Memory type Bayesian adaptive max-EWMA control chart for weibull processes

The simultaneous monitoring of both the process mean and dispersion has gained considerable attention in statistical process control, especially when the process follows the normal distribution. This paper introduces a novel Bayesian adaptive maximum exponentially weighted moving average (Max-EWMA) control chart, designed to jointly monitor the mean and dispersion of a non-normal process. This is achieved through the utilization of the inverse response function, particularly suitable for processes conforming to a Weibull distribution. To assess the effectiveness of the proposed control chart, we employed the average run length (ARL) and the standard deviation of run length (SDRL). Subsequently, we compared the performance of our proposed control chart with that of an existing Max-EWMA control chart. Our findings suggest that the proposed control chart demonstrates a higher level of sensitivity in detecting out-of-control signals. Finally, to illustrate the effectiveness of our Bayesian Max-EWMA control chart under various Loss Functions (LFs) for a Weibull process, we present a practical case study focusing on the hard-bake process in the semiconductor manufacturing industry. This case study highlights the adaptability of the chart to different scenarios. Our results provide compelling evidence of the exceptional performance of the suggested control chart in rapidly detecting out-of-control signals during the hard-bake process, thereby significantly contributing to the improvement of process monitoring and quality control.

Age and growth of the blue shark Prionace glauca (Linnaeus, 1758) in the Ecuadorian Pacific: Bayesian multi-models

The blue shark Prionace glauca plays a critical role as a predator in marine ecosystems but is threatened by by-catch. To obtain more precise biological data, a Bayesian approach was used, and 536 vertebrae samples collected during 1 year at the landing stage called "Playita Mía" Manta, Ecuador, were analysed. The objective was to estimate the age and growth parameters of the species. The size of the specimens varied between 116 and 310 cm in total length (TL). Using a Bayesian approach based on the Markov Chain Monte Carlo (MCMC) method, growth parameters were evaluated. The von Bertalanffy model was the one that best fitted the data and provided more adequate estimates (females:L ∞ $$ L\infty $$ = 325.50 cm,L 0 $$ {L}_0 $$ = 53.23 cm, and k = 0.12 years; males:L ∞ $$ L\infty $$ = 331.47 cm,L 0 $$ {L}_0 $$ = 51.59 cm, k = 0.12 years -1; combined sexes:L ∞ $$ L\infty $$ = 329.65 cm,L 0 $$ {L}_0 $$ = 53.64 cm, k = 0.11 year-1). The results indicated that females and males have a similar growth, and that the species has a slow growth. Further studies using multi-model Bayesian approaches and covering a broader range of sizes in the Pacific Ocean are suggested. These studies will provide crucial information for the management and conservation of this species.

Bayesian reinforcement learning: A basic overview

We and other animals learn because there is some aspect of the world about which we are uncertain. This uncertainty arises from initial ignorance, and from changes in the world that we do not perfectly know; the uncertainty often becomes evident when our predictions about the world are found to be erroneous. The Rescorla-Wagner learning rule, which specifies one way that prediction errors can occasion learning, has been hugely influential as a characterization of Pavlovian conditioning and, through its equivalence to the delta rule in engineering, in a much wider class of learning problems. Here, we review the embedding of the Rescorla-Wagner rule in a Bayesian context that is precise about the link between uncertainty and learning, and thereby discuss extensions to such suggestions as the Kalman filter, structure learning, and beyond, that collectively encompass a wider range of uncertainties and accommodate a wider assortment of phenomena in conditioning.

Trends in chronic childhood undernutrition in Bangladesh for small domains

Chronic childhood undernutrition, known as stunting, is an important population health problem with short- and long-term adverse outcomes. Bangladesh has made strides to reduce chronic childhood undernutrition, yet progress is falling short of the 2030 Sustainable Development Goals targets. This study estimates trends in age-specific chronic childhood undernutrition in Bangladesh's 64 districts during 1997-2018, using underlying direct estimates extracted from seven Demographic and Health Surveys in the development of small area time-series models. These models combine cross-sectional, temporal, and spatial data to predict in all districts in both survey and non-survey years. Nationally, there has been a steep decline in stunting from about three in five to one in three children. However, our results highlight significant inequalities in chronic undernutrition, with several districts experiencing less pronounced declines. These differences are more nuanced at the district-by-age level, with only districts in more socio-economically advantaged areas of Bangladesh consistently reporting declines in stunting across all age groups.

Estimation of the percentile of Birnbaum-Saunders distribution and its application to PM2.5 in Northern Thailand

The Birnbaum-Saunders distribution plays a crucial role in statistical analysis, serving as a model for failure time distribution in engineering and the distribution of particulate matter 2.5 (PM2.5) in environmental sciences. When assessing the health risks linked to PM2.5, it is crucial to give significant weight to percentile values, particularly focusing on lower percentiles, as they offer a more precise depiction of exposure levels and potential health hazards for the population. Mean and variance metrics may not fully encapsulate the comprehensive spectrum of risks connected to PM2.5 exposure. Various approaches, including the generalized confidence interval (GCI) approach, the bootstrap approach, the Bayesian approach, and the highest posterior density (HPD) approach, were employed to establish confidence intervals for the percentile of the Birnbaum-Saunders distribution. To assess the performance of these intervals, Monte Carlo simulations were conducted, evaluating them based on coverage probability and average length. The results demonstrate that the GCI approach is a favorable choice for estimating percentile confidence intervals. In conclusion, this article presents the results of the simulation study and showcases the practical application of these findings in the field of environmental sciences.

Joint modeling of longitudinal and competing-risk data using cumulative incidence functions for the failure submodels accounting for potential failure cause misclassification through double sampling

Most of the literature on joint modeling of longitudinal and competing-risk data is based on cause-specific hazards, although modeling of the cumulative incidence function (CIF) is an easier and more direct approach to evaluate the prognosis of an event. We propose a flexible class of shared parameter models to jointly model a normally distributed marker over time and multiple causes of failure using CIFs for the survival submodels, with CIFs depending on the "true" marker value over time (i.e., removing the measurement error). The generalized odds rate transformation is applied, thus a proportional subdistribution hazards model is a special case. The requirement that the all-cause CIF should be bounded by 1 is formally considered. The proposed models are extended to account for potential failure cause misclassification, where the true failure causes are available in a small random sample of individuals. We also provide a multistate representation of the whole population by defining mutually exclusive states based on the marker values and the competing risks. Based solely on the assumed joint model, we derive fully Bayesian posterior samples for state occupation and transition probabilities. The proposed approach is evaluated in a simulation study and, as an illustration, it is fitted to real data from people with HIV.

Bayesian second-order sensitivity of longitudinal inferences to non-ignorability: an application to antidepressant clinical trial data

Incomplete data is a prevalent complication in longitudinal studies due to individuals' drop-out before intended completion time. Currently available methods via commercial software for analyzing incomplete longitudinal data at best rely on the ignorability of the drop-outs. If the underlying missing mechanism was non-ignorable, potential bias arises in the statistical inferences. To remove the bias when the drop-out is non-ignorable, joint complete-data and drop-out models have been proposed which involve computational difficulties and untestable assumptions. Since the critical ignorability assumption is unverifiable based on the observed part of the sample, some local sensitivity indices have been proposed in the literature. Specifically, Eftekhari Mahabadi (Second-order local sensitivity to non-ignorability in Bayesian inferences. Stat Med 2018;59:55-95) proposed a second-order local sensitivity tool for Bayesian analysis of cross-sectional studies and show its better performance for handling bias compared with the first-order ones. In this paper, we aim to extend this index for the Bayesian sensitivity analysis of normal longitudinal studies with drop-outs. The index is driven based on a selection model for the drop-out mechanism and a Bayesian linear mixed-effect complete-data model. The presented formulas are calculated using the posterior estimation and draws from the simpler ignorable model. The method is illustrated via some simulation studies and sensitivity analysis of a real antidepressant clinical trial data. Overall, the numerical analysis showed that when repeated outcomes are subject to missingness, regression coefficient estimates are nearly approximated well by a linear function in the neighbourhood of MAR model, but there are a considerable amount of second-order sensitivity for the error term and random effect variances in Bayesian linear mixed-effect model framework.

