Adapting vector surveillance using Bayesian experimental design: An application to an ongoing tick monitoring program in the southeastern United States

Maps of the distribution of medically-important ticks throughout the US remain lacking in spatial and temporal resolution in many areas, leading to holes in our understanding of where and when people are at risk of tick encounters, an important baseline for informing public health response. In this work, we demonstrate the use of Bayesian Experimental Design (BED) in planning spatiotemporal surveillance of disease vectors. We frame survey planning as an optimization problem with the objective of identifying a calendar of sampling locations that maximizes the expected information regarding some goal. Here we consider the goals of understanding associations between environmental factors and tick presence and minimizing uncertainty in high risk areas. We illustrate our proposed BED workflow using an ongoing tick surveillance study in South Carolina parks. Following a model comparison study based on two years of initial data, several techniques for finding optimal surveys were compared to random sampling. Two optimization algorithms found surveys better than all replications of random sampling, while a space-filling heuristic performed favorably as well. Further, optimal surveys of just 20 visits were more effective than repeating the schedule of 111 visits used in 2021. We conclude that BED shows promise as a flexible and rigorous means of survey design for vector control, and could help alleviate pressure on local agencies by limiting the resources necessary for accurate information on arthropod distributions. We have made the code for our BED workflow publicly available on Zenodo to help promote the application of these methods to future surveillance efforts.

Robust time selection for interim analysis in the Bayesian phase 2 exploratory clinical trial

In phase 2 clinical trials, we expect to make a right Go or No-Go decision during the interim analysis (IA) and make this decision at the right time. The optimal time for IA is usually determined based on a utility function. In most previous research, utility functions aim to minimize the expected sample size or total cost in confirmatory trials. However, the selected time can vary depending on different alternative hypotheses. This paper proposes a new utility function for Bayesian phase 2 exploratory clinical trials. It evaluates the predictability and robustness of the Go and No-Go decision made during the IA. We can make a robust time selection for the IA based on the function regardless of the treatment effect assumptions.

The "Why" behind including "Y" in your imputation model

Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. Here, we investigate the scenario where a covariate used in an analysis has missingness and will be imputed. There are recommendations to include the outcome from the analysis model in the imputation model for missing covariates, but it is not necessarily clear if this recommendation always holds and why this is sometimes true. We examine deterministic imputation (i.e. single imputation with fixed values) and stochastic imputation (i.e. single or multiple imputation with random values) methods and their implications for estimating the relationship between the imputed covariate and the outcome. We mathematically demonstrate that including the outcome variable in imputation models is not just a recommendation but a requirement to achieve unbiased results when using stochastic imputation methods. Moreover, we dispel common misconceptions about deterministic imputation models and demonstrate why the outcome should not be included in these models. This article aims to bridge the gap between imputation in theory and in practice, providing mathematical derivations to explain common statistical recommendations. We offer a better understanding of the considerations involved in imputing missing covariates and emphasize when it is necessary to include the outcome variable in the imputation model.

Predictive Ppk calculations for biologics and vaccines using a Bayesian approach - a tutorial

In pharmaceutical manufacturing, especially biologics and vaccines manufacturing, emphasis on speedy process development can lead to inadequate process development, which often results in less robust commercial manufacturing process after launch. Process performance index (Ppk) is a statistical measurement of the ability of a process to produce output within specification limits over a period of time. In biopharmaceutical manufacturing, progression in process development is based on Critical Quality Attributes meeting their specification limits, lacking insight into the process robustness. Ppk is typically estimated after 15-30 commercial batches at which point it may be too late/too complex to make process adjustments to enhance robustness. The use of Bayesian statistics, prior knowledge, and input from Subject matter experts (SMEs) offers an opportunity to make predictions on process capability during the development cycle. Developing a standard methodology to assess long term process capability at various stages of development provides several benefits: provides opportunity for early insight into process vulnerabilities thereby enabling resolution pre-licensure; identifies area of the process to prioritize and focus on during process development/process characterization (PC) using a data-driven approach; and ultimately results in higher process robustness/process knowledge at launch. We propose a Bayesian-based method to predict the performance of a manufacturing process at full manufacturing scale during the development and commercialization phase, before commercial data exists. Under Bayesian framework, limited development data for the process of interest at hand, data from similar products, general SME knowledge, and literature can be carefully formulated into informative priors. The implementation of the proposed approach is presented through two examples. To allow for continuous improvement during process development, we recommend to embed this approach of using predictive Ppk at pre-defined commercialization stage-gates, for example, at completion of process development, prior to and completion of PC, prior to technology transfer runs (Engineering/Process Performance Qualification, PPQ), and prior to commercial specification setting.

