, or there are currently limited guidelines on the timing of LVRS for patients with different characteristics.

Objective: The objective of this study is to assess the timing of receiving LVRS in terms of patient outcomes, taking into consideration a patient's characteristics.

Methods: A finite-horizon Markov decision process model for patients with severe emphysema was developed to determine the short-term (5 y) and long-term timing of emphysema treatment. Maximizing the expected life expectancy, expected quality-adjusted life-years, and total expected cost of each treatment option were applied as the objective functions of the model. To estimate parameters in the model, the data provided by the National Emphysema Treatment Trial were used.

Results: The results indicate that the treatment timing strategy for patients with upper-lobe predominant emphysema is to receive LVRS regardless of their specific characteristics. However, for patients with non-upper-lobe-predominant emphysema, the optimal strategy depends on the age, maximum workload level, and forced expiratory volume in 1 second level.

Conclusion: This study demonstrates the utilization of clinical trial data to gain insights into the timing of surgical treatment for patients with emphysema, considering patient age, observable health condition, and location of emphysema.

Highlights: Both short-term and long-term Markov decision process models were developed to assess the timing of receiving lung volume reduction surgery in patients with severe emphysema.How clinical trial data can be used to estimate the parameters and obtain short-term results from the Markov decision process model is demonstrated.The results provide insights into the timing of receiving lung volume reduction surgery as a function of a patient's characteristics, including age, emphysema location, maximum workload, and forced expiratory volume in 1 second level.

Background: Metamodels can address some of the limitations of complex simulation models by formulating a mathematical relationship between input parameters and simulation model outcomes. Our objective was to develop and compare the performance of a machine learning (ML)-based metamodel against a conventional metamodeling approach in replicating the findings of a complex simulation model.

Methods: We constructed 3 ML-based metamodels using random forest, support vector regression, and artificial neural networks and a linear regression-based metamodel from a previously validated microsimulation model of the natural history hepatitis C virus (HCV) consisting of 40 input parameters. Outcomes of interest included societal costs and quality-adjusted life-years (QALYs), the incremental cost-effectiveness (ICER) of HCV treatment versus no treatment, cost-effectiveness analysis curve (CEAC), and expected value of perfect information (EVPI). We evaluated metamodel performance using root mean squared error (RMSE) and Pearson's R² on the normalized data.

Results: The R² values for the linear regression metamodel for QALYs without treatment, QALYs with treatment, societal cost without treatment, societal cost with treatment, and ICER were 0.92, 0.98, 0.85, 0.92, and 0.60, respectively. The corresponding R² values for our ML-based metamodels were 0.96, 0.97, 0.90, 0.95, and 0.49 for support vector regression; 0.99, 0.83, 0.99, 0.99, and 0.82 for artificial neural network; and 0.99, 0.99, 0.99, 0.99, and 0.98 for random forest. Similar trends were observed for RMSE. The CEAC and EVPI curves produced by the random forest metamodel matched the results of the simulation output more closely than the linear regression metamodel.

Conclusions: ML-based metamodels generally outperformed traditional linear regression metamodels at replicating results from complex simulation models, with random forest metamodels performing best.

Highlights: Decision-analytic models are frequently used by policy makers and other stakeholders to assess the impact of new medical technologies and interventions. However, complex models can impose limitations on conducting probabilistic sensitivity analysis and value-of-information analysis, and may not be suitable for developing online decision-support tools.Metamodels, which accurately formulate a mathematical relationship between input parameters and model outcomes, can replicate complex simulation models and address the above limitation.The machine learning-based random forest model can outperform linear regression in replicating the findings of a complex simulation model. Such a metamodel can be used for conducting cost-effectiveness and value-of-information analyses or developing online decision support tools.

Background: Network meta-analysis exploits randomized data to compare multiple interventions and generate rankings. Selecting an optimal treatment may be complicated when multiple conflicting outcomes are evaluated in parallel.

Design: The present study suggested the incorporation of multicriteria decision-making methods in network meta-analyses to select the best intervention when multiple outcomes are of interest by creating partial and absolute rankings with the TOPSIS, VIKOR, and PROMETHEE algorithms. The TOPSIS and VIKOR techniques represent distance-based methods for compromise intervention selection, whereas the PROMETHEE analysis method allows the definition of preference and indifference thresholds. In addition, the PROMETHEE technique allows a variety of modeling options by selecting alternative preference functions. Different weights may be applied to outcomes objectively with the entropy method as well as subjectively with the analytic hierarchy process, enabling the individualization of treatment choice depending on the clinical scenario.

Results: Visualization of decision analysis may be performed with multicriteria score-adjusted scatterplots, while league tables may be constructed to depict the PROMETHEE I partial ordering of interventions. A simulated study was performed assuming equal weights of outcomes, and the TOPSIS, VIKOR, and PROMETHEE II methods were compared using a similarity coefficient, indicating a high degree of agreement among methods, especially with higher numbers of interventions.

Conclusions: Multicriteria decision analysis provides a flexible and computationally direct way of selecting compromise interventions and visualizing treatment selection in network meta-analyses. Further research should provide empirical data about the implementation of multicriteria decision analysis in real-world network meta-analyses aiming to define the most suitable method depending on the clinical question.

Highlights: Multicriteria decision-making methods can be implemented in network meta-analysis to indicate compromise interventions.The TOPSIS, VIKOR, and PROMETHEE methods can be used for optimal treatment selection when conflicting outcomes are evaluated.The weights of outcomes can be defined objectively or subjectively, reflecting the priorities of the decision maker.

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.

In an introductory tutorial, we illustrated building cohort state-transition models (cSTMs) in R, where the state transition probabilities were constant over time. However, in practice, many cSTMs require transitions, rewards, or both to vary over time (time dependent). This tutorial illustrates adding 2 types of time dependence using a previously published cost-effectiveness analysis of multiple strategies as an example. The first is simulation-time dependence, which allows for the transition probabilities to vary as a function of time as measured since the start of the simulation (e.g., varying probability of death as the cohort ages). The second is state-residence time dependence, allowing for history by tracking the time spent in any particular health state using tunnel states. We use these time-dependent cSTMs to conduct cost-effectiveness and probabilistic sensitivity analyses. We also obtain various epidemiological outcomes of interest from the outputs generated from the cSTM, such as survival probability and disease prevalence, often used for model calibration and validation. We present the mathematical notation first, followed by the R code to execute the calculations. The full R code is provided in a public code repository for broader implementation.