Markov Model Research Articles

Cost-Effectiveness Analysis of Digital Breast Tomosynthesis and Mammography in Breast Cancer Screening: A Markov Modeling Study.

Mammography (MG) has demonstrated its effectiveness in diminishing mortality and advanced-stage breast cancer incidences in breast screening initiatives. Notably, research has accentuated the superior diagnostic efficacy and cost-effectiveness of digital breast tomosynthesis (DBT). However, the scope of evidence validating the cost-effectiveness of DBT remains limited, prompting a requisite for more comprehensive investigation. The present study aimed to rigorously evaluate the cost-effectiveness of DBT plus MG (DBT-MG) compared to MG alone within the framework of Taiwan's National Health Insurance program. All parameters for the Markov decision tree model, encompassing event probabilities, costs, and utilities (quality-adjusted life years, QALYs), were sourced from reputable literature, expert opinions, and official records. With 10,000 iterations, a 2-year cycle length, a 30-year time horizon, and a 2% annual discount rate, the analysis determined the incremental cost-effectiveness ratio (ICER) to compare the cost-effectiveness of the two screening methods. Probabilistic and one-way sensitivity analyses were also conducted to demonstrate the robustness of findings. The ICER of DBT-MG compared to MG was US$5971.5764/QALYs. At a willingness-to-pay (WTP) threshold of US$33,004 (Gross Domestic Product of Taiwan in 2021) per QALY, more than 98% of the probabilistic simulations favored adopting DBT-MG versus MG. The one-way sensitivity analysis also shows that the ICER depended heavily on recall rates, biopsy rates, and positive predictive value (PPV2). DBT-MG shows enhanced diagnostic efficacy, potentially diminishing recall costs. While exhibiting a higher biopsy rate, DBT-MG aids in the detection of early-stage breast cancers, reduces recall rates, and exhibits notably superior cost-effectiveness.

Cost-effectiveness of a complex continuum of care intervention targeting women and children: protocol for an economic evaluation of the Bukhali trial in South Africa

IntroductionAs nearly two-thirds of women presenting at their first antenatal visit are either overweight or obese in urban South Africa, the preconception period is an opportunity to optimise health and...

Retraction Note: The design of network security protection trust management system based on an improved hidden Markov model

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s13635-023-00146-z.

Genome Mining of Fungal Unique Trichodiene Synthase-like Sesquiterpene Synthases

Sesquiterpenoids served as an important source for natural product drug discovery. Although genome mining approaches have revealed numerous novel sesquiterpenoids and biosynthetic enzymes, the comprehensive landscape of fungal sesquiterpene synthases (STSs) remains elusive. In this study, 123 previously reported fungal STSs were subjected to phylogenetic analysis, resulting in the identification of a fungi-specific STS family known as trichodiene synthase-like sesquiterpene synthases (TDTSs). Subsequently, the application of hidden Markov models allowed the discovery of 517 TDTSs from our in-house fungi genome library of over 400 sequenced genomes, and these TDTSs were defined into 79 families based on a sequence similarity network. Based on the novelty of protein sequences and the completeness of their biosynthetic gene clusters, 23 TDTS genes were selected for heterologous expression in Aspergillus oryzae. In total, 10 TDTSs were active and collectively produced 12 mono- and sesquiterpenes, resulting in the identification of the first chamipinene synthase, as well as the first fungi-derived cedrene, sabinene, and camphene synthases. Additionally, with the guidance of functionally characterized TDTSs, we found that TDTSs in Family 1 could produce bridged-cyclic sesquiterpenes, while those in Family 2 could synthesize spiro- and bridged-cyclic sesquiterpenes. Our research presents a new avenue for the genome mining of fungal sesquiterpenoids.

Hidden Markov model–based asynchronous fault-tolerant control for mode-dependent time-delayed Markovian jump systems with uncertain conditional probability

In control engineering, time-delayed systems are quite common. Modern industrial systems often involve delays due to complicated network environments, the accuracy of sensors and actuators, or signal processing. Speaking of Markovian jump systems, such systems undergo discrete mode changes caused by a Markov process. Hence, it is reasonable to consider the mode-dependent time delays. In this paper, the asynchronous fault-tolerant controller is developed for mode-dependent time-delayed Markovian jump systems. First, the estimations of states and actuator fault are provided by designing an intermediate observer. Then, based on these estimations, the asynchronous fault-tolerant controller is designed by virtue of the hidden Markov process with uncertain conditional probability. Finally, the existence conditions of both the designed observer and fault-tolerant controller are given in terms of linear matrix inequalities. In the meantime, a numerical example is illustrated to validate the effectiveness and potential of the proposed method.