How to select the initial dose for a pediatric study?

In typical clinical development programs, a new drug is first developed for the adult use. Drugs are often approved for adult use or in the process of obtaining approval in adults in the target indication before pediatric development is initiated. In designing the first pediatric clinical trial, one of the challenges is to select the initial dose to be tested. The ICH E11 R1 guidance advises that chronologic age alone may not always be the most appropriate categorical determinant to define developmental subgroups in pediatric studies. In this manuscript, the approaches to utilize available data in adults related to those factors beyond age to inform the starting dose selection in pediatric drug development are discussed. Practical considerations and approaches are provided for informing pediatric starting dose. Additional considerations to use pre-clinical information are provided in the case when adult information is limited or not available.

A cardiologist-like computer-aided interpretation framework to improve arrhythmia diagnosis from imbalanced training datasets

Arrhythmias can pose a significant threat to cardiac health, potentially leading to serious consequences such as stroke, heart failure, cardiac arrest, shock, and sudden death. In computer-aided electrocardiogram interpretation systems, the inclusion of certain classes of arrhythmias, which we term "aggressive" or "bullying," can lead to the underdiagnosis of other "vulnerable" classes. To address this issue, a method for arrhythmia diagnosis is proposed in this study. This method combines morphological-characteristic-based waveform clustering with Bayesian theory, drawing inspiration from the diagnostic reasoning of experienced cardiologists. The proposed method achieved optimal performance in macro-recall and macro-precision through hyperparameter optimization, including spliced heartbeats and clusters. In addition, with increasing bullying by aggressive arrhythmias, our model obtained the highest average recall and the lowest average drop in recall on the nine vulnerable arrhythmias. Furthermore, the maximum cluster characteristics were found to be consistent with established arrhythmia diagnostic criteria, lending interpretability to the proposed method.

Process models of verbal memory in cancer survivors: Bayesian process modeling approach to variation in test scores

INTRODUCTION

Verbal memory is a complex and fundamental aspect of human cognition. However, traditional sum-score analyses of verbal learning tests oversimplify underlying verbal memory processes. We propose using process models to subdivide memory into multiple processes, which helps in localizing the most affected processes in impaired verbal memory. Additionally, the model can be used to address score and process variability. This study aims to investigate the effects of cancer and its treatment on verbal memory, as well as provide a demonstration of how process models can be used to investigate the uncertainty in neuropsychological test scores.

METHOD

We present an investigation of memory process scores in non-CNS cancer survivors (n = 184) and no-cancer controls (n = 204). The participants completed the Amsterdam Cognition Scan (ACS), in which classical neuropsychological tests are digitally recreated for online at-home administration. We analyzed data from the ACS equivalent of a Verbal Learning Test using both traditional sum scores and a Bayesian process model.

RESULTS

Analysis of the sum score indicated that patients scored lower than controls on immediate recall but found no difference for delayed recall. The process model analysis indicated a small difference between patients and controls in immediate retrieval from both the partially learned and learned states, with no differences in learning or delayed retrieval processes. Individual-level analysis shows considerable uncertainty for sum scores. Sum scores were more certain than single trials. Retrieval parameters also showed less uncertainty than learning parameters.

CONCLUSION

The Bayesian process model increased the informativity of Verbal Learning test data, by showing uncertainty of the traditional sum score measurements as well as how the underlying processes differed between populations. Additionally, the model grants insight into underlying memory processes for individuals and how these processes vary within and between them.

Phantom and clinical evaluation of Block Sequential Regularized Expectation Maximization (BSREM) reconstruction algorithm in 68Ga-PSMA PET-CT studies

In this study, we aimed to examine the effect of varying β-values in the block sequential regularized expectation maximization (BSREM) algorithm under differing lesion sizes to determine an optimal penalty factor for clinical application. The National Electrical Manufacturers Association phantom and 15 prostate cancer patients were injected with 68Ga-PSMA and scanned using a GE Discovery IQ PET/CT scanner. Images were reconstructed using ordered subset expectation maximization (OSEM) and BSREM with different β-values. Then, the background variability (BV), contrast recovery, signal-to-noise ratio, and lung residual error were measured from the phantom data, and the signal-to-background ratio (SBR) and contrast from the clinical data. The increment of BV using a β-value of 100 was 120.0%, and the decrement of BV using a β-value of 1000 was 40.5% compared to OSEM. As β decreased from 1000 to 100, the [Formula: see text] increased by 59.0% for a sphere with a diameter of 10 mm and 26.4% for a sphere with a diameter of 37 mm. Conversely, [Formula: see text] increased by 140.5% and 29.0% in the smallest and largest spheres, respectively. Furthermore, the Δ[Formula: see text] and Δ[Formula: see text] were - 41.1% and - 36.7%, respectively. In the clinical study, OSEM exhibited the lowest SBR and contrast. When the β-value was reduced from 500 to 100, the SBR and contrast increased by 69.7% and 71.8% in small and 35.6% and 33.0%, respectively, in large lesions. Moreover, the optimal β-value decreased as lesion size decreased. In conclusion, a β-value of 400 is optimal for small lesion reconstruction, while β-values of 600 and 500 are optimal for large lesions in phantom and clinical studies, respectively.

Bayesian borrowing from historical control data in a vaccine efficacy trial

In the context of vaccine efficacy trial where the incidence rate is very low and a very large sample size is usually expected, incorporating historical data into a new trial is extremely attractive to reduce sample size and increase estimation precision. Nevertheless, for some infectious diseases, seasonal change in incidence rates poses a huge challenge in borrowing historical data and a critical question is how to properly take advantage of historical data borrowing with acceptable tolerance to between-trials heterogeneity commonly from seasonal disease transmission. In this article, we extend a probability-based power prior which determines the amount of information to be borrowed based on the agreement between the historical and current data, to make it applicable for either a single or multiple historical trials available, with constraint on the amount of historical information to be borrowed. Simulations are conducted to compare the performance of the proposed method with other methods including modified power prior (MPP), meta-analytic-predictive (MAP) prior and the commensurate prior methods. Furthermore, we illustrate the application of the proposed method for trial design in a practical setting.

A Bayesian-adaptive decision-theoretic approach can reduce the sample sizes for multiarm exercise oncology trials

OBJECTIVES

Adaptive designs may reduce trial sample sizes and costs. This study illustrates a Bayesian-adaptive decision-theoretic design applied to a multiarm exercise oncology trial.

STUDY DESIGN AND SETTING

In the Physical exercise during Adjuvant Chemotherapy Effectiveness Study (PACES) trial, 230 breast cancer patients receiving chemotherapy were randomized to supervised resistance and aerobic exercise (OnTrack), home-based physical activity (OncoMove) or usual care (UC). Data were reanalyzed as an adaptive trial using both Bayesian decision-theoretic and a frequentist group-sequential approach incorporating interim analyses after every 36 patients. Endpoint was chemotherapy treatment modifications (any vs. none). Bayesian analyses were performed for different continuation thresholds and settings with and without arm dropping and both in a 'pick-the-winner' and a 'pick-all-treatments-superior-to-control' setting.

RESULTS

Treatment modifications occurred in 34% of patients in UC and OncoMove vs. 12% in OnTrack (P = 0.002). Using a Bayesian-adaptive decision-theoretic design, OnTrack was identified as most effective after 72 patients in the 'pick-the-winner' setting and after 72-180 patients in the 'pick-all-treatments-superior-to-control' setting. In a frequentist setting, the trial would have been stopped after 180 patients, and the proportion of patients with treatment modifications was significantly lower for OnTrack than UC.

CONCLUSION

A Bayesian-adaptive decision-theoretic approach substantially reduced the sample size required for this three-arm exercise trial, especially in the 'pick-the-winner' setting.

