ObjectiveTo evaluate the economics of duplizumab in combination with optimal supportive care versus optimal supportive care for moderate-to-severe atopic dermatitis in adults from the perspective of our health system. MethodsA Markov model embedded in a decision tree was constructed to compare the long-term cost-utility of dupilumab combined with optimal supportive care versus optimal supportive care, and a sensitivity analysis was performed on the results. ResultsThe results of the basic analysis showed that compared with the optimal supportive care, dupliyuzumab in combination with optimal supportive care resulted in 3.82 more QALYs, while its cost was 125 549.42 yuan more. The ICER was 32 854.83 yuan/QALY, which was less than one times China's per capita GDP in 2022, and was economical. Univariate sensitivity analysis showed that factors such as Dupilumab-16-week post - no response utility value, Dupilumab-52-week post response rate and Dupilumab-52-week adherence had a greater impact on the cost changes. The results of the probabilistic sensitivity analysis showed a stable model structure and good robustness. ConclusionIn adult patients with moderate-to-severe atopic dermatitis, dupliyuzumab in combination with an optimal supportive care regimen is more cost-effective compared to an optimal supportive care regimen.
Health economics analysis has become increasingly important in recent years. It is essential to master the use of relevant software to conduct research in health economics. TreeAge Pro software is widely used in the healthcare decision analysis. It can carry out decision analysis, cost-effectiveness analysis, and Monte Carlo simulation. With powerful functionlity and outstanding visualization, it can build Markov disease transition models to analyze Markov processes according to disease models and accomplish decision analysis with decision trees and influence diagrams. This paper introduces cost-effectiveness analysis based on Markov model with examples and explains the main graphs.
Objective To compare the economic effectiveness of universal screening, high-risk population screening, and no screening strategies for thyroid disease prevention and control among pregnant women in China through cost-effectiveness analysis, providing evidence-based support for optimizing health policy decisions on prenatal thyroid disease screening. Methods Based on the characteristics of thyroid disorders during pregnancy, a combined decision tree and Markov model was developed to conduct a lifetime cost-effectiveness analysis across three strategies: no screening, high-risk population screening, and universal screening. Sensitivity analyses were performed on key parameters. Results Base-case analysis demonstrated that universal screening was the most cost-effective strategy when the World Health Organization (WHO)-recommended payment threshold of 1×gross domestic product (GDP) per capita was used, with an incremental cost-effectiveness ratio (ICER) of 20636.18 yuan per quality-adjusted life year (QALY) compared to no screening, followed by high-risk population screening (ICER=21071.71 yuan/QALY). The results of the sensitivity analysis showed a strong stability of the model. Conclusions Of the 3 screening programs for thyroid disease in pregnancy, universal screening is the most cost-effective when the WHO-recommended payment threshold of 1×GDP per capita is used.
Vision is an important way for human beings to interact with the outside world and obtain information. In order to research human visual behavior under different conditions, this paper uses a Gaussian mixture-hidden Markov model (GMM-HMM) to model the scanpath, and proposes a new model optimization method, time-shifting segmentation (TSS). The TSS method can highlight the characteristics of the time dimension in the scanpath, improve the pattern recognition results, and enhance the stability of the model. In this paper, a linear discriminant analysis (LDA) method is used for multi-dimensional feature pattern recognition to evaluates the rationality and the accuracy of the proposed model. Four sets of comparative trials were carried out for the model evaluation. The first group applied the GMM-HMM to model the scanpath, and the average accuracy of the classification could reach 0.507, which is greater than the opportunity probability of three classification (0.333). The second set of trial applied TSS method, and the mean accuracy of classification was raised to 0.610. The third group combined GMM-HMM with TSS method, and the mean accuracy of classification reached 0.602, which was more stable than the second model. Finally, comparing the model analysis results with the saccade amplitude (SA) characteristics analysis results, the modeling analysis method is much better than the basic information analysis method. Via analyzing the characteristics of three types of tasks, the results show that the free viewing task have higher specificity value and a higher sensitivity to the cued object search task. In summary, the application of GMM-HMM model has a good performance in scanpath pattern recognition, and the introduction of TSS method can enhance the difference of scanpath characteristics. Especially for the recognition of the scanpath of search-type tasks, the model has better advantages. And it also provides a new solution for a single state eye movement sequence.
In order to improve the motion fluency and coordination of lower extremity exoskeleton robots and wearers, a pace recognition method of exoskeleton wearer is proposed base on inertial sensors. Firstly, the triaxial acceleration and triaxial angular velocity signals at the thigh and calf were collected by inertial sensors. Then the signal segment of 0.5 seconds before the current time was extracted by the time window method. And the Fourier transform coefficients in the frequency domain signal were used as eigenvalues. Then the support vector machine (SVM) and hidden Markov model (HMM) were combined as a classification model, which was trained and tested for pace recognition. Finally, the pace change rule and the human-machine interaction force were combined in this model and the current pace was predicted by the model. The experimental results showed that the pace intention of the lower extremity exoskeleton wearer could be effectively identified by the method proposed in this article. And the recognition rate of the seven pace patterns could reach 92.14%. It provides a new way for the smooth control of the exoskeleton.
