Use Of Bayesian Approach Research Articles

Spatially Varying Regression Coefficient Model For Predicting Stunting Hotspots In Indonesia

Stunting is a significant issue, particularly in the context of Indonesia. Identifying crucial risk factors is crucial for mitigating and developing effective strategies to control stunting. A Bayesian approach was employed to develop a regression model that incorporates spatial variation, allowing risk factors to vary across different districts and cities. The aim was to obtain the most optimal regression model. The analysis revealed that the impact of immunization varies across districts and cities in Indonesia when it comes to explaining the differences in stunting prevalence. The hotspot prediction results indicate that most urban districts in Indonesia remain hotspot areas, with a stunting risk exceeding 20%. The government must ensure the effective implementation of the immunization program in order to mitigate the prevalence of stunting in Indonesia. The novelty of this research lies in the use of Bayesian approaches to spatial analysis in identifying and understanding stunting risk factors as well as the prediction of stunting hotspots in Indonesia. This approach provides in-depth insight into local variations in the prevalence of stunting and the effectiveness of health interventions, which supports more effective and targeted policy development.

Bayesian Estimation of Inverted Beta Mixture Models With Extended Stochastic Variational Inference for Positive Vector Classification.

The finite inverted beta mixture model (IBMM) has been proven to be efficient in modeling positive vectors. Under the traditional variational inference framework, the critical challenge in Bayesian estimation of the IBMM is that the computational cost of performing inference with large datasets is prohibitively expensive, which often limits the use of Bayesian approaches to small datasets. An efficient alternative provided by the recently proposed stochastic variational inference (SVI) framework allows for efficient inference on large datasets. Nevertheless, when using the SVI framework to address the non-Gaussian statistical models, the evidence lower bound (ELBO) cannot be explicitly calculated due to the intractable moment computation. Therefore, the algorithm under the SVI framework cannot directly use stochastic optimization to optimize the ELBO, and an analytically tractable solution cannot be derived. To address this problem, we propose an extended version of the SVI framework with more flexibility, namely, the extended SVI (ESVI) framework. This framework can be used in many non-Gaussian statistical models. First, some approximation strategies are applied to further lower the ELBO to avoid intractable moment calculations. Then, stochastic optimization with noisy natural gradients is used to optimize the lower bound. The excellent performance and effectiveness of the proposed method are verified in real data evaluation.

Bayesian Methods in Human Drug and Biological Products Development in CDER and CBER.

The Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER) of the U.S. Food and Drug Administration (FDA) have been leaders in protecting and promoting the U.S. public health by helping to ensure that safe and effective drugs and biological products are available in the United States for those who need them. The null hypothesis significance testing approach, along with other considerations, is typically used to demonstrate the effectiveness of a drug or biological product. The Bayesian framework presents an alternative approach to demonstrate the effectiveness of a treatment. This article discusses the Bayesian framework for drug and biological product development, highlights key settings in which Bayesian approaches may be appropriate, and provides recent examples of the use of Bayesian approaches within CDER and CBER.

Regulatory issues and the potential use of Bayesian approaches for early drug approval systems in Japan.

Bayesian methods quantify and interpret the therapeutic effects of investigational drugs based on probability statements of the posterior distribution. However, the basic principle underlying the use of Bayesian methods in registration trials for new drug applications in Japan has not been adequately discussed. Motivated by the two drug approval systems for early approval recently enacted in Japan, we present our perspectives on the application of the Bayesian approach in registration trials in Japan. These are based on discussions among academic, industry, and regulatory experts at invited workshops. Based on the aforementioned early approval systems, we discuss putative common regulatory issues related to the use of the Bayesian approach and introduce instances of clinical trials in which the Bayesian approach is expected to be used. This article provides a well-defined premise for the discussion between industry and regulatory agencies on the use of Bayesian approaches for early drug approval in Japan.

Challenges and potential strategies utilizing external data for efficacy evaluation in small-sized clinical trials

ABSTRACT In clinical trials for diseases with very small patient populations, trial investigators may encounter recruitment difficulties. It can be challenging to conduct clinical trials with enough power to detect a treatment effect, and randomization may not be feasible due to timeline, budget, and ethical concerns. To bring breakthrough therapies to the market quickly, it is important to come up with efficient approaches to utilizing individual patient data through improved study design and sound statistical methods. Emerging topics in this area include the use of Bayesian approaches to flexibly incorporate prior information into the current clinical trials, the use of historical controls to efficiently conduct trials that will reduce the number of subjects recruited and ease ethical considerations, and the use of innovative study designs, such as a platform design, to improve the efficiency and speed of the medical therapy development progress. In this paper, we describe three scenarios which highlight some of the challenges encountered in small-sized clinical trial development and provide potential statistical approaches to overcome the aforementioned challenges.