Decoupling between carbon source and sink induced by responses of daily stem growth to water availability in subtropical urban forests

Urban forests are anticipated to offer sustainable ecosystem services, necessitating a comprehensive understanding of the ways in which trees respond to environmental changes. This study monitored stem radius fluctuations in Cinnamomum camphora and Taxodium distichum var. imbricatum trees using high-resolution dendrometers at two sites, respectively. Gross primary production (GPP) was measured using eddy-covariance techniques and aggregated to daily sums. Hourly and daily stem radius fluctuations were estimated across both species, and the responses of stems to radiation (Rg), air temperature (Tair), vapor pressure deficit (VPD), and soil humidity (SoilH) were quantified using Bayesian linear models. The diel growth patterns of the monitored trees showed similar characteristics at the species level. Results revealed that trees growth occurred primarily at night, with the lowest hourly contribution to total growth and probability for growth occurring in the afternoon. Furthermore, the Bayesian models indicated that VPD was the most important driver of daily growth and growth probability. After considering the potential constraints imposed by VPD, a modified Gompertz equation showed good performance, with R² ranging from 0.94 to 0.99 for the relationship between accumulative growth and time. Bayes-based model-independent data assimilation using advanced Markov chain Monte Carlo (MCMC) algorithms provided deeper insights into nonlinear model parameterization. Finally, the quantified relationship between GPP and stem daily growth revealed that the decoupling between carbon source and sink increased with VPD. These findings provided direct empirical evidence for VPD as a key driver of daily growth patterns and raise questions about carbon neutrality accounting under future climate change given the uncertainties induced by increased water stress limitations on carbon utilization.

Incorporating non-genetic evidence in large scale missing person searches: A general approach beyond filtering

The search for missing persons implies several steps, from the preliminary investigation that involves collecting background data related to the case to the genetic kinship testing. Despite its crucial importance in identifications, only some approaches mathematically formalize the possibility of using preliminary investigation data. In some cases, a filtering strategy is applied, which implies selecting a subset of possible victims where some non-genetic variables perfectly match those of the missing. Through a Bayesian approach, we propose a mathematical model for computing the prior odds based on non-genetic variables usually collected during the preliminary investigation, such as biological sex, hair colour, and age. We use computational simulations to show how to incorporate these prior odds in DNA-database searches. Importantly, our results suggest that applying the proposed model leads to better search performance in underpowered cases from the genetic point of view, where few or distant relatives of the missing person are available for genotyping. Furthermore, the results are also helpful when using non-genetic data for prior odds in well-powered cases, where genetic data are enough to reach a reliable conclusion. It performs better than other approaches, such as using non-genetic data for filtering. The software mispitools, freely available on CRAN, implements all described methods (https://CRAN.R-project.org/package=mispitools).

Assessing phenotypic virulence of Salmonella enterica across serovars and sources

Introduction

Whole genome sequencing (WGS) is increasingly used for characterizing foodborne pathogens and it has become a standard typing technique for surveillance and research purposes. WGS data can help assessing microbial risks and defining risk mitigating strategies for foodborne pathogens, including Salmonella enterica.

Methods

To test the hypothesis that (combinations of) different genes can predict the probability of infection [P(inf)] given exposure to a certain pathogen strain, we determined P(inf) based on invasion potential of 87 S. enterica strains belonging to 15 serovars isolated from animals, foodstuffs and human patients, in an in vitro gastrointestinal tract (GIT) model system. These genomes were sequenced with WGS and screened for genes potentially involved in virulence. A random forest (RF) model was applied to assess whether P(inf) of a strain could be predicted based on the presence/absence of those genes. Moreover, the association between P(inf) and biofilm formation in different experimental conditions was assessed.

Results and Discussion

P(inf) values ranged from 6.7E-05 to 5.2E-01, showing variability both among and within serovars. P(inf) values also varied between isolation sources, but no unambiguous pattern was observed in the tested serovars. Interestingly, serovars causing the highest number of human infections did not show better ability to invade cells in the GIT model system, with strains belonging to other serovars displaying even higher infectivity. The RF model did not identify any virulence factor as significant P(inf) predictors. Significant associations of P(inf) with biofilm formation were found in all the different conditions for a limited number of serovars, indicating that the two phenotypes are governed by different mechanisms and that the ability to form biofilm does not correlate with the ability to invade epithelial cells. Other omics techniques therefore seem more promising as alternatives to identify genes associated with P(inf), and different hypotheses, such as gene expression rather than presence/absence, could be tested to explain phenotypic virulence [P(inf)].

The effect of mixed vaccination rollout strategy: A modelling study

Vaccines have measurable efficacy obtained first from vaccine trials. However, vaccine efficacy (VE) is not a static measure and long-term population studies are needed to evaluate its performance and impact. COVID-19 vaccines have been developed in record time and the currently licensed vaccines are extremely effective against severe disease with higher VE after the full immunization schedule. To assess the impact of the initial phase of the COVID-19 vaccination rollout programmes, we used an extended Susceptible - Hospitalized - Asymptomatic/mild - Recovered (SHAR) model. Vaccination models were proposed to evaluate different vaccine types: vaccine type 1 which protects against severe disease only but fails to block disease transmission, and vaccine type 2 which protects against both severe disease and infection. VE was assumed as reported by the vaccine trials incorporating the difference in efficacy between one and two doses of vaccine administration. We described the performance of the vaccine in reducing hospitalizations during a momentary scenario in the Basque Country, Spain. With a population in a mixed vaccination setting, our results have shown that reductions in hospitalized COVID-19 cases were observed five months after the vaccination rollout started, from May to June 2021. Specifically in June, a good agreement between modelling simulation and empirical data was well pronounced.

Online Personalized Preference Learning Method Based on In-Formative Query for Lane Centering Control Trajectory

The personalization of autonomous vehicles or advanced driver assistance systems has been a widely researched topic, with many proposals aiming to achieve human-like or driver-imitating methods. However, these approaches rely on an implicit assumption that all drivers prefer the vehicle to drive like themselves, which may not hold true for all drivers. To address this issue, this study proposes an online personalized preference learning method (OPPLM) that utilizes a pairwise comparison group preference query and the Bayesian approach. The proposed OPPLM adopts a two-layer hierarchical structure model based on utility theory to represent driver preferences on the trajectory. To improve the accuracy of learning, the uncertainty of driver query answers is modeled. In addition, informative query and greedy query selection methods are used to improve learning speed. To determine when the driver's preferred trajectory has been found, a convergence criterion is proposed. To evaluate the effectiveness of the OPPLM, a user study is conducted to learn the driver's preferred trajectory in the curve of the lane centering control (LCC) system. The results show that the OPPLM can converge quickly, requiring only about 11 queries on average. Moreover, it accurately learned the driver's favorite trajectory, and the estimated utility of the driver preference model is highly consistent with the subject evaluation score.

Using a Bayesian model of the joint distribution of pain and time on medication to decide on pain medication for neuropathy

The PAIN-CONTRoLS trial compared four medications in treating Cryptogenic sensory polyneuropathy. The primary outcome was a utility function that combined two outcomes, patients' pain score reduction and patients' quit rate. However, additional analysis of the individual outcomes could also be leveraged to inform selecting an optimal medication for future patients. We demonstrate how joint modeling of longitudinal and time-to-event data from PAIN-CONTRoLS can be used to predict the effects of medication in a patient-specific manner and helps to make patient-focused decisions. A joint model was used to evaluate the two outcomes while accounting for the association between the longitudinal process and the time-to-event processes. Results suggested no significant association between the patients' pain scores and time to the medication quit in the PAIN-CONTRoLS study, but the joint model still provided robust estimates and a better model fit. Using the model estimates, given patients' baseline characteristics, a drug profile on both the pain reduction and medication time could be obtained for each drug, providing information on how likely they would quit and how much pain reduction they should expect. Our analysis suggested that drugs viable for one patient may not be beneficial for others.