Using lineups to evaluate goodness of fit of animal movement models

Abstract Movement models are frequently fit to animal location data to understand how individuals respond to and interact with local environmental features. Several open‐source software packages are available for analysing animal movements and can facilitate parameter estimation, yet there are relatively few methods available for evaluating model goodness of fit. We describe how a simple graphical technique, the lineup protocol, can be used to evaluate goodness of fit of integrated step‐selection analyses and hidden Markov models, but the method can be applied much more broadly. We leverage the ability to simulate data from fitted models and demonstrate the approach using both an integrated step‐selection analysis and a hidden Markov model applied to fisher (Pekania pennanti) data. A variety of responses and movement metrics can be used to evaluate models, and the lineup protocol can be tailored to focus on specific model assumptions or movement features that are of primary interest. Although it is possible to evaluate statistical significance using a formal hypothesis test, the method can also be used in a more exploratory fashion (e.g. to explore variability in model behaviour across stochastic simulations or to identify areas where the model could be improved). We provide coded examples and vignettes to demonstrate the flexibility of the approach. We encourage movement ecologists to consider how their models will be applied when choosing appropriate graphical responses for evaluating goodness of fit.

Cost-effectiveness of sutimlimab in cold agglutinin disease.

Primary cold agglutinin disease (CAD) is a rare autoimmune hemolytic anemia caused by cold-reactive antibodies that bind to red blood cells and lead to complement-mediated hemolysis. Patients with primary CAD experience the burden of increased health resource utilization and reduced quality of life. The standard-of-care (SOC) in patients with primary CAD has included cold avoidance, transfusion support, and chemoimmunotherapy. The use of sutimlimab, a humanized monoclonal antibody that selectively inhibits C1-mediated hemolysis, was shown to reduce transfusion-dependence and improve quality of life across two pivotal phase 3 studies, further supported by 2-year extension data. Using data from the transfusion-dependent patient population that led to sutimlimab's initial FDA approval, we performed the first-ever cost-effectiveness analysis in primary CAD. The projected incremental cost-effectiveness ratio (ICER) in our Markov model was $2 340 000/QALY, significantly above an upper-end conventional US willingness-to-pay threshold of $150 000/QALY. These results are consistent across scenarios of higher body weight and a pan-refractory SOC patient phenotype (i.e., treated sequentially with bendamustine-rituximab, bortezomib, ibrutinib, and eculizumab). No parameter variations in deterministic sensitivity analyses changed our conclusion. In probabilistic sensitivity analysis, SOC was favored over sutimlimab in 100% of 10 000 iterations. Exploratory threshold analyses showed that significant price reduction (>80%) or time-limited treatment (<18 months) followed by lifelong clinical remission off sutimlimab would allow sutimlimab to become cost-effective. The impact of sutimlimab on health system costs with longer term follow-up data merits future study and consideration through a distributional cost-effectiveness framework.

Adaptive Design of Solar-Powered Energy Systems Based on Daily Clearness State Evolution

The optimal designing of the hybrid energy system (HES) is a challenging task due to the multiple objectives and various uncertainties. Especially for HES, primarily powered by solar energy, the reference solar radiation data directly impact the result of the optimization design. To incorporate the stochastic characteristics of solar radiation into the sizing process, a data-driven stochastic modeling method for solar radiation is proposed. The method involves two layers of stochastic processes that capture the intraday variation and daily evolution of solar radiation. First, the clearness index (CI) is introduced to describe the radiation intensity at different times. Then, the daily clearness state (DCS) is proposed, based on the statistical indicators of the intraday CI. The Markov model is used to describe the stochastic evolutionary characteristics between different DCSs. The probabilistic distribution of the CI under different DCS is obtained based on the diffusion kernel density estimation (DKDE), which is used for the stochastic generation of the CI at various times of the day. Finally, the radiation profile required for the optimal design is obtained by the stochastic generation of the DCS sequences and the intraday clearness index under corresponding states. A case study of an off-grid solar-powered HES is provided to illustrate this methodology.

Cost-effectiveness of PCV20 to Prevent Pneumococcal Disease in the Pediatric Population: A German Societal Perspective Analysis.