Improved RNA stability estimation through Bayesian modeling reveals most Salmonella transcripts have subminute half-lives

RNA decay is a crucial mechanism for regulating gene expression in response to environmental stresses. In bacteria, RNA-binding proteins (RBPs) are known to be involved in posttranscriptional regulation, but their global impact on RNA half-lives has not been extensively studied. To shed light on the role of the major RBPs ProQ and CspC/E in maintaining RNA stability, we performed RNA sequencing of Salmonella enterica over a time course following treatment with the transcription initiation inhibitor rifampicin (RIF-seq) in the presence and absence of these RBPs. We developed a hierarchical Bayesian model that corrects for confounding factors in rifampicin RNA stability assays and enables us to identify differentially decaying transcripts transcriptome-wide. Our analysis revealed that the median RNA half-life in Salmonella in early stationary phase is less than 1 min, a third of previous estimates. We found that over half of the 500 most long-lived transcripts are bound by at least one major RBP, suggesting a general role for RBPs in shaping the transcriptome. Integrating differential stability estimates with cross-linking and immunoprecipitation followed by RNA sequencing (CLIP-seq) revealed that approximately 30% of transcripts with ProQ binding sites and more than 40% with CspC/E binding sites in coding or 3' untranslated regions decay differentially in the absence of the respective RBP. Analysis of differentially destabilized transcripts identified a role for ProQ in the oxidative stress response. Our findings provide insights into posttranscriptional regulation by ProQ and CspC/E, and the importance of RBPs in regulating gene expression.

Estimating Asymptomatic and Symptomatic Transmission of the COVID-19 First Few Cases in Selenge Province, Mongolia

BACKGROUND

Following the first locally transmitted case in Sukhbaatar soum, Selenge Province, we aimed to investigate the ultimate scale of the epidemic in the scenario of uninterrupted transmission.

METHODS

This was a prospective case study following the locally modified WHO FFX cases generic protocol. A rapid response team collected data from November 14 to 29, 2020. We created a stochastic process to draw many transmission chains from this greater distribution to better understand and make inferences regarding the outbreak under investigation.

RESULTS

The majority of the cases involved household transmissions (35, 52.2%), work transmissions (20, 29.9%), index (5, 7.5%), same apartment transmissions (2, 3.0%), school transmissions (2, 3.0%), and random contacts between individuals transmissions (1, 1.5%). The posterior means of the basic reproduction number of both the asymptomatic casesR 0 Asy $$ {R}_0^{Asy} $$ and the presymptomatic casesR 0 Pre $$ {R}_0^{Pre} $$ (1.35 [95% CrI 0.88-1.86] and 1.29 [95% CrI 0.67-2.10], respectively) were lower than that of the symptomatic cases (2.00 [95% Crl 1.38-2.76]).

CONCLUSION

Our study highlights the heterogeneity of COVID-19 transmission across different symptom statuses and underscores the importance of early identification and isolation of symptomatic cases in disease control. Our approach, which combines detailed contact tracing data with advanced statistical methods, can be applied to other infectious diseases, facilitating a more nuanced understanding of disease transmission dynamics.

Linking fast and slow: The case for generative models

A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days, or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multiscale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.