The risk assessment of a crude oil pipeline using fuzzy and bayesian based bow-tie analysis

Cross country pipelines are one of the major modes of transportation of petroleum products. Due to external and internal factors, the failure probability of pipeline is increasing over the past decades. The failure of pipelines can cause fires, which can result in environmental damage, loss of materials and further resulting into casualties. Effective risk assessment is vital to prevent and mitigate such potential incidents. In this work, an efficient tool that makes use of Bayesian approach along with Bow-tie analysis is used to obtain the failure frequencies of all the identified causes that may lead to failure of the selected pipeline located in Kerala, India. Bayesian networks can perform effective dynamic risk analysis by considering the conditional dependencies between various basic events leading to pipeline failure. The fuzzy logic and expert elicitation method are incorporated to determine the prior failure frequencies of all the identified causes of pipeline failure. The effectiveness of Bayesian network in performing forward analysis to determine the probabilities of pipeline failure consequences is demonstrated. This study also identifies the weak links associated with the occurrence of particular consequences, so that adequate measures can be taken to rectify them.

The U.S. Food and Drug Administration's Complex Innovative Trial Design Pilot Meeting Program: Progress to date.

The Center for Drug Evaluation and Research and the Center for Biologics Evaluation and Research of the U.S. Food and Drug Administration have been leaders in advancing science to protect and promote public health by ensuring that safe and effective drugs and biological products are available to those who need them. Recently, new therapeutic discoveries, increased understanding of disease mechanisms, the need for innovation to optimally use resources, and global public health crises have led to an evolving drug development landscape. As a result, the U.S. Food and Drug Administration and medical product developers are faced with unique challenges and opportunities. The U.S. Food and Drug Administration is proactively meeting the challenges of this evolving landscape through various efforts, including the Complex Innovative Trial Design Pilot Meeting Program. Our focus, here, will be on the pilot meeting program. The U.S. Food and Drug Administration has defined a process to facilitate the implementation of the Complex Innovative Trial Design Pilot Meeting Program. The process is transparent and outlines the steps and timeline for submission, review, and meetings. Five submitted meeting requests have been selected for participation in the Complex Innovative Trial Design Pilot Meeting Program. The pilot meeting program has been successful in further educating stakeholders on the potential uses of complex innovative designs in trials intended to provide substantial evidence of effectiveness. The selected submissions, thus far, have all utilized a Bayesian framework. The reasons for the use of Bayesian approaches may be due to the flexibility provided, the ability to incorporate multiple sources of evidence, and a desire to better understand the U.S. Food and Drug Administration perspective on such approaches. We are confident the pilot meeting program will have continued success and impact the collective goal of bringing safe and effective medical products to patients.

On Bayesian approach to composite Pareto models.

In data modelling using the composite Pareto distribution, any observations above a particular threshold value are assumed to follow Pareto type distribution, whereas the rest of the observations are assumed to follow a different distribution. This paper proposes on the use of Bayesian approach to the composite Pareto models involving specification of the prior distribution on the proportion of data coming from the Pareto distribution, instead of assuming the prior distribution on the threshold, as often done in the literature. Based on a simulation study, it is found that the parameter estimates determined when using uniform prior on the proportion is less biased as compared to the point estimates determined when using uniform prior on the threshold. Applications on income data and finance are included for illustrative examples.

Bayesian survival analysis for early detection of treatment effects in phase 3 clinical trials

Despite appealing characteristics for the clinical trials setting, Bayesian inference methods remain scarcely used, especially in randomized controlled clinical trials (RCT). This is particularly true when dealing with a survival endpoint, likely due to the additional complexities to model specifications. We propose to use Bayesian inference to estimate the treatment effect in this setting, using a proportional hazards (PH) model for right-censored data. Implementation of such an estimation process is illustrated on two working examples from cancer RCTs, the ALLOZITHRO and the CLL7-SA trials, both originally analyzed using a frequentist approach. In these two different settings, we show that Bayesian sequential analyses can provide early insight on treatment effect in RCTs. Relying on posterior distributions and predictive posterior probabilities, we find that Bayesian sequential analyses of the ALLOZITHRO trial, which was terminated early due to an unanticipated deleterious effect of the intervention on survival, allow quantifying early that the treatment effect was opposite to what was expected. Then, incorporating historical data in the sequential analyses of the CLL7-SA trial would have allowed the treatment effect to be closer to the protocol hypothesis. These post-hoc results give grounds to advocate for a wider use of Bayesian approaches in RCTs, including those with right-censored endpoints, as informative decision tools.