An adaptive parameter selection strategy based on maximizing the probability of data for robust fluorescence molecular tomography reconstruction

To alleviate the ill-posed of the inverse problem in fluorescent molecular tomography (FMT), many regularization methods based on L₂ or L₁ norm have been proposed. Whereas, the quality of regularization parameters affects the performance of the reconstruction algorithm. Some classical parameter selection strategies usually need initialization of parameter range and high computing costs, which is not universal in the practical application of FMT. In this paper, an universally applicable adaptive parameter selection method based on maximizing the probability of data (MPD) strategy was proposed. This strategy used maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation to establish a regularization parameters model. The stable optimal regularization parameters can be determined by multiple iterative estimates. Numerical simulations and in vivo experiments show that MPD strategy can obtain stable regularization parameters for both regularization algorithms based on L₂ or L₁ norm and achieve good reconstruction performance.

Applying Monte Carlo Simulations to a Small Data Analysis of a Case of Economic Growth in COVID-19 Times

Studies on the going-on COVID-19 pandemic face small sample issues. In this context, Bayesian estimation is considered a viable alternative to frequentist estimation. Demonstrating the Bayesian approach's advantage in dealing with this problem, our research conducted a case study concerning ASEAN economic growth during the COVID-19 pandemic. By using Monte Carlo standard errors and interval hypothesis testing to check parameter bias within a Bayesian MCMC simulation study, the author obtained significant conclusions as follows: first, in insufficient sample sizes, in contrast to frequentist estimation, the Bayesian framework can offer meaningful results, that is, expansionary monetary and contractionary fiscal policies are positively associated with economic growth; second, in the face of a small sample, by incorporating more information into prior distributions for the model parameters, Bayesian Monte Carlo simulations perform so far better than naïve Bayesian and frequentist estimation; third, in case of a correctly specified prior, the inferences are robust to different prior specifications. The author strongly recommends applying specific informative priors to Bayesian analyses, particularly in small sample investigations.

Personalized Dosing of Infliximab in Patients With Inflammatory Bowel Disease Using a Bayesian Approach: A Next Step in Therapeutic Drug Monitoring

Although biological agents have revolutionized the management of inflammatory bowel diseases (IBDs), a significant proportion of patients show primary non-response or develop secondary loss of response. Therapeutic drug monitoring (TDM) is advocated to maintain the efficacy of biologic agents. Reactive TDM can rationalize the management of primary non-response and secondary loss of response and has shown to be more cost-effective compared with empiric dose escalation. Proactive TDM is shown to increase clinical remission and the durability of the response to a biologic agent. However, the efficacy of proactive and reactive TDM has been questioned in recent studies and meta-analyses. Hence, we need a different approach to TDM, which addresses inflammatory burden, the individual patient, and disease factors. Bayesian approaches, which use population pharmacokinetic models, enable clinicians to make better use of TDM for dose adjustment. With rapid improvement in computer technology, these Bayesian model-based software packages are now available for clinical use. Bayesian dashboard systems allow clinicians to apply model-based dosing to understand an individual's pharmacokinetics and achieve a target serum drug concentration. The model is updated using previously measured drug concentrations and relevant patient factors, such as body weight, C-reactive protein, and serum albumin concentration, to maintain effective drug concentrations in the serum. Initial studies have found utility for the Bayesian approach in induction and maintenance, in adult and pediatric patients, in clinical trials, and in real-life situations for patients with IBD treated with infliximab. This needs confirmation in larger studies. This article reviews the Bayesian approach to therapeutic drug monitoring in IBD.

Practical basket design for binary outcomes with control of family-wise error rate

BACKGROUND

A basket trial is a type of clinical trial in which eligibility is based on the presence of specific molecular characteristics across subpopulations with different cancer types. The existing basket designs with Bayesian hierarchical models often improve the efficiency of evaluating therapeutic effects; however, these models calibrate the type I error rate based on the results of simulation studies under various selected scenarios. The theoretical control of family-wise error rate (FWER) is important for decision-making regarding drug approval.

METHODS

In this study, we propose a new Bayesian two-stage design with one interim analysis for controlling FWER at the target level, along with the formulations of type I and II error rates. Since the difficulty lies in the complexity of the theoretical formulation of the type I error rate, we devised the simulation-based method to approximate the type I error rate.

RESULTS

The proposed design enabled adjustment of the cutoff value to control the FWER at the target value in the final analysis. The simulation studies demonstrated that the proposed design can be used to control the well-approximated FWER below the target value even in situations where the number of enrolled patients differed among subpopulations.

CONCLUSIONS

The accrual number of patients is sometimes unable to reach the pre-defined value; therefore, existing basket designs may not ensure defined operating characteristics before beginning the trial. The proposed design that enables adjustment of the cutoff value to control FWER at the target value based on the results in the final analysis would be a better alternative.

Establishing a standard method for analysing case detection delay in leprosy using a Bayesian modelling approach

BACKGROUND

Leprosy is an infectious disease caused by Mycobacterium leprae and remains a source of preventable disability if left undetected. Case detection delay is an important epidemiological indicator for progress in interrupting transmission and preventing disability in a community. However, no standard method exists to effectively analyse and interpret this type of data. In this study, we aim to evaluate the characteristics of leprosy case detection delay data and select an appropriate model for the variability of detection delays based on the best fitting distribution type.

METHODS

Two sets of leprosy case detection delay data were evaluated: a cohort of 181 patients from the post exposure prophylaxis for leprosy (PEP4LEP) study in high endemic districts of Ethiopia, Mozambique, and Tanzania; and self-reported delays from 87 individuals in 8 low endemic countries collected as part of a systematic literature review. Bayesian models were fit to each dataset to assess which probability distribution (log-normal, gamma or Weibull) best describes variation in observed case detection delays using leave-one-out cross-validation, and to estimate the effects of individual factors.

RESULTS

For both datasets, detection delays were best described with a log-normal distribution combined with covariates age, sex and leprosy subtype [expected log predictive density (ELPD) for the joint model: -1123.9]. Patients with multibacillary (MB) leprosy experienced longer delays compared to paucibacillary (PB) leprosy, with a relative difference of 1.57 [95% Bayesian credible interval (BCI): 1.14-2.15]. Those in the PEP4LEP cohort had 1.51 (95% BCI: 1.08-2.13) times longer case detection delay compared to the self-reported patient delays in the systematic review.

CONCLUSIONS

The log-normal model presented here could be used to compare leprosy case detection delay datasets, including PEP4LEP where the primary outcome measure is reduction in case detection delay. We recommend the application of this modelling approach to test different probability distributions and covariate effects in studies with similar outcomes in the field of leprosy and other skin-NTDs.

NMπ 2.0: Software update to minimize the risk of false positives among determinants of reproductive success

In plant populations, parentage analysis helps understand factors shaping individual reproductive success. However, estimating reproductive success determinants based on parentage counts requires decoupling the effects of individual fecundity and propagule dispersal. The neighbourhood model implemented in the NMπ software provides a standard solution for this problem based on the fixed-effects regression-like approach. Nonetheless, it has been recently shown that the method is prone to false discoveries when important fecundity determinants are omitted. To account for the unexplained variance in fecundity, the Bayesian approach was developed based on the new model (the hierarchical neighbourhood model; HNM). Here, I present the NMπ software update that allows the HNM approach to be used in the framework of a friendly interface. More importantly, the HNM approach is now made available for both dispersed (seedlings) and nondispersed (seeds with known mothers) progeny data. The Bayesian approach, among others, selects significant fecundity determinants, estimates the proportion of variance in reproductive potential explained by selected determinants (R² ), and provides individual female and male fecundity values. Although the software was designed to handle microsatellite marker data, a solution is proposed for large sets of single nucleotide polymorphisms. The program can be run on Windows (using either a terminal or a graphical interface) as well as (using a terminal) on Linux, or macOS platforms. In any case, NMπ can utilize multicore processors to speed up the analysis. The updated package containing the code, the executable file, the user manual, and example data is available at https://www.ukw.edu.pl/pracownicy/plik/igor_chybicki/3694/.

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner-Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.

Automatic detection of active and inactive multiple sclerosis plaques using the Bayesian approach in susceptibility-weighted imaging

BACKGROUND

Susceptibility-weighted imaging (SWI) is efficient in detecting multiple sclerosis (MS) plaques and evaluating the level of disease activity.

PURPOSE

To automatically detect active and inactive MS plaques in SWI images using a Bayesian approach.

