The need to combine direct and indirect evidence is increasing in clinical fields, and this is especially true when direct evidence is inconclusive. Thus, in recent years, network meta-analysis has been utilized increasingly in medicine. Network meta-analysis is a statistical method that enables comparison of multiple treatments simultaneously-by combining direct and indirect evidence of the relative treatment effects-to assess the comparative effectiveness of multiple interventions even if there are no head-to-head comparisons. Network meta-analysis has some advantages in addressing all treatments for a specific condition, comparing interventions and ranking the efficacy and safety of multiple treatments, and increasing the certainty of evidence by pooling direct and indirect evidence to generate overall estimates. The major assumption in network meta-analysis is exchange-ability of the studies, and other key assumptions include similarity, consistency, and transitivity. The Bayesian approach is used most commonly in network meta-analysis because it provides greater flexibility that allows for the use of more complex models and can produce estimates of rank probabilities. Bayesian network meta-analysis produces treatment rankings according to the probability of being the best treatment, the second best, third best, and so forth. Network meta-analysis is an interesting method that provides useful information for use in by rheumatologists in decision-making.
Bayes Theorem ; Pliability

OBJECTIVE: The authors calculated the positive and negative predictive values of the Korean version of minimenteal state examination(MMSEK) and Cognitive Impairment Diagnosing Instrument(CIDI) in order to estimate the reliability of these tests In large unselected populations. METHODS: The data of the MMSEK were selected from Park et al's study. The data of the CIDI were selected from Park's another study. Calculation of the positive and negative predictive values was based on the Bayes theorem. RESULTS: When the prevalence of dementia is 2.5% among the elderly people in a community, the MMSEK cutoff point of 20 for identification of dementia yielded 72.3% FV(+) and 99.5% PV(-). When it is 10.8%, the curio(f point for identification of dementia yielded 92.5% FV(+) and 97.8% FV(-). Using the CIDI cutoff point of 57, FV(+) was estimated as 27.5% and FV(-) as 99.8% when the prevalence of dementia is 2.5% among the elderly people in a community. FV(+) and FV(-) were computed as 64.2% and 99.1%, respectively, assuming that the prevalence of dementia is 10.8% in an elderly population based on the above CIDI cutoff point. CONCLUSION: When the MMSEK and CIDI are to be used in unselected elderly samples, it is important to know the predictive values of these tests related with prevalences.
Aged* ; Bayes Theorem* ; Dementia ; Humans ; Prevalence

Bayes Theorem ; Public Health ; Translational Medical Research

OBJECTIVETo explore the differences between the Bayesian interim analysis and the classical interim analysis.

METHODSTo compare the means of two independent samples between control and treatment, superior hypothesis test was established. In line with the data requirements for group sequential design, Type Iota error of Bayesian interim analysis based on various prior distributions, Power, Average Sample Size and Average Stage were estimated in the interim analysis.

RESULTSIn the Pocock and O' Brien & Fleming designs, the Type Iota errors in the Bayesian interim analysis based on the skeptical prior distribution and the handicap prior distribution were controlled at around 0.05. When the powers of these two classical designs were both 80%, Bayesian powers of the skeptical prior distribution and the handicap prior distribution were markedly lower. The powers of the non-informative prior distribution and the enthusiastic prior distribution were distinctly higher than 80%.

CONCLUSIONIn the Bayesian interim analysis based on the skeptical prior distribution and the handicap Prior distribution, the Type Iota errors can be well controlled. Bayesian interim analyses using these two prior distributions, compared with the analysis adopting the O' Brien & Fleming method, can markedly increase the possibility of ending the clinical trials ahead of time. The Bayesian interim analyses based on these two distributions do not have practical value for group sequential design of the Pocock method.

This paper aims to construct a Bayesian(BN) fault diagnosis model of traditional Chinese medicine dry granulation based on the failure model and effect analysis(FMEA), effectively control risk factors and ensure the quality of granules.Firstly, the risk ana-lysis of dry granulation process was carried out with FMEA, and the selected medium and high risk factors were taken as node variables to establish corresponding BN network with causality.According to the mathematical reasoning method of probability theory, the model was accurately inferred and verified by Netica, and the granule nonconformance was used as the evidence for reversed reasoning to determine the most likely cause of the failure that affected the granule quality.The BN fault diagnosis model of traditional Chinese medicine dry gra-nulation was established based on the medium and high risk factors of process, prescription and equipment screened out by FMEA, such as roller pressure, raw material viscosity, clearance between rollers in the paper.The fault diagnosis of traditional Chinese medicine dry granulation process was then carried out according to the model, and the posterior probability of each node under the premise of nonconforming granule quality was obtained.This method could provide strong support for operators to quickly eliminate faults and make decisions, so as to improve the efficiency and accuracy for fault diagnosis and prediction, with innovation in its application.
Bayes Theorem ; Medicine, Chinese Traditional ; Probability