Since 2009, a pneumococcal conjugate vaccine (PCV) covering 13 serotypes (PCV13) has been included by Germany's Standing Committee on Vaccinations for infants, resulting in major reductions in pneumococcal disease (PD). Higher-valent vaccines may further reduce PD burden. This cost-effectiveness analysis compared 20-valent PCV (PCV20) under a 3+1 schedule with 15-valent PCV (PCV15) and PCV13, both under 2+1 schedule, in Germany's pediatric population. A Markov model with annual cycles over a 10-year time horizon was adapted to simulate the clinical and economic impact of pediatric vaccination with PCV20 versus lower-valent PCVs in Germany. The model used PCV13 clinical effectiveness and impact studies as well as PCV7 efficacy studies for vaccine direct and indirect effect estimates. Epidemiologic, utility, and medical cost inputs were obtained from published sources. Benefits and costs were discounted at 3% from a German societal perspective. Outcomes included PD cases, deaths, costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs). In the base case, PCV20 provided greater health benefits than PCV13, averting more cases of invasive pneumococcal disease (IPD; 15,301), hospitalized and non-hospitalized pneumonia (460,197 and 472,365, respectively), otitis media (531,634), and 59,265 deaths over 10 years. This resulted in 904,854 additional QALYs and a total cost saving of €2,393,263,611, making PCV20 a dominant strategy compared with PCV13. Compared to PCV15, PCV20 was estimated to avert an additional 11,334 IPD, 704,948 pneumonia, and 441,643 otitis media cases, as well as 41,596 deaths. PCV20 was associated with a higher QALY gain and lower cost (i.e., dominance) compared with PCV15. The robustness of the results was confirmed through scenario analyses as well as deterministic and probabilistic sensitivity analyses. PCV20 3+1 dominated both PCV13 2+1 and PCV15 2+1 over 10 years. Replacing lower-valent PCVs with PCV20 would result in greater clinical and economic benefits, given PCV20's broader serotype coverage.

AARF: Autonomous Attack Response Framework for Honeypots to Enhance Interaction Based on Multi-Agent Dynamic Game

Highly interactive honeypots can form reliable connections by responding to attackers to delay and capture intranet attacks. However, current research focuses on modeling the attacker as part of the environment and defining single-step attack actions by simulation to study the interaction of honeypots. It ignores the iterative nature of the attack and defense game, which is inconsistent with the correlative and sequential nature of actions in real attacks. These limitations lead to insufficient interaction of the honeypot response strategies generated by the study, making it difficult to support effective and continuous games with attack behaviors. In this paper, we propose an autonomous attack response framework (named AARF) to enhance interaction based on multi-agent dynamic games. AARF consists of three parts: a virtual honeynet environment, attack agents, and defense agents. Attack agents are modeled to generate multi-step attack chains based on a Hidden Markov Model (HMM) combined with the generic threat framework ATT&amp;CK (Adversarial Tactics, Techniques, and Common Knowledge). The defense agents iteratively interact with the attack behavior chain based on reinforcement learning (RL) to learn to generate honeypot optimal response strategies. Aiming at the sample utilization inefficiency problem of random uniform sampling widely used in RL, we propose the dynamic value label sampling (DVLS) method in the dynamic environment. DVLS can effectively improve the sample utilization during the experience replay phase and thus improve the learning efficiency of honeypot agents under the RL framework. We further couple it with a classic DQN to replace the traditional random uniform sampling method. Based on AARF, we instantiate different functional honeypot models for deception in intranet scenarios. In the simulation environment, honeypots collaboratively respond to multi-step intranet attack chains to defend against these attacks, which demonstrates the effectiveness of AARF. The average cumulative reward of the DQN with DVLS is beyond eight percent, and the convergence speed is improved by five percent compared to a classic DQN.

Simulating Future Exposure to Coastal Urban Flooding Using a Neural Network–Markov Model

Simulating Future Exposure to Coastal Urban Flooding Using a Neural Network–Markov Model

Do traffic flow states follow Markov properties? A high-order spatiotemporal traffic state reconstruction approach for traffic prediction and imputation