MATERIAL AND METHODS

A 1.5-T scanner was used to evaluate 147 patients with MS. The area of the plaques along with their active or inactive status were automatically identified using a Bayesian approach. Plaques were given an orange color if they were active and a blue color if they were inactive, based on the preset signal intensity.

RESULTS

Experimental findings show that the proposed method has a high accuracy rate of 91% and a sensitivity rate of 76% for identifying the type and area of plaques. Inactive plaques were properly identified in 87% of cases, and active plaques in 76% of cases. The Kappa analysis revealed an 80% agreement between expert diagnoses based on contrast-enhanced and FLAIR images and Bayesian inferences in SWI.

CONCLUSION

The results of our study demonstrated that the proposed method has good accuracy for identifying the MS plaque area as well as for identifying the types of active or inactive plaques in SWI. Therefore, it might be helpful to use the proposed method as a supplemental tool to accelerate the specialist's diagnosis.

Evaluating the predictive performance of gut microbiota for the early-stage colorectal cancer

BACKGROUND

Colorectal cancer (CRC) has been regarded as one of the most frequently diagnosed malignancies among the leading causes of cancer-related morbidity and mortality globally. Diagnosis of CRC at the early-stages of tumour might improve the survival rate of patients. The current study sought to determine the performance of fecal Fusobacterium nucleatum (F. nucleatum) and Streptococcus bovis (S. bovis) for timely predicting CRC.

METHODS

Through a case-control study, the fecal sample information of 83 individuals (38 females, 45 males) referring to a hospital in Tehran, Iran was used. All patients underwent a complete colonoscopy, regarded as a gold standard test. Bacterial species including S. bovis and F. nucleatum were measured by absolute quantitative real-time PCR. The Bayesian univariate and bivariate latent class models (LCMs) were applied to estimate the ability of the candidate bacterial markers in order to early detection of patients with CRC.

RESULTS

Bayesian univariate LCMs demonstrated that the sensitivities of S. bovis and F. nucleatum were estimated to be 86% [95% credible interval (CrI) 0.82-0.91] and 82% (95% CrI 0.75-0.88); while specificities were 84% (95% CrI 0.78-0.89) and 80% (95% CrI 0.73-0.87), respectively. Moreover, the area under the receiver operating characteristic curves (AUCs) were 0.88 (95% CrI 0.83-0.94) and 0.80 (95% CrI 0.73-0.85) respectively for S. bovis and F. nucleatum. Based on the Bayesian bivariate LCMs, the sensitivities of S. bovis and F. nucleatum were calculated as 93% (95% CrI 0.84-0.98) and 90% (95% CrI 0.85-0.97), the specificities were 88% (95% CrI 0.78-0.93) and 87% (95% CrI 0.79-0.94); and the AUCs were 0.91 (95% CrI 0.83-0.99) and 0.88(95% CrI 0.81-0.96), respectively.

CONCLUSIONS

Our data has identified that according to the Bayesian bivariate LCM, S. bovis and F. nucleatum had a more significant predictive accuracy compared with the univariate model. In summary, these intestinal bacteria have been highlighted as novel tools for early-stage CRC diagnosis.

A new cure rate regression framework for bivariate data based on the Chen distribution

The present study introduces a new multivariate mixture cure rate model based on the Chen probability distribution to model recurrent event data in the presence of cure fraction. In this context, we provide an alternative for the use of some usual modeling approaches as the semiparametric Cox proportional hazards model commonly used in lifetime data analysis, considering a new bivariate parametric model to be used in the data analysis of bivariate lifetime data assuming a mixture structure for the bivariate data in presence of covariates, censored data and cure fraction. Under a Bayesian setting, the proposed methodology was considered to analyze two real medical datasets from a retrospective cohort study related to leukemia and diabetic retinopathy diseases. The model validation process was addressed by using the Cox-Snell residuals, which allowed us to identify the suitability of the new proposed mixture cure rate model.

Overview of the epidemic history of Hepatitis C uncommon subtypes 2i and 4d in Tunisia and in the world

The impressive improvements in qua therapy efficacy alone are not sufficient to substantially reduce the Hepatitis C Virus burden because of the usually very long asymptomatic phase of the infection. In turn, this renders prevention of infection of great importance. The value of learning how the virus has spread in the past is that this can provide clues as to what routes the virus likely spreads through today, which can feedback into prevention policy. In Tunisia, HCV subtypes 2i and 4d are minor circulating subtypes. Here, we applied a Bayesian Markov Chain Monte Carlo method for visualization of spatial and temporal spread of HCV-2i and 4d in Tunisia and some other countries in the world. Our analysis included sequences retrieved from Genbank and isolated from several countries in the world; 21 HCV-NS5B subtype 2i genome sequences obtained during the period 2002-2020 and 206 HCV-NS5B-4d sequences detected between 2000 and 2019. Phylogenetic analysis revealed that two geographical clusters could be identified in HCV-2i tree with two clearly distinguished clusters in HCV-4d Tree. The estimated time for the most recent common ancestor suggested that current HCV-2i strains emerged in 1963 [1930, 1995] and current HCV-4d strains emerged in 1992 [1988, 1996] in Tunisia and other countries from the world investigated in the present study.

Phylogenetic placement of Turkish populations of Ixodes ricinus and Ixodes inopinatus

Studies on phylogeography and population structure of Ixodes ricinus have been carried out in Europe for decades, but the number of specimens from the Middle East included in these analyses is relatively small, despite the wide distribution of the species in this area. This study aimed to clarify the phylogenetic positions of I. ricinus from Turkey as well as to investigate the presence of Ixodes inopinatus in Anatolia. For this purpose, one mitochondrial (mt 16S rDNA) and one nuclear gene (defensin) were used to generate molecular data from I. ricinus samples, which were collected from 17 locations across the species' distributional range in Turkey. Bayesian inference was used to investigate phylogenetic relationships. Globally, the mt 16S rDNA lineages correspond to the lineages revealed by defensin; I. ricinus and I. inopinatus sequences clustered separately. However, a discordant genetic pattern was observed between the phylogenetic position of turkish I. ricinus revealed by nuclear versus mitochondrial genes. All Turkish haplotypes of mt 16SrDNA clustered with I. ricinus samples from Europe, which might be the result of extensive gene flow between populations of Europe and the Middle East. On the other hand, a sample from Thrace Region grouped within I. inopinatus clade. Thus, the occurrence of I. inopinatus in Turkey was demonstrated for the first time using molecular data. Moreover, four individuals were found to be heterozygous for the defensin. The potential evolutionary processes that underlie this observed discrepancy between the phylogenetic trees of two genes have been discussed.

Estimation of Projection Parameter Distribution and Initial Model Generation in Single-Particle Analysis

This study focused on the problem of projection parameter search in 3D reconstruction using single-particle analysis. We treated the sampling distribution for the parameter search as a prior distribution and designed a probabilistic model for efficient parameter estimation. Using our method, we showed that it is possible to perform 3D reconstruction from synthetic and actual electron microscope images using an initial model, and to generate the initial model itself. We also examined whether the optimization function used in the stochastic gradient descent method can be applied with loose constraints to improve the convergence of initial model generation and confirmed the effect. In order to investigate the advantage of generating a smooth sampling distribution from the stochastic model, we compared the distribution of estimated projection directions with the conventional method of performing a global search using spherical gridding. As a result, our method, which is simple in both mathematical model and implementation, showed no algorithmic artifacts.