A Bayesian approach to estimating the population prevalence of mood and anxiety disorders using multiple measures.

There is currently no universally accepted measure for population-based surveillance of mood and anxiety disorders. As such, the use of multiple linked measures could provide a more accurate estimate of population prevalence. Our primary objective was to apply Bayesian methods to two commonly employed population measures of mood and anxiety disorders to make inferences regarding the population prevalence and measurement properties of a combined measure. We used data from the 2012 Canadian Community Health Survey - Mental Health linked to health administrative databases in Ontario, Canada. Structured interview diagnoses were obtained from the survey, and health administrative diagnoses were identified using a standardised algorithm. These two prevalence estimates, in addition to data on the concordance between these measures and prior estimates of their psychometric properties, were used to inform our combined estimate. The marginal posterior densities of all parameters were estimated using Hamiltonian Monte Carlo (HMC), a Markov Chain Monte Carlo technique. Summaries of posterior distributions, including the means and 95% equally tailed posterior credible intervals, were used for interpretation of the results. The combined prevalence mean was 8.6%, with a credible interval of 6.8-10.6%. This combined estimate sits between Bayesian-derived prevalence estimates from administrative data-derived diagnoses (mean = 7.4%) and the survey-derived diagnoses (mean = 13.9%). The results of our sensitivity analysis suggest that varying the specificity of the survey-derived measure has an appreciable impact on the combined posterior prevalence estimate. Our combined posterior prevalence estimate remained stable when varying other prior information. We detected no problematic HMC behaviour, and our posterior predictive checks suggest that our model can reliably recreate our data. Accurate population-based estimates of disease are the cornerstone of health service planning and resource allocation. As a greater number of linked population data sources become available, so too does the opportunity for researchers to fully capitalise on the data. The true population prevalence of mood and anxiety disorders may reside between estimates obtained from survey data and health administrative data. We have demonstrated how the use of Bayesian approaches may provide a more informed and accurate estimate of mood and anxiety disorders in the population. This work provides a blueprint for future population-based estimates of disease using linked health data.

Assessing the Characteristics of the Maximal Spring Flood Runoff in the Don Basin under Nonstationary Conditions

Data on the maximal runoff of rivers in the Don Basin during spring flood are generalized, the cases of nonstationarity of time series are identified along with periods when such changes have taken place. The obtained results are used to evaluate the characteristics of maximal flow of spring flood under nonstationary conditions with the use of Bayesian approach. The results are given as a map of the depths of maximal runoff of spring flood with 1% exceedance probability and recommended for hydrological calculations when designing water-management facilities.

Bayesian group sequential designs for phase III emergency medicine trials: a case study using the PARAMEDIC2 trial

BackgroundPhase III trials often require large sample sizes, leading to high costs and delays in clinical decision-making. Group sequential designs can improve trial efficiency by allowing for early stopping for efficacy and/or futility and thus may decrease the sample size, trial duration and associated costs. Bayesian approaches may offer additional benefits by incorporating previous information into the analyses and using decision criteria that are more practically relevant than those used in frequentist approaches. Frequentist group sequential designs have often been used for phase III studies, but the use of Bayesian group sequential designs is less common. The aim of this work was to explore how Bayesian group sequential designs could be constructed for phase III trials conducted in emergency medicine.MethodsThe PARAMEDIC2 trial was a phase III randomised controlled trial that compared the use of adrenaline to placebo in out-of-hospital cardiac arrest patients on 30-day survival rates. It used a frequentist group sequential design to allow early stopping for efficacy or harm. We constructed several alternative Bayesian group sequential designs and studied their operating characteristics via simulation. We then virtually re-executed the trial by applying the Bayesian designs to the PARAMEDIC2 data to demonstrate what might have happened if these designs had been used in practice.ResultsWe produced three alternative Bayesian group sequential designs, each of which had greater than 90% power to detect the target treatment effect. A Bayesian design which performed interim analyses every 500 patients recruited produced the lowest average sample size. Using the alternative designs, the PARAMEDIC2 trial could have declared adrenaline superior for 30-day survival with approximately 1500 fewer patients.ConclusionsUsing the PARAMEDIC2 trial as a case study, we demonstrated how Bayesian group sequential designs can be constructed for phase III emergency medicine trials. The Bayesian framework enabled us to obtain efficient designs using decision criteria based on the probability of benefit or harm. It also enabled us to incorporate information from previous studies on the treatment effect via the prior distributions. We recommend the wider use of Bayesian approaches in phase III clinical trials.Trial registrationPARAMEDIC2 Trial registration ISRCTN, ISRCTN73485024. Registered 13 March 2014, http://www.isrctn.com/ISRCTN73485024

