BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks

The BayesNetBP package has been developed for probabilistic reasoning and visualization in Bayesian networks with nodes that are purely discrete, continuous or mixed (discrete and continuous). Probabilistic reasoning enables a user to absorb information into a Bayesian network and make queries about how the probabilities within the network change in light of new information. The package was developed in the R programming language and is freely available from the Comprehensive R Archive Network. A shiny app with Cytoscape widgets provides an interactive interface for evidence absorption, queries, and visualizations

User-guided knowledge discovery using Bayesian networks.

A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has shown to be remarkably effective for some data-modeling problems. In this paper, we represent a computational model to apply Bayesian networks to knowledge discovery under uncertainty in a decision support system. Major features of this model include user-computer interaction and iterative information extraction. The user plays a primary role when determining the acceptance or refusal of intermediate information, while the computer in a supporting role crunches the numbers. Two computation streams are provided in the model: (1) Top-down stream: the user enters the expectation value for the goal, and then calculates the expected values for all the nodes in the network. (2) Bottom-up stream: the user input provides evidence into the network, and testifies the effect of the evidence to the goal node. We also designed and developed a software prototype to demonstrate the application of the proposed model. By using the software prototype, the user can easily construct and modify a Bayesian network. Not only does the network establish a connection between the customer requirement and the given source data, but also serves as the tool for our knowledge discovery process. With a tentative Bayesian network, propagations are carried out to testify the relevance represented in the network. After reviewing the results, the user may decide to remove some irrelevant components from the network, or he may want to add new components into the network, which will start a new iteration of the know ledge discovery process. As the repetition goes on, the user will get closer and closer to reach a Bayesian network that suits the problem domain. The information retrieved by applying the derived Bayesian network, together with the network itself, will then be used in further decision support

Consistency between direct trial evidence and Bayesian Mixed Treatment Comparison: Is head-to-head evidence always more reliable?

Objectives This study aims to highlight the benefits of Bayesian mixed treatment comparison (MTC), within a case study of the efficacy of three treatments (pegfilgrastim, filgrastim and lenograstim) for the prevention of febrile neutropenia (FN) following chemotherapy. Methods Two published meta-analyses have assessed the relative efficacy of the three treatments based on head-to-head trials. In the present study, all the trials from these meta-analyses were synthesised within a single network in a Bayesian MTC. Following a systematic review, the evidence base was then updated to include further recently-published trials. The metaanalyses and MTC were re-analysed using the updated evidence base. Results Using data from the previously-published meta-analyses only, the relative risk of FN for pegfilgrastim vs. no treatment was estimated at 0.08 (95% confidence interval: 0.03, 0.18) from the head-to-head trial and 0.27 (95% credible interval: 0.12, 0.60) from the MTC, reflecting strong inconsistency between the results of the direct and indirect methodologies. When subsequently-published head-to-head trials were included, the meta-analysis estimate increased to 0.29 (95% confidence interval: 0.15, 0.55), while the MTC gave a relative risk of 0.34 (95% credible interval: 0.23, 0.54). The initial MTC results were therefore a better predictor of subsequent study results than was the direct trial. The MTC was also able to estimate the probability that there were clinically significant difference in efficacy between the treatments. Conclusions Bayesian MTC provides clinically relevant information, including a measure of the consistency of direct and indirect evidence. Where inconsistency exists, it should not always be assumed that the direct evidence is more appropriate

Consistency between direct trial evidence and Bayesian Mixed Treatment Comparison: Is head-to-head evidence always more reliable?

Objectives: This study aims to highlight the benefits of Bayesian mixed treatment comparison (MTC), within a case study of the efficacy of three treatments (pegfilgrastim, filgrastim and lenograstim) for the prevention of febrile neutropenia (FN) following chemotherapy. Methods: Two published meta-analyses have assessed the relative efficacy of the three treatments based on head-to-head trials. In the present study, all the trials from these meta-analyses were synthesised within a single network in a Bayesian MTC. Following a systematic review, the evidence base was then updated to include further recently-published trials. The metaanalyses and MTC were re-analysed using the updated evidence base. Results: Using data from the previously-published meta-analyses only, the relative risk of FN for pegfilgrastim vs. no treatment was estimated at 0.08 (95% confidence interval: 0.03, 0.18) from the head-to-head trial and 0.27 (95% credible interval: 0.12, 0.60) from the MTC, reflecting strong inconsistency between the results of the direct and indirect methodologies. When subsequently-published head-to-head trials were included, the meta-analysis estimate increased to 0.29 (95% confidence interval: 0.15, 0.55), while the MTC gave a relative risk of 0.34 (95% credible interval: 0.23, 0.54). The initial MTC results were therefore a better predictor of subsequent study results than was the direct trial. The MTC was also able to estimate the probability that there were clinically significant difference in efficacy between the treatments. Conclusions: Bayesian MTC provides clinically relevant information, including a measure of the consistency of direct and indirect evidence. Where inconsistency exists, it should not always be assumed that the direct evidence is more appropriate

Bayesian models for aggregate and individual patient data component network meta-analysis.

Network meta-analysis can synthesize evidence from studies comparing multiple treatments for the same disease. Sometimes the treatments of a network are complex interventions, comprising several independent components in different combinations. A component network meta-analysis (CNMA) can be used to analyze such data and can in principle disentangle the individual effect of each component. However, components may interact with each other, either synergistically or antagonistically. Deciding which interactions, if any, to include in a CNMA model may be difficult, especially for large networks with many components. In this article, we present two Bayesian CNMA models that can be used to identify prominent interactions between components. Our models utilize Bayesian variable selection methods, namely the stochastic search variable selection and the Bayesian LASSO, and can benefit from the inclusion of prior information about important interactions. Moreover, we extend these models to combine data from studies providing aggregate information and studies providing individual patient data (IPD). We illustrate our models in practice using three real datasets, from studies in panic disorder, depression, and multiple myeloma. Finally, we describe methods for developing web-applications that can utilize results from an IPD-CNMA, to allow for personalized estimates of relative treatment effects given a patient's characteristics

Rapid earthquake loss updating of spatially distributed systems via sampling-based bayesian inference

Within moments following an earthquake event, observations collected from the affected area can be used to define a picture of expected losses and to provide emergency services with accurate information. A Bayesian Network framework could be used to update the prior loss estimates based on ground-motion prediction equations and fragility curves, considering various field observations (i.e., evidence). While very appealing in theory, Bayesian Networks pose many challenges when applied to real-world infrastructure systems, especially in terms of scalability. The present study explores the applicability of approximate Bayesian inference, based on Monte-Carlo Markov-Chain sampling algorithms, to a real-world network of roads and built areas where expected loss metrics pertain to the accessibility between damaged areas and hospitals in the region. Observations are gathered either from free-field stations (for updating the ground-motion field) or from structure-mounted stations (for the updating of the damage states of infrastructure components). It is found that the proposed Bayesian approach is able to process a system comprising hundreds of components with reasonable accuracy, time and computation cost. Emergency managers may readily use the updated loss distributions to make informed decisions