Determination of Prestress Losses in Existing Pre-Tensioned Structures Using Bayesian Approach

Deterioration of materials and structures is an unavoidable fact, and prestressed concrete structures are not an exception. The evaluation of load-carrying capacity and remaining service life includes collecting various information. However, one type of information is essential and the most important, the state of prestressing, which inevitably decreases over time. Currently, many possible methods for the evaluation of prestressing are available. These techniques are part of the structural assessment and provide residual prestressing force value which is later used in the evaluation process. Therefore, it is suitable to provide the value of prestressing force based on certain probabilistic backgrounds. This study addresses the determination of residual prestressing force in pre-tensioned railway sleepers one year after their production, using the so-called Bayesian approach. This technique is focused on the validation of results obtained from the application of the non-destructive indirect saw-cut method. The Bayesian approach considers analytic calculation as the primary method of prestressing determination. In this paper, Monte Carlo simulation was used to determine the total variability that defines all Bayesian systems of probability functions. Specifically, a total of 1000 simulations was applied, and the current random vector of prestressing force derived from the analytical calculation has been assumed as a normally distributed function. Finally, obtained results for different depths of saw-cuts are compared. The results of the experimental and statistical determination of residual prestressing force provide its value with a 95% confidence level. This study suggests that the implementation of the probability approach can be an effective tool for determining prestress losses.

A hierarchical Bayesian approach for modeling the evolution of the 7-day moving average of the number of deaths by COVID-19

In this paper, we propose a hierarchical Bayesian approach for modeling the evolution of the 7-day moving average for the number of deaths due to COVID-19 in a country, state or city. The proposed approach is based on a Gaussian process regression model. The main advantage of this model is that it assumes that a nonlinear function f used for modeling the observed data is an unknown random parameter in opposite to usual approaches that set up f as being a known mathematical function. This assumption allows the development of a Bayesian approach with a Gaussian process prior over f. In order to estimate the parameters of interest, we develop an MCMC algorithm based on the Metropolis-within-Gibbs sampling algorithm. We also present a procedure for making predictions. The proposed method is illustrated in a case study, in which, we model the 7-day moving average for the number of deaths recorded in the state of São Paulo, Brazil. Results obtained show that the proposed method is very effective in modeling and predicting the values of the 7-day moving average.

A dynamic simulation model to support reduction in illegal trade within legal wildlife markets

Sustainable wildlife trade is critical for biodiversity conservation, livelihoods, and food security. Regulatory frameworks are needed to secure these diverse benefits of sustainable wildlife trade. However, regulations limiting trade can backfire, sparking illegal trade if demand is not met by legal trade alone. Assessing how regulations affect wildlife market participants' incentives is key to controlling illegal trade. Although much research has assessed how incentives at both the harvester and consumer ends of markets are affected by regulations, little has been done to understand the incentives of traders (i.e., intermediaries). We built a dynamic simulation model to support reduction in illegal wildlife trade within legal markets by focusing on incentives traders face to trade legal or illegal products. We used an Approximate Bayesian Computation approach to infer illegal trading dynamics and parameters that might be unknown (e.g., price of illegal products). We showcased the utility of the approach with a small-scale fishery case study in Chile, where we disentangled within-year dynamics of legal and illegal trading and found that the majority (∼77%) of traded fish is illegal. We utilized the model to assess the effect of policy interventions to improve the fishery's sustainability and explore the trade-offs between ecological, economic, and social goals. Scenario simulations showed that even significant increases (over 200%) in parameters proxying for policy interventions enabled only moderate improvements in ecological and social sustainability of the fishery at substantial economic cost. These results expose how unbalanced trader incentives are toward trading illegal over legal products in this fishery. Our model provides a novel tool for promoting sustainable wildlife trade in data-limited settings, which explicitly considers traders as critical players in wildlife markets. Sustainable wildlife trade requires incentivizing legal over illegal wildlife trade and consideration of the social, ecological, and economic impacts of interventions.

Clinics register based HIV prevalence in Jimma zone, Ethiopia: applications of likelihood and Bayesian approaches

Background

The distribution of HIV is not uniform in Ethiopia with some regions recording higher prevalence than others. However, reported regional HIV prevalence estimates mask the heterogeneity of the epidemic within regions. The main purpose of this study was to assess the district differences in HIV prevalence and other factors that affect the prevalence of HIV infection in Jimma zone, Oromia region of Ethiopia. We aimed to identify districts which had higher or lower than zone average HIV prevalence. Such in-depth analysis of HIV data at district level may help to develop effective strategies to reduce the HIV transmission rate.

Methods

Data collected from 8440 patients who were tested for HIV status in government clinics at the 22 Districts between September 2018 to August 2019 in Jimma zone were used for the analyses. A generalized linear mixed effects model with district random effects was applied to assess the factors associated with HIV infection and the best linear unbiased prediction was used to identify districts that had higher or lower HIV infection. Both likelihood and Bayesian methods were considered.

Results

The statistical test on district random effects variance suggested the need for district random effects in all the models. The results from applying both methods on full data show that the odds of HIV infection are significantly associated with covariates considered in this study. Disaggregation of prevalence by gender also highlighted the persistent features of the HIV epidemic in Jimma zone. After controlling for covariates effects, the results from both techniques revealed that there was heterogeneity in HIV infection prevalence among districts within Jimma zone, where some of them had higher and some had lower HIV infection prevalence compared to the zone average HIV infection prevalence.

Conclusions

The study recommends government to give attention to those districts which had higher HIV infection and to conduct further research to improve their intervention strategies. Further, related to those districts which had lower infection, it would be advantageous to identify reasons for their performance and may apply them to overcome HIV infection among residents in those districts which had higher HIV infection. The approach used in this study can also help to assess the effect of interventions introduced by the authorities to control the epidemic and it can easily be extended to assess the regions HIV infection rate relative to the rate at the national level, or zones HIV infection rate relative to the rate at a region level.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06965-0.

Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches

Nitrate is a major pollutant in groundwater whose main source is municipal wastewater and agricultural activities. In the present study, Bayesian approaches such as Bayesian generalized linear model (BGLM), Bayesian regularized neural network (BRNN), Bayesian additive regression tree (BART), and Bayesian ridge regression (BRR) were used to model groundwater nitrate contamination in a semiarid region Marvdasht watershed, Fars province, Iran. Eleven groundwater (GW) nitrate conditioning factors have been taken as input parameters for predictive modeling. The results showed that the Bayesian models used in this study were all competent to model groundwater nitrate and the BART model with R² = 0.83 was more efficient than the other models. The result of variable importance showed that potassium (K) has the highest importance in the models followed by rainfall, altitude, groundwater depth, and distance from the residential area. The results of the study can support the decision-making process to control and reduce the sources of nitrate pollution.

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.

Does Sex in Managerial Positions Really Matter? Differences in Work-Related Feelings and Behaviors

PURPOSE

The aim of the current study was to test hypotheses regarding differences in work-related feelings (ie, dejection, anxiety, anger, and happiness) and behaviors (aggressive, avoidance-passive, and proactive) between males and females, managers and non-managers, and male and female managers.

METHODS

This survey-based study included a total of 3019 respondents, consisting of 502 managers and 2517 employees working in non-managerial positions. Data were collected using two questionnaires developed by the authors: the scale of work-related affective feelings (WORAF) and the scale of work-related behaviors (WORAB).

RESULTS

The results revealed significant differences between managers and non-managers, with managers being happier in their jobs and exhibiting more proactive behaviors. However, there were no differences in work-related feelings or work-related behaviors between males and females in the total sample of respondents or in the group of employees holding managerial positions.

CONCLUSION

In terms of work-related feelings and behaviors, there are no sex differences among working people. However, some differences between managers and non-managers were observed.

Joint analysis of longitudinal measurements and spatially clustered competing risks HIV/AIDS data

The joint modeling of repeated measurements and time-to-event provides a general framework to describe better the link between the progression of disease through longitudinal measurements and time-to-event outcome. In the survival data, a sample of individuals is frequently grouped into clusters. In some applications, these clusters could be arranged spatially, for example, based on geographical regions. There are two benefits of considering spatial variation in these data, enhancing the efficiency and accuracy of the parameters estimations, and investigating the survivorship spatial pattern. On the other hand, in survival data, there is a situation that subjects are supposed to experience more than one type of event potentially, but the occurrence of one type of event prevents the occurrence of the others. In this article, we considered a Bayesian joint model of longitudinal and competing risks outcomes for spatially clustered HIV/AIDS data. The data were from a registry-based study carried in Hamadan Province, Iran, from December 1997 to June 2020. In this joint model, a linear mixed effects model was used for the longitudinal submodel and a cause-specific hazard model with spatial and spatial-risk random effects was used for the survival submodel. Also, a latent structure was defined by random effects to link both event times and longitudinal processes. We used a univariate intrinsic conditional autoregressive (ICAR) distribution and a multivariate ICAR distribution for modeling the areal spatial and spatial-risk random effects, respectively. The performance of our proposed model using simulation studies and analysis of HIV/AIDS data were assessed.