Objective To evaluate the cost effectiveness of human papillomavirus vaccine (HPV) for treating cervical cancer. Methods We constructed a Markov model to evaluate the cost-effectiveness of HPV versus Chinese healthy women aged 18 to 25 for treating Cervical Cancer. We calculated the clinical benefits and cost-effectiveness and judged the results based on willing to pay. Sensitivity analysis was made for parameters like cost, discounting rate and vaccine efficacy. Results HPV vaccination was a cost-effective option under the local willing to pay value with the incremental cost utility ratio 43 489 per QALY gained. It proved that vaccination was an economic and effective solution. Conclusion Given the results of Markov model, the cost effectiveness of HPV vaccination of Chinese women aged 18 to 25 is positive. Considering the data sources and model hypothesis, this report has some limitations. Further studies are warranted.
ObjectivesTo compare the efficacy and economy of febuxostat and allopurinol in the treatment of chronic gout, and to provide reference for clinical rational drug use.MethodsThe Markov model was established to conduct cost-effectiveness analysis for febuxostat and allopurinol serving as the front-line treated medicines. In view of the uncertainty of model parameters, single factor, probability sensitivity analysis and other methods were used to analyze the stability of the results.ResultsThe cost of the therapeutic schedule of allopurinol 300 mg was lower than febuxostat 40 mg, and it saved RMB 4 339.6 Yuan for each patients on average, while obtained 0.067 more QALY. Uncertainty analysis revealed that only those utility value which could not reach the standard influenced the final results in all included variable elements. When the aspiration payment value was zero, the percentage of therapeutic schedule for allopurinol 300 mg was 100. With the increase of aspiration payment value, the probability for febuxostat scheme becoming the superior one showed a very gradual growth. When the aspiration payment value reached 150 000, the probability still remained under 10%.ConclusionsAllopurinol is more economical than finasteride as the first choice in the treatment of chronic gout. Therefore, it is recommended that allopurinol should be used as the first-line drug for economical considerations.
Objectives To determine the health benefit of elbasvir/grazoprevir versus peginterferon combing with ribavirin (PR regimen) for Chinese chronic hepatitis C patients with genotype 1b infection. Methods Markov cohort state-transition models were constructed to conduct cost utility analysis. Sensitivity analyses were performed based on base-case analysis. Results Elbasvir/grazoprevir was dominant versus PR, resulting in higher QALYs and lower costs for both noncirrhotic patients (13.867 5 QALYs, 82 090.82 RMB vs. 12.696 2 QALYs, 122 791.55 RMB) and cirrhotic patients (12.841 6 QALYs, 225 807.70 RMB vs. 8.892 4 QALYs, 326 545.01 RMB). Elbasvir/grazoprevir was economically dominant in nearly 100% among all patients within the range of threshold from 0 to 161 805 RMB/QALY. Conclusions Elbasvir/grazoprevir was dominant in treatment of genotype 1b chronic hepatitis C infection in China.
Markov model is one of the decision analysis models, which is widely used in pharmacoeconomic evaluation studies. In terms of dealing with changes of disease risks during different times, the transition probabilities among different Markov health states becomes hard to calculate. Nevertheless, survival analysis is an available resolution. In this paper, we introduced how to apply survival analysis in calculation of transition probability in time-dependent model based on cumulative probability with a case analysis on advanced gastric cancer Markov model, and provide more information for researchers to build models.
Rapid and accurate recognition of human action and road condition is a foundation and precondition of implementing self-control of intelligent prosthesis. In this paper, a Gaussian mixture model and hidden Markov model are used to recognize the road condition and human motion modes based on the inertial sensor in artificial limb (lower limb). Firstly, the inertial sensor is used to collect the acceleration, angle and angular velocity signals in the direction of x, y and z axes of lower limbs. Then we intercept the signal segment with the time window and eliminate the noise by wavelet packet transform, and the fast Fourier transform is used to extract the features of motion. Then the principal component analysis (PCA) is carried out to remove redundant information of the features. Finally, Gaussian mixture model and hidden Markov model are used to identify the human motion modes and road condition. The experimental results show that the recognition rate of routine movement (walking, running, riding, uphill, downhill, up stairs and down stairs) is 96.25%, 92.5%, 96.25%, 91.25%, 93.75%, 88.75% and 90% respectively. Compared with the support vector machine (SVM) method, the results show that the recognition rate of our proposed method is obviously higher, and it can provide a new way for the monitoring and control of the intelligent prosthesis in the future.