Assessing traffic states accurately is challenging due to the complex, high-dimensional, and nonlinear nature of traffic systems. This study introduces the innovative High-Order Spatiotemporal Traffic State Reconstruction (HOSTSR) algorithm, designed to track and predict traffic flow dynamics effectively. It combines phase space reconstruction with time delays and high-order neighborhood concepts from graph theory to improve traffic state assessments' accuracy. The algorithm's effectiveness is validated using chi-square tests and the Chapman-Kolmogorov equation to confirm the Markovian properties of traffic flows. A lean autoencoder, informed by prior Markov knowledge of traffic states, is developed for mapping traffic states to real traffic data, proving highly effective for traffic data imputation due to the Markov model's memoryless property. Experimental results from the PeMSD04 and PeMSD08 datasets show that HOSTSR outperforms traditional state reconstruction methods based on delayed coordinate embedding in predicting future traffic flow state based on four key metrics. The autoencoder framework, guided by prior Markov knowledge, shows significant advantages in addressing traffic data gaps in different cases over six baseline models. Gradient sensitivity analysis further evaluates the impact of prior knowledge on improving the autoencoder's interpretability for interpolation efforts.

Part-of-speech Tagging for Low-resource Languages: Activation Function for Deep Learning Network to Work with Minimal Training Data

Numerous natural language processing (NLP) applications exist today, especially for the most commonly spoken languages such as English, Chinese, and Spanish. Popular traditional methods such as Rule based methods, Naive Bayes classifiers, Hidden Markov models, Conditional Random field-based classifiers, and other stochastic methods have contributed to this improvement in the past. Recently, deep learning has led to exciting breakthroughs in several areas of artificial intelligence, including image processing and natural language processing. It is important to label words as parts of speech to begin developing most of the NLP applications. A deep study in this area reveals that many popular approaches used for this purpose require massive training data. Therefore, these approaches have not been helpful for languages not rich in digital resources. Applying these methods with very little training data prompts the need for innovative problem-solving. This article describes our research, which examines the strengths and weaknesses of well-known approaches, such as conditional random fields and state-of-the-art deep learning models, when applied for part-of-speech tagging using minimal training data for Assamese and English. We also examine the factors affecting them. We discuss our deep learning architecture and the proposed activation function, which shows promise with little training data. The activation function categorizes words belonging to different classes with more confidence by using the outcomes of statistical methods with SMTaylor SoftMax in our deep learning model. With minimal training, our deep learning architecture using the proposed modification of SM-Taylor SoftMax improves accuracy upto 4%, for our small dataset. This technique is a combination of SMTaylor SoftMax and statistical probability distribution of words over tags.

A cost effectiveness analysis of two treatment strategies for trigeminal neuralgia in Ontario

Background: Trigeminal neuralgia (TN) is a debilitating disease with an annual incidence of approximately 4–27/100,000. In Ontario, over 2000 patients receive interventions for profound pain, including medical and surgical therapies. The global expected cost of these approaches is unknown. This study aims to analyze the cost-effectiveness of one surgical therapy, microvascular decompression (MVD), compared with the best medical therapy (carbamazepine) as first-line therapy. Methods: Costs were gathered from the Canadian Institute for Health Information, Ontario Drug Benefit Formulary, and Ontario Ministry of Health Schedule of Benefits for Physician Services. Academic literature was used to estimate unavailable items. A cost-benefit Markov model was created for each strategy with literature-based rates for annual cycles from years 1 to 5, followed by a linear recurrent cycle from years 6 to 10. Incremental cost-effectiveness ratios (ICERs) were calculated based on the incremental cost in 2022 Canadian Dollars (CAD) per pain-free year. Results: Base case cost per patient was $10,866 at 10 years in the “MVD first” group and $10,710 in the “carbamazepine first” group. Ten-year ICER was $1,104 for “MVD first,” with strict superiority beyond this time point. One-way deterministic sensitivity analysis for multiple factors suggested the highest cost variability and ICER variability were due to surgery cost, medication failure rate, and medication cost. Conclusion: Economic benefit is established for a “MVD first” strategy in the Ontario context with strict superiority beyond the 10-year horizon. If a cost-effectiveness threshold of $50,000 per pain-controlled year is used, the benefit is established at 4 years.

