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.
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.
In current domestic research on laparoscopic training, researchers usually consider instrument movement path in the hand-eye coordination relationship. However, they ignore the information contained in visual cues by which could guide and control instrument movements. Studies in other areas have shown that trainers can improve their perceptual-motor skills by gaze training. This paper was designed to examine the effectiveness of eye gaze tracking technology in laparoscopic training and to analyze gaze strategy of the subjects in different training methods. The Tobii X1 Light Eye Tracker was used to track the gaze position of subjects when they were performing the two-handed transferring task in box trainer, and to obtain parameters related to gaze strategy including the efficiency of task completion, as well as visual search, visual processing and observation transfer analysis based on Markov chain model. The results showed that the completion time during the last training in gaze training group was decreased by 101.5 s comparing to the first training. Compared with video training group, gaze strategy of gaze training group has a significant change, such as fixation and saccade duration rate was increased by 38%, fixation duration on target area was increased, and saccade amplitude increased by 0.58°, and the probability of the fixation point transferring to equipment decreased by 15%. The results demonstrated that eye gaze tracking technology can be used in laparoscopic training, and can improve the subjects’ skills and shorten the learning curve by learning gaze strategies of experts.
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.
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.
Heart sound segmentation is a key step before heart sound classification. It refers to the processing of the acquired heart sound signal that separates the cardiac cycle into systolic and diastolic, etc. To solve the accuracy limitation of heart sound segmentation without relying on electrocardiogram, an algorithm based on the duration hidden Markov model (DHMM) was proposed. Firstly, the heart sound samples were positionally labeled. Then autocorrelation estimation method was used to estimate cardiac cycle duration, and Gaussian mixture distribution was used to model the duration of sample-state. Next, the hidden Markov model (HMM) was optimized in the training set and the DHMM was established. Finally, the Viterbi algorithm was used to track back the state of heart sounds to obtain S1, systole, S2 and diastole. 500 heart sound samples were used to test the performance of our algorithm. The average evaluation accuracy score (F1) was 0.933, the average sensitivity was 0.930, and the average accuracy rate was 0.936. Compared with other algorithms, the performance of our algorithm was more superior. It is proved that the algorithm has high robustness and anti-noise performance, which might provide a novel method for the feature extraction and analysis of heart sound signals collected in clinical environments.
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.
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.
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.