In Zherong county, Fujian province, the black spot of Pseudostellaria heterophylla often breaks out in the rainy season from April to June every year. As one of the main leaf diseases of P. heterophylla, black spot seriously affects the yield and quality of the medicinal material. To identify and characterize the pathogens causing black spot, we isolated the pathogens, identified them as a species of Alternaria according to Koch's postulates, and then tested their pathogenicity and biological characteristics. The results showed that the pathogens causing P. heterophylla black spot were A. gaisen, as evidenced by the similar colony morphology, spore characteristics, sporulation phenotype, and the same clade with A. gaisen on the phylogenetic tree(the maximum likelihood support rate of 100% and the Bayesian posterior probability of 1.00) built based on the tandem sequences of ITS, tef1, gapdh, endoPG, Alta1, OPA10-2, and KOG1077. The optimum conditions for mycelial growth of the pathogen were 25 ℃, pH 5-8, and 24 h dark culture. The lethal conditions for mycelia and spores were both treatment at 50 ℃ for 10 min. We reported for the first time the A. gaisen-caused black spot of P. heterophylla. The results could provide a theoretical basis for the diagnosis and control of P. heterophylla leaf spot diseases.
Bayes Theorem ; Phylogeny ; Caryophyllaceae ; Alternaria ; Mycelium

OBJECTIVES: Occupational injuries are known to be the main adverse outcome of occupational accidents. The purpose of the current study was to identify control strategies to reduce the severity of occupational injuries in the mining industry using Bayesian network (BN) analysis. METHODS: The BN structure was created using a focus group technique. Data on 425 mining accidents was collected, and the required information was extracted. The expectation-maximization algorithm was used to estimate the conditional probability tables. Belief updating was used to determine which factors had the greatest effect on severity of accidents. RESULTS: Based on sensitivity analyses of the BN, training, type of accident, and activity type of workers were the most important factors influencing the severity of accidents. Of individual factors, workers’ experience had the strongest influence on the severity of accidents. CONCLUSIONS: Among the examined factors, safety training was the most important factor influencing the severity of accidents. Organizations may be able to reduce the severity of occupational injuries by holding safety training courses prepared based on the activity type of workers.
Accidents, Occupational ; Bayes Theorem ; Focus Groups ; Mining ; Occupational Injuries

The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequentist methods. The corresponding R packages were “gemtc” for the Bayesian approach and “netmeta” for the frequentist approach. In estimating a network meta-analysis model using a Bayesian framework, the “rjags” package is a common tool. “rjags” implements Markov chain Monte Carlo simulation with a graphical output. The estimated overall effect sizes, test for heterogeneity, moderator effects, and publication bias were reported using R software. The authors focus on two flexible models, Bayesian and frequentist, to determine overall effect sizes in network meta-analysis. This study focused on the practical methods of network meta-analysis rather than theoretical concepts, making the material easy to understand for Korean researchers who did not major in statistics. The authors hope that this study will help many Korean researchers to perform network meta-analyses and conduct related research more easily with R software.
Bayes Theorem ; Hope ; Markov Chains ; Population Characteristics ; Publication Bias

OBJECTIVES: Occupational injuries are known to be the main adverse outcome of occupational accidents. The purpose of the current study was to identify control strategies to reduce the severity of occupational injuries in the mining industry using Bayesian network (BN) analysis.METHODS: The BN structure was created using a focus group technique. Data on 425 mining accidents was collected, and the required information was extracted. The expectation-maximization algorithm was used to estimate the conditional probability tables. Belief updating was used to determine which factors had the greatest effect on severity of accidents.RESULTS: Based on sensitivity analyses of the BN, training, type of accident, and activity type of workers were the most important factors influencing the severity of accidents. Of individual factors, workers' experience had the strongest influence on the severity of accidents.CONCLUSIONS: Among the examined factors, safety training was the most important factor influencing the severity of accidents. Organizations may be able to reduce the severity of occupational injuries by holding safety training courses prepared based on the activity type of workers.
Accidents, Occupational ; Bayes Theorem ; Focus Groups ; Mining ; Occupational Injuries

The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequentist methods. The corresponding R packages were “gemtc” for the Bayesian approach and “netmeta” for the frequentist approach. In estimating a network meta-analysis model using a Bayesian framework, the “rjags” package is a common tool. “rjags” implements Markov chain Monte Carlo simulation with a graphical output. The estimated overall effect sizes, test for heterogeneity, moderator effects, and publication bias were reported using R software. The authors focus on two flexible models, Bayesian and frequentist, to determine overall effect sizes in network meta-analysis. This study focused on the practical methods of network meta-analysis rather than theoretical concepts, making the material easy to understand for Korean researchers who did not major in statistics. The authors hope that this study will help many Korean researchers to perform network meta-analyses and conduct related research more easily with R software.
Bayes Theorem ; Hope ; Markov Chains ; Population Characteristics ; Publication Bias