Cost-utility analysis of add-on vericiguat for the treatment of chronic heart failure with reduced ejection fraction in China

ObjectiveThis study aimed to evaluate the cost-utility of the addition of vericiguat for treating chronic heart failure (CHF) in China from the healthcare payer’s perspective.MethodsA Markov model was built to estimate the cost and utility of treating CHF using vericiguat plus standard treatment (vericiguat group) vs. standard treatment alone (standard treatment group). The clinical parameters (mortality of cardiovascular and hospitalization rate of HF) were calculated according to the VICTORIA clinical trial. The HF cost and utility data were obtained from the literature published in China. One-way sensitivity analysis and probability sensitivity analysis were performed.ResultsAccording to the 13-year model, vericiguat was more expensive (155599.07 CNY vs. 259396.83 CNY) and more effective (4.41 QALYs vs. 4.54 QALYs). The incremental cost‐utility ratio (ICUR) was 802389.27 CNY per QALY. One-way sensitivity analysis revealed that cardiovascular mortality in the two groups was the parameter that had the greatest impact on the results. The GDP per capita in 2022 in China was 85,700 CNY. The probability sensitivity analysis (PSA) showed that the probability of vericiguat being cost-effective was only 41.7% at the willingness-to-pay (WTP) threshold of 3 times GDP per capita (257,100 CNY).ConclusionsIn China, the treatment of CHF with vericiguat is not cost-effective. The drug price could decrease to 145.8 CNY, which could be considered cost-effective.

Multi-market P2P trading of cooling–heating-power-hydrogen integrated energy systems: An equilibrium-heuristic online prediction optimization approach

In this paper, an equilibrium-heuristic online prediction optimization approach is proposed for multi-market peer-to-peer (P2P) electricity–hydrogen trading of integrated energy systems (IESs) with uncertainties. First, the IES, consisting of a hydrogen energy storage subsystem and a combined cooling, heating, and power subsystem, is constructed in the distribution network to improve energy utilization and market efficiency. Then, a bi-level optimization model for IESs, participating in the P2P electricity–hydrogen energy trading, the real-time electricity, and the ancillary service markets, is proposed, in which the top-level model can be formulated as a P2P electricity–hydrogen trading pricing model through the social welfare maximization problem, and the lower-level model is used to maximize the IES operating profit. To effectively solve the bi-level model, the game equilibrium-based ADMM distributed algorithm is used to obtain the P2P trading volume and prices of the top-level model, and a new hybrid heuristic algorithm, called hybrid sand cat swarm optimization and improved honey badger algorithm (SCIHB), is proposed to solve the lower level model. SCIHB utilizes a hybrid mechanism and an adaptive learning rate parameter to balance local exploitation and global exploration. Furthermore, a new online learning probabilistic prediction method based on the hidden Markov model and wavelet transform is introduced to describe the uncertainties of wind power, electricity prices, and frequency regulation prices. Finally, case studies are conducted on an IEEE 33-bus test system, and the numerical results verify the effectiveness of the proposed P2P trading model and solution approach.

Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models

A botnet is a network of compromised computer systems, or bots, remotely controlled by an attacker through bot controllers. This covert network poses a threat through large-scale cyber attacks, including phishing, distributed denial of service (DDoS), data theft, and server crashes. Botnets often camouflage their activity by utilizing common internet protocols, such as HTTP and IRC, making their detection challenging. This paper addresses this threat by proposing a method to identify botnets based on distinctive communication patterns between command and control servers and bots. Recognizable traits in botnet behavior, such as coordinated attacks, heartbeat signals, and periodic command distribution, are analyzed. Probabilistic models, specifically Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), are employed to learn and identify these activity patterns in network traffic data. This work utilizes publicly available datasets containing a combination of botnet, normal, and background traffic to train and test these models. The comparative analysis reveals that both HMMs and PHMMs are effective in detecting botnets, with PHMMs exhibiting superior accuracy in botnet detection compared to HMMs.

Traffic trajectory data analysis technology based on HMM model map matching algorithm.

The rapid growth of traffic trajectory data and the development of positioning technology have driven the demand for its analysis. However, in the current application scenarios, there are some problems such as the deviation between positioning data and real roads and low accuracy of existing trajectory data traffic prediction models. Therefore, a map matching algorithm based on hidden Markov models is proposed in this study. The algorithm starts from the global route, selects K nearest candidate paths, and identifies candidate points through the candidate paths. It uses changes in speed, angle, and other information to generate a state transition matrix that match trajectory points to the actual route. When processing trajectory data in the experiment, K = 5 is selected as the optimal value, the algorithm takes 51 ms and the accuracy is 95.3%. The algorithm performed well in a variety of road conditions, especially in parallel and mixed road sections, with an accuracy rate of more than 96%. Although the time loss of this algorithm is slightly increased compared with the traditional algorithm, its accuracy is stable. Under different road conditions, the accuracy of the algorithm is 98.3%, 97.5%, 94.8% and 96%, respectively. The accuracy of the algorithm based on traditional hidden Markov models is 95.9%, 95.7%, 95.4% and 94.6%, respectively. It can be seen that the accuracy of the algorithm designed has higher precision. The experiment proves that the map matching algorithms based on hidden Markov models is superior to other algorithms in terms of matching accuracy. This study makes the processing of traffic trajectory data more accurate.