Prioritization of zero-carbon measures for sustainable urban mobility using integrated double hierarchy decision framework and EDAS approach

Zero-carbon is the current buzzword triggering the minds of every people in the world. The current pandemic situation has given the world an alarm to act towards the reduction/eradication of carbon footprint. Developing countries like India are striving hard to strike a balance between sustainability and global growth. To support the nation, certain measures and their prioritization would be helpful. Motivated by this notion, in this study, a new framework is proposed with double hierarchy fuzzy information, which not only gives experts a better style to articulate preferences linguistically but also makes a rational decision with methodical support. Mayor's transport strategy, 2018 is a popular document that provides valuable information towards sustainable transport practices, and the measures considered in this study are adapted from the same. In this framework, (i) a novel attitudinal evidence-based Bayesian approach is proposed for criteria weight estimation; (ii) experts' weights are determined by using variance approach, and (iii) Evaluation based on distance from average solution (EDAS) approach is extended for prioritizing zero-carbon measures. These approaches are integrated into a framework and its practicality is exemplified by considering a case example of prioritizing measures for a smart city in India. Finally, comparison with extant methods reveals the merits and shortcomings of the proposal.

Ecological and probabilistic human health risk assessment of heavy metal(loid)s in river sediments affected by mining activities in Ecuador

Gold mining is a significant source of metal(loid)s released into the environment. It is an issue of concern due to the potential adverse health effects associated with exposure to toxic elements. This study aimed to assess the ecological and human health risk caused by heavy metal(loid)s exposure in river sediments in Ponce Enríquez, one of the most important mining sites in Ecuador. Concentrations of As, Cd, Cu, Pb, and Zn were evaluated in 172 sediment samples to determine the Potential ecological risk (R_I) and the carcinogenic (CR) and non-carcinogenic risk (HQ). The human exposure to polluted sediments during recreational activities was computed using Bayesian probabilistic models. Residents were randomly surveyed to adjust the risk models to the specific population data. More than 68% of the sampling stations pose a severe As and Cd ecological risk index ([Formula: see text] > 320). Likewise, residents exposed to river sediments showed a non-acceptable carcinogenic risk by incidental ingestion, being As the primary contributor to overall cancer in both children and adults receptors. Moreover, non-carcinogenic risk through the incidental ingestion of sediments was above the safe limit for children. This is the first study conducted in a mining region in Ecuador that reveals the severe levels of ecological and human health risk to which the population is exposed. These results can be applied as a baseline to develop public health strategies to monitor and reduce the health hazards of the residents of mining communities.

A probabilistic approach towards source level inquiries for forensic soil examination based on mineral counts

Forensic soil examination has a well-established foundation in forensic science, this is in part due to the widely varied and complex nature of soil. Within this domain, mineral suite studies are a commonly utilized tool in soil examination. However, statistical or probabilistic approaches towards the interpretation of results from such analysis are lacking and this study aims to fill that gap. Soil samples from four different locations in the city of Lausanne, Switzerland were sampled and their mineral fractions, light and heavy of size between 90 and 180 µm, were studied utilizing microscopical methods. First, the light minerals were identified and counted by employing scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS). Second, the heavy minerals were identified and counted manually under a polarized light microscope (PLM). The resulting count data were subjected to various multivariate statistical treatments such as principal components analysis (PCA), hierarchical clustering analysis (HCA), and linear discriminant analysis (LDA). These methods assist in identifying pertinent variables and subsequently in building various classification models. The validities of these models were then tested and evaluated using blind tests. Finally, these methods demonstrate how a probabilistic approach can be taken in the interpretation of the results to answer source level questions.

Efficacy and safety of nivolumab in Japanese patients with first recurrence of glioblastoma: an open-label, non-comparative study

BACKGROUND

An open-label, non-comparative study assessed the efficacy and safety of nivolumab in Japanese patients with first recurrence glioblastoma.

METHODS

Patients with first recurrence of histologically confirmed World Health Organization Grade IV glioma, after treatment with temozolomide and radiotherapy, received nivolumab 3 mg/kg every 2 weeks until confirmed disease progression (Response Assessment in Neuro-Oncology criteria) or toxicity. Primary endpoint was 1-year overall survival rate assessed by Bayesian approach. The prespecified efficacy criterion was that the Bayesian posterior probability threshold for exceeding the 1-year overall survival of bevacizumab (34.5%) from the Japanese phase 2 study (JO22506) would be 93%.

RESULTS

Of the 50 enrolled patients, 44 (88.0%) had recurrent malignant glioma (glioblastoma, gliosarcoma), and of these, 26 (59.1%) had at least one measurable lesion at baseline. The Bayesian posterior mean 1-year overall survival (90% Bayesian credible intervals) with nivolumab was 54.4% (42.27-66.21), and the Bayesian posterior probability of exceeding the threshold of the 1-year overall survival rate of bevacizumab (34.5%) was 99.7%. Median (90% confidence interval) overall and progression-free survival was 13.1 (10.4-17.7) and 1.5 (1.4-1.5) months, respectively. One partial response was observed (objective response rate 1/26 evaluable patients [3.8%]). Treatment-related adverse event rates were 14.0% for Grade 3-4 and 2.0% for Grade 5; most adverse events resolved and were manageable.

CONCLUSIONS

The 1-year overall survival with nivolumab monotherapy in Japanese patients with glioblastoma met the prespecified efficacy criterion. The safety profile of nivolumab was consistent with that observed in other tumor types.

CLINICAL TRIAL REGISTRATION

JapicCTI-152967.

Concepts and emerging issues of network meta-analysis

Most diseases have more than two interventions or treatment methods, and the application of network meta-analysis (NMA) studies to compare and evaluate the superiority of each intervention or treatment method is increasing. Understanding the concepts and processes of systematic reviews and meta-analyses is essential to understanding NMA. As with systematic reviews and meta-analyses, NMA involves specifying the topic, searching for and selecting all related studies, and extracting data from the selected studies. To evaluate the effects of each treatment, NMA compares and analyzes three or more interventions or treatment methods using both direct and indirect evidence. There is a possibility of several biases when performing NMA. Therefore, key assumptions like similarity, transitivity, and consistency should be satisfied when performing NMA. Among these key assumptions, consistency can be evaluated and quantified by statistical tests. This review aims to introduce the concepts of NMA, analysis methods, and interpretation and presentation of the results of NMA. It also briefly introduces the emerging issues in NMA, including methods for evaluation of consistency.

Uncertainty calculation methodologies in microflow measurements: Comparison of GUM, GUM-S1 and Bayesian approach

The importance of measurement quality cannot be over emphasized in medical applications, as one is dealing with life issues and the wellbeing of society, from oncology to new-borns, and more recently to patients of the COVID-19 pandemic. In all these dire situations, the accuracy of fluid delivered according to a prescribed dose can be critical. Microflow applications are growing in importance for a wide variety of scientific fields, namely drug development and administration, Organ-on-a-Chip, or bioanalysis, but accurate and reliable measurements are a tough challenge in micro-to-femto flow operating ranges, from 2.78 × 10^-4 mL/s down to 2.78 × 10^-7 mL/s (1000 μL/h down to 1 μL/h). Several sources of error have been established such as the mass measurement, the fluid evaporation dependent on the gravimetric methodology implemented, the tube adsorption and the repeatability, believed to be closely related to the operating mode of the stepper motor and drive screw pitch of a syringe pump. In addition, the difficulty in dealing with microflow applications extends to the evaluation of measurement uncertainty which will qualify the quality of measurement. This is due to the conditions entailed when measuring very small values, close to zero, of a quantity such as the flow rate which is inherently positive. Alternative methods able to handle these features were developed and implemented, and their suitability will be discussed.

