ObjectivesTo evaluate the economic efficacy of nab-paclitaxel (NAB-P) combined with gemcitabine (GEM) versus GEM alone in the treatment of metastatic pancreatic cancer in China.MethodsA Markov model simulating the costs and health outcomes was developed to estimate quality-adjusted life years (QALYs) and incremental cost-effectiveness ratio (ICER). The impact of parameter uncertainty on the model was assessed by deterministic one-way sensitivity analysis.ResultsNAB-P combined with GEM was shown superior efficacy compared to gemcitabine monotherapy, however with higher costs. The ICER between the two groups was 964 780.79￥/QALY.ConclusionsCompared with gemcitabine monotherapy, NAB-P combined with GEM is not cost-effective. The conclusion is confirmed by deterministic one-way sensitivity analysis.
To solve the defect which is recognizing but not rating the stress, or rating but not considering the influence of the previous stress state to the current state of the existing affective stress evaluation method, this paper proposes an approach of affective stress rating model on electrocardiogram (ECG). An affective stress rating algorithm based on hidden Markov model (HMM) was established with the theory of affective computing. The individual's affective stress was rated using this affective rating model combining the investigation questionnaire. Features like complexity and approximate entropy of ECG were used in the model, and a matching process suggested that it improved the accuracy of affective stress rating. The result of the experiment illustrated that the model considering the environmental factors and the influence of previous stress state to the current state was an effective method in affective stress rating, and the accuracy of rating was improved by this affective stress rating method.
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.
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.
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.
In order to meet the requirements in the cooperation and competition experiments for an individual patient in clinical application, two human interactive behavior key-press models based on hidden Markov model (HMM) were proposed. To validate the cooperative and competitive models, a verification experimental task was designed and the data were collected. The correlation of the score and subjects’ participation level has been used to analyze the reasonability verification. Behavior verification was conducted by comparing the statistical difference in response time for subjects between human-human and human-computer experiment. In order to verify the physiological validity of the models, we have utilized the coherence analysis to analyze the deep information of prefrontal brain area. Reasonability verification shows that the correlation coefficient for the training data and the testing data is 0.883 1 and 0.578 6 respectively based on cooperation model, and 0.813 1 and 0.617 8 respectively based on the competition model. The behavioral verification result shows that the cooperation and competition models have an accuracy of 71.43% respectively. The results of physiological validity show that the deep information of prefrontal brain area could been extracted based on the cooperation and competition models, and reveal the consistency of coherence between the double key-press cooperative and competitive experiments, respectively. Above all, the high consistency is obtained between the cooperatio/competition model and the double key-press experiment by the behavioral and physiological evaluation results. Consequently, the cooperation and competition models could be applied to clinical trials.
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.
Sleep status is an important indicator to evaluate the health status of human beings. In this paper, we proposed a novel type of unperturbed sleep monitoring system under pillow to identify the pattern change of heart rate variability (HRV) through obtained RR interval signal, and to calculate the corresponding sleep stages combined with hidden Markov model (HMM) under the no-perception condition. In order to solve the existing problems of sleep staging based on HMM, ensemble empirical mode decomposition (EEMD) was proposed to eliminate the error caused by the individual differences in HRV and then to calculate the corresponding sleep stages. Ten normal subjects of different age and gender without sleep disorders were selected from Guangzhou Institute of Respirator Diseases for heart rate monitoring. Comparing sleep stage results based on HMM to that of polysomnography (PSG), the experimental results validate that the proposed noninvasive monitoring system can capture the sleep stages S1–S4 with an accuracy more than 60%, and performs superior to that of the existing sleep staging scheme based on HMM.
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.