Bayesian updates for indoor thermal comfort models

Achieving thermal comfort through sustainable indoor design is an increasing concern. Thermal comfort modelling is crucial for achieving building energy saving. This study reviews and categorizes major developments and trends in the field of thermal comfort research in recent years. Discrepancies between actual and predicted results of thermal sensation and thermal satisfaction suggests a performance gap in Fanger's model. Based on the current research gaps identified, a practical solution is proposed to improve the reliability of thermal comfort predictions. Two Bayesian updating protocols, namely individual updating and global updating, are put forward and the use of Bayesian approach to systemically update current thermal comfort beliefs with openly available field data is demonstrated. Besides being a practical tool for modelling thermal comfort using the best information available (i.e. existing models and field survey data), the proposed Bayesian updating provides an achievable solution to the present challenges in establishing a reliable thermal comfort prediction model.

Model Risk Management: Limits and Future of Bayesian Approaches

This paper discusses the use of Bayesian approaches when the models are misspecified (model risk). In particular we explore the limits and future of Bayesian approaches in order to provide answers to the following questions recently asked by the prudential supervision for Finance/Insurance: How to measure model risk? How to use in a coherent way the different misspecified models (as rating models) usually employed within and between financial institutions.

Bayesian logistic regression approaches to predict incorrect DRG assignment.

Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.

Computational Neuropsychology and Bayesian Inference.

Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine ‘prior’ beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology – optimal inference with suboptimal priors – and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient’s behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.

Bayesian Parameter Identification and Model Selection for Normalized Modulus Reduction Curves of Soils

ABSTACTThis work develops a procedure that involves the use of Bayesian approach to quantify data scatterness, estimates the optimal values of model parameters, and selects the most appropriate model for the construction of normalized modulus reduction curves of soils. The proposed procedure is then demonstrated using real observation data based on a set of comprehensive resonant column tests on coarse-grained soils conducted in the study.

VIX Forecast Under Different Volatility Specifications

Stochastic volatility (SV) models are theoretically more attractive than the GARCH type of models as it allows additional randomness. The classical SV models deduce a continuous probability distribution for volatility so that it does not admit a computable likelihood function. The estimation requires the use of Bayesian approach. A recent approach considers discrete stochastic autoregressive volatility models for a bounded and tractable likelihood function. Hence, a maximum likelihood estimation can be achieved. This paper proposes a general approach to link SV models under the physical probability measure, both continuous and discrete types, to their processes under a martingale measure. Doing so enables us to deduce the close-form expression for the VIX forecast for the both SV models and GARCH type models. We then carry out an empirical study to compare the performances of the continuous and discrete SV models using GARCH models as benchmark models.

Critically evaluating the theory and performance of Bayesian analysis of macroevolutionary mixtures

Bayesian analysis of macroevolutionary mixtures (BAMM) has recently taken the study of lineage diversification by storm. BAMM estimates the diversification-rate parameters (speciation and extinction) for every branch of a study phylogeny and infers the number and location of diversification-rate shifts across branches of a tree. Our evaluation of BAMM reveals two major theoretical errors: (i) the likelihood function (which estimates the model parameters from the data) is incorrect, and (ii) the compound Poisson process prior model (which describes the prior distribution of diversification-rate shifts across branches) is incoherent. Using simulation, we demonstrate that these theoretical issues cause statistical pathologies; posterior estimates of the number of diversification-rate shifts are strongly influenced by the assumed prior, and estimates of diversification-rate parameters are unreliable. Moreover, the inability to correctly compute the likelihood or to correctly specify the prior for rate-variable trees precludes the use of Bayesian approaches for testing hypotheses regarding the number and location of diversification-rate shifts using BAMM.

Idle thoughts of a 'well-calibrated' Bayesian in clinical drug development.

The use of Bayesian approaches in the regulated world of pharmaceutical drug development has not been without its difficulties or its critics. The recent Food and Drug Administration regulatory guidance on the use of Bayesian approaches in device submissions has mandated an investigation into the operating characteristics of Bayesian approaches and has suggested how to make adjustments in order that the proposed approaches are in a sense calibrated. In this paper, I present examples of frequentist calibration of Bayesian procedures and argue that we need not necessarily aim for perfect calibration but should be allowed to use procedures, which are well-calibrated, a position supported by the guidance.