A children's epilepsy diagnosis aid: Development and early validation using a Bayesian approach

INTRODUCTION

The diagnosis of epilepsy in children is difficult and misdiagnosis rates can be as much as 36%. Diagnosis in all countries is essentially clinical, based on asking a series of questions and interpreting the answers. Doctors experienced enough to do this are either scarce or absent in very many parts of the world so there is a need to develop a diagnostic aid to help less-experienced doctors or non-physician health workers (NPHWs) do this. We used a Bayesian approach to determine the most useful questions to ask based on their likelihood ratios (LR), and incorporated these into a Children's Epilepsy Diagnosis Aid (CEDA).

METHODS

Ninety-six consecutive new referrals with possible epilepsy aged under 10 years attending a pediatric neurology clinic in Khartoum were included. Initially, their caregivers were asked 65 yes/no questions by a medical officer, then seen by pediatric neurologist and the diagnosis of epilepsy (E), not epilepsy (N), or uncertain (U) was made. The LR was calculated and then we selected the variables with the highest and lowest LRs which are the most informative at differentiating epilepsy from non-epilepsy. An algorithm, (CEDA), based on the most informative questions was constructed and tested on a new sample of 47 consecutive patients with a first attendance of possible epilepsy. We calculated the sensitivity and specificity for CEDA in the diagnosis of epilepsy.

RESULTS

Sixty-nine (79%) had epilepsy and 18 (21%) non-epilepsy giving pre-test odds of having epilepsy of 3.83. Eleven variables with the most informative LRs formed the diagnostic aid (CEDA). The pre-test odds and algorithm were used to determine the probability of epilepsy diagnosis in a subsequent sample of 47 patients. There were 36 patients with epilepsy and 11 with nonepileptic conditions. The sensitivity of CEDA was 100% with specificity of 97% and misdiagnosis 8.3%.

CONCLUSION

Children's Epilepsy Diagnosis Aid has the potential to improve pediatric epilepsy diagnosis and therefore management and is particularly likely to be useful in the many situations where access to epilepsy specialists is limited. The algorithm can be presented as a smartphone application or used as a spreadsheet on a computer.

Systematic Review Estimating the Burden of Dementia in the Latin America and Caribbean Region: A Bayesian Approach

Background: The global burden of dementia has increasingly shifted to low- and middle-income regions that lack essential data for monitoring epidemiological progression, and policy and planning support. Drawing upon data that have emerged since the last known estimates published in 2015, this study aims to update dementia estimates in the Latin America and Caribbean (LAC) region for the years 2020, 2030, and 2050 through the application of a recently validated Bayesian approach for disease estimates useful when data sources are scarce.

Methods: A comprehensive parallel systematic review of PubMed, EMBASE, PsycINFO, Global Health, and LILACS was conducted to identify prospective population-based epidemiological studies on dementia published in English from 2013 to 2018 in LAC. English and non-English data cited by a recent review on dementia estimates in LAC were also examined for additional data. A Bayesian normal-normal hierarchical model (NNHM) was developed to estimate age-specific and age-adjusted dementia prevalence in people aged 60+. Using age-specific population projections from the UN, the total number of people affected by dementia for the years 2020, 2030, and 2050 were estimated.

Results: 1,414 studies were identified, of which only 7 met the inclusion criteria. The studies had 7,684 participants and 1,191 dementia cases. The age-standardized prevalence of all forms of dementia in LAC was 8% (95% CI: 5–11.5%) in people aged 60+. The estimated prevalence varied with age, increasing from 2.5% (95% CI: 0.08–4.0%) in the 60-69 age group, to 9.4% (95% CI: 5.4–13.2%) in the 70–79 age group and 28.9% (95% CI: 20.3–37.2%) in the ≥80 age group. The number of people age 60 and older living with dementia in LAC in 2020 was estimated at 6.86 (95% CI: 4.3–9.8) million, 9.94 (95% CI: 6.16–14.15) million in 2030, and 19.33 (95% CI: 12.3–13.6) million in 2050.

Conclusion: We project an upward disease trajectory for dementia in LAC countries. The projection is likely an underestimation of the true dementia burden given the underrepresentation of rural and socio-economically deprived populations. More research is urgently needed to improve the accuracy of disease estimates, guide clinicians to improve evaluations for earlier recognition of dementia, and support the development of effective policies for improving dementia prevention, diagnosis and clinical management in LAC's diverse and aging communities.

Detecting SARS-CoV-2 RNA prone clusters in a municipal wastewater network using fuzzy-Bayesian optimization model to facilitate wastewater-based epidemiology

The current pandemic disease coronavirus (COVID-19) has not only become a worldwide health emergency, but also devoured the global economy. Despite appreciable research, identification of targeted populations for testing and tracking the spread of COVID-19 at a larger scale is an intimidating challenge. There is a need to quickly identify the infected individual or community to check the spread. The diagnostic testing done at large-scale for individuals has limitations as it cannot provide information at a swift pace in large populations, which is pivotal to contain the spread at the early stage of its breakouts. Recently, scientists are exploring the presence of SARS-CoV-2 RNA in the faeces discharged in municipal wastewater. Wastewater sampling could be a potential tool to expedite the early identification of infected communities by detecting the biomarkers from the virus. However, it needs a targeted approach to choose optimized locations for wastewater sampling. The present study proposes a novel fuzzy based Bayesian model to identify targeted populations and optimized locations with a maximum probability of detecting SARS-CoV-2 RNA in wastewater networks. Consequently, real time monitoring of SARS-CoV-2 RNA in wastewater using autosamplers or biosensors could be deployed efficiently. Fourteen criteria such as population density, patients with comorbidity, quarantine and hospital facilities, etc. are analysed using the data of 14 lac individuals infected by COVID-19 in the USA. The uniqueness of the proposed model is its ability to deal with the uncertainty associated with the data and decision maker's opinions using fuzzy logic, which is fused with Bayesian approach. The evidence-based virus detection in wastewater not only facilitates focused testing, but also provides potential communities for vaccine distribution. Consequently, governments can reduce lockdown periods, thereby relieving human stress and boosting economic growth.

A Bayesian Approach to Diagnose Growth Hormone Deficiency in Children: Insulin-Like Growth Factor Type 1 Is Valuable for Screening and IGF-Binding Protein Type 3 for Confirmation

BACKGROUND

The utility of insulin-like growth factor type 1 (IGF-1) is well established in the diagnosis of growth hormone deficiency (GHD), whereas IGF-binding protein type 3 (IGFBP-3) has a more controversial role. Most studies evaluated the value of these peptides by assessing their sensitivity and specificity but not considering the low prevalence of GHD among short children (<2%).

OBJECTIVE

To evaluate the utility of basal IGF-1 and IGFBP-3 values in the GHD diagnosis process with a Bayesian approach, based on pre- and post-test probability.

METHODS

We determined ROC curves, sensitivity, specificity, and positive and negative predictive values for IGF-1 and IGFBP-3 obtained from patients with GHD (n = 48) and GH-sufficient children (n = 175). The data were also analyzed by classifying the children into early childhood and late childhood (girls and boys younger and older than 8 and 9 years, respectively).

RESULTS

The area under the curve (AUC) of the receiver operating characteristic curve of IGF-1-SDS (standard deviation score) was greater than that of IGFBP-3-SDS (AUC 0.886 and 0.786, respectively, p = 0.001). In early childhood, the AUC of IGFBP-3-SDS was significantly improved (0.866) and similar to IGF-1-SDS (0.898). IGF-1-SDS, in comparison to IGFBP-3-SDS, had a greater sensitivity (92 vs. 45.8%, respectively), lower specificity (69 vs. 93.8%, respectively), and lower positive predictive value (5.7 vs. 13.1%, respectively), with similar negative predictive values.

CONCLUSION

IGF-1-SDS is a useful screening tool in the diagnosis of GHD. Although IGFBP-3-SDS lacks sensitivity, its high specificity supports the role to confirm GHD in short children, especially in early childhood. This strategy could simplify and reduce the necessity of a second laborious and expensive GH stimulation test to confirm the diagnosis of GHD.