Evaluating the cost-effectiveness of voclosporin for the treatment of lupus nephritis in the United States.

Lupus nephritis (LN) is a severe manifestation of systemic lupus erythematosus; up to 30% of patients with LN will develop end-stage kidney disease (ESKD). One of the main treatment goals for LN is preservation of kidney function, with early decreases in proteinuria associated with improved long-term outcomes. Voclosporin, a second-generation calcineurin inhibitor, was approved in the United States in 2021 for the treatment of active LN combined with background immunosuppression. The AURORA 1 study found that the use of voclosporin with low doses of mycophenolate mofetil and glucocorticoids yielded significant reductions in proteinuria. The AURORA 2 study showed long-term efficacy and safety of voclosporin over a 3-year period with kidney function preservation. The Institute for Clinical and Economic Review (ICER) is a nonprofit organization that evaluates medical evidence to help improve patient outcomes and control costs. In 2021, ICER published an economic model to estimate the impact and cost-effectiveness of LN therapies. From a US health care perspective, voclosporin was cost-effective at $149,260 per quality-adjusted life-year (QALY) and $131,528 per equal value of life-years gained (evLYG). At the time of the LN cost-effectiveness model (CEM) development, voclosporin was not yet approved in the United States and the cost of treating patients with LN with ESKD was not captured in the literature. To evaluate the cost-effectiveness of voclosporin given the emergence of new data. The LN CEM uses a short-term trial-based Markov model and long-term extrapolation using partitioned survival modeling data assuming adults with LN start with active disease, transitioning to complete or partial renal response, kidney failure, or death. In the current analysis, clinical data for voclosporin, duration of voclosporin treatment for nonresponders, and drug costs reflecting the 2023 price of voclosporin were updated. Additionally, health care payer costs of disease management were incorporated based on real-world claims data on the costs of treating patients with LN. Using the LN CEM with inputs reflecting the latest and most relevant evidence, the incremental cost of voclosporin per QALY was $88,076 and per evLYG was $77,643. For a subpopulation of Black, Hispanic, and Latino patients, the incremental cost of voclosporin per QALY was $77,435 and per evLYG was $67,828. Following the inclusion of updated data in the cost-effectiveness analysis, voclosporin remains a cost-effective therapy for the treatment of active LN including in a Black, Hispanic, and Latino subpopulation, substantially below the ICER willingness-to-pay threshold of $150,000/QALY.

Cost-effectiveness of restorative treatments for permanent molars with severe molar incisor hypomineralization: perspectives for the Brazilian public health system.

To undertake a cost-effectiveness analysis of restorative treatments for a first permanent molar with severe molar incisor hypomineralization from the perspective of the Brazilian public system. Two models were constructed: a one-year decision tree and a ten-year Markov model, each based on a hypothetical cohort of one thousand individuals through Monte Carlo simulation. Eight restorative strategies were evaluated: high viscosity glass ionomer cement (HVGIC);encapsulated GIC; etch and rinse adhesive + composite; self-etch adhesive + composite;preformed stainless steel crown; HVGIC + etch and rinse adhesive + composite; HVGIC + self-etch adhesive + composite, and encapsulated GIC + etch and rinse adhesive + composite. Effectiveness data were sourced from the literature. Micro-costing was applied using 2022 USD market averages with a 5% variation. Incremental cost-effectiveness ratio (ICER), net monetary benefit (%NMB), and the budgetary impact were obtained. Cost-effective treatments included HVGIC (%NMB = 0%/0%), encapsulated GIC (%NMB = 19.4%/ 19.7%), and encapsulated GIC + etch and rinse adhesive + composite (%NMB = 23.4%/ 24.5%) at 1year and 10years, respectively. The benefit gain of encapsulated GIC + etch and rinse adhesive + composite in relation to encapsulated GIC was small when compared to the cost increase at 1year (gain of 3.28% and increase of USD 24.26) and 10years (gain of 4% and increase of USD 15.54). Within the horizon and perspective analyzed, the most cost-effective treatment was encapsulated GIC restoration. This study can provide information for decision-making.