Flexible Bayesian inference on partially observed epidemics

Individual-based models of contagious processes are useful for predicting epidemic trajectories and informing intervention strategies. In such models, the incorporation of contact network information can capture the non-randomness and heterogeneity of realistic contact dynamics. In this article, we consider Bayesian inference on the spreading parameters of an SIR contagion on a known, static network, where information regarding individual disease status is known only from a series of tests (positive or negative disease status). When the contagion model is complex or information such as infection and removal times is missing, the posterior distribution can be difficult to sample from. Previous work has considered the use of Approximate Bayesian Computation (ABC), which allows for simulation-based Bayesian inference on complex models. However, ABC methods usually require the user to select reasonable summary statistics. Here, we consider an inference scheme based on the Mixture Density Network compressed ABC, which minimizes the expected posterior entropy in order to learn informative summary statistics. This allows us to conduct Bayesian inference on the parameters of a partially observed contagious process while also circumventing the need for manual summary statistic selection. This methodology can be extended to incorporate additional simulation complexities, including behavioural change after positive tests or false test results.

New approach for early and specific Alzheimer disease diagnosis from different plasma biomarkers

BACKGROUND

Alzheimer Disease (AD) is a complex pathology, in which several biochemical pathways could be involved. Therefore, the development of clinical studies combining different nature biomarkers in an AD diagnosis approach is required. Specifically, the present study evaluated blood biomarkers from different molecular pathways (epigenomics, lipid metabolism, lipid peroxidation), to obtain an early and specific AD diagnosis approach.

METHODS

The participants were classified into early AD (n = 53), and non-AD (healthy controls, other dementias) (n = 83). Blood samples were collected and biochemical determinations (microRNAs, lipids, lipid peroxidation compounds) were carried out by quantitative PCR and liquid chromatography coupled to mass spectrometry, respectively. Then, a logistic regression model with a Bayesian variable selection procedure was developed.

RESULTS

The Bayesian variable selection procedure for microRNAs did not show any relevant variable. Therefore, microRNA biomarkers were excluded. So, the developed model considered only lipids and lipid peroxidation compounds. The corresponding selected variables were age, 18:0 LPC, PGE2, isoprostanes and, isofurans. The validated model (by leave-one-out cross-validation) provided satisfactory diagnosis indexes (AUC 0.83, Sensitivity 87 %, Specificity 79 %).

CONCLUSION

The developed model included biomarkers from different pathways (lipid metabolism, oxidative stress), achieving a promising approach to early, specific and, minimally invasive AD diagnosis. Nevertheless, further work to validate clinically these preliminary results with an external cohort is required. Also, the integration of different compounds coming from several biochemical pathways could constitute a relevant research field for the development of AD therapeutic targets.

Autistic and nonautistic adolescents do not differ in adaptation to gaze direction

Predictive processing accounts of autism posit that autistic individuals' perception is less biased by expectations than nonautistic individuals', perhaps through stronger precision-weighting of prediction errors. Since precision-weighting is fundamental to all information processing, under this theory, the differences between autistic and nonautistic individuals should be domain-general and observable in both behavior and brain responses. This study used EEG, behavioral responses, and eye-tracking co-registration during gaze-direction adaptation, to investigate whether increased precision-weighting of prediction errors is evident through smaller adaptation after-effects in autistic adolescents compared with nonautistic peers. Multilevel modeling showed that autistic and nonautistic adolescents' responses were consistent with behavioral adaptation, with Bayesian statistics providing extremely strong evidence for the absence of a group difference. Cluster-based permutation testing of ERP responses did not show the expected adaptation after-effect but did show habituation to repeated stimulus presentation, and no group difference was detected, a result not consistent with the theoretical account. Combined with the few other available studies, the current findings raise challenges for the theory, suggesting no fundamental difference in precision-weighting of prediction errors in autism.

Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia

Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics conducted social cartography workshops, where community representatives estimated numbers of dwellings and people throughout their regions. We repurposed this information, combining it with remotely sensed buildings data and other geospatial data. To estimate building counts and population sizes, we developed hierarchical Bayesian models, trained using nearby full-coverage census enumerations and assessed using 10-fold cross-validation. We compared models to assess the relative contributions of community knowledge, remotely sensed buildings, and their combination to model fit. The Community model was unbiased but imprecise; the Satellite model was more precise but biased; and the Combination model was best for overall accuracy. Results reaffirmed the power of remotely sensed buildings data for population estimation and highlighted the value of incorporating local knowledge.

Modelling animal social networks: New solutions and future directions

Research Highlight: Ross, C. T., McElreath, R., & Redhead, D. (2023). Modelling animal network data in R using STRAND. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.14021. One of the most important insights in ecology over the past decade has been that the social connections among animals affect a wide range of ecological and evolutionary processes. However, despite over 20 years of study effort on this topic, generating knowledge from data on social associations and interactions remains fraught with problems. Redhead et al. present an R package-STRAND-that extends the current animal social network analysis toolbox in two ways. First, they provide a simple R interfaces to implement generative network models, which are an alternative to regression approaches that draw inference by simulating the data-generating process. Second, they implement these models in a Bayesian framework, allowing uncertainty in the observation process to be carried through to hypothesis testing. STRAND therefore fills an important gap for hypothesis testing using network data. However, major challenges remain, and while STRAND represents an important advance, generating robust results continues to require careful study design, considerations in terms of statistical methods and a plurality of approaches.

Dabrafenib and trametinib administration in patients with BRAF V600E/R or non-V600 BRAF mutated advanced solid tumours (BELIEVE, NCCH1901): a multicentre, open-label, and single-arm phase II trial

Background

BRAF V600 mutations are common in melanoma, thyroid, and non-small-cell lung cancers. Despite dabrafenib and trametinib being standard treatments for certain cancers, their efficacy across various solid tumours remains unelucidated. The BELIEVE trial assessed the efficacy of dabrafenib and trametinib in solid tumours with BRAF V600E/R or non-V600 BRAF mutations.

Methods

Between October 1, 2019, and June 2022, at least 50 patients with measurable and seven without measurable diseases examined were enrolled in a subcohort of the BELIEVE trial (NCCH1901, jRCTs031190104). BRAF mutated solid tumour cases other than BRAF V600E mutated colorectal cancer, melanoma, and non-small cell lung cancer cases were included. Patients with solid tumours received dabrafenib (150 mg) twice daily and trametinib (2 mg) once daily until disease progression or intolerable toxicity was observed. The primary endpoint was overall response rate (ORR), and secondary endpoints included progression-free survival (PFS), 6-month PFS, and overall survival (OS). Bayesian analysis was performed using a prior distribution with a 30% expected response rate [Beta (0.6, 1.4)].

Findings

Fourty-seven patients with measurable disease, mainly with the BRAF V600E mutation (94%), and three others with non-V600E BRAF mutations (V600R, G466A, and N486_P490del) were enrolled. The primary sites included the thyroid gland, central nervous system, liver, bile ducts, colorectum, and pancreas. The confirmed ORR was 28.0%; the expected value of posterior distribution [Beta (14.6, 37.4)] was 28.1%, although the primary endpoint was achieved, not exceeding an unexpectedly high response rate of 60% obtained using Bayesian analysis. The disease control rate (DCR) was 84.0%. The median PFS was 6.5 months (95% confidence interval [CI]; 4.2-7.2 months, 87.8% at 6 months). Responses were observed across seven tumour types. Median OS was 9.7 months (95% CI, 7.5-12.2 months). Additional patients without measurable diseases had a median PFS of 4.5 months. Adverse events (AEs) were consistent with previous reports, with 45.6% of patients experiencing grade ≥3 AEs.

Interpretation

This study reported promising efficacy against BRAF V600-mutant tumours. Dabrafenib and trametinib would offer a new therapeutic option for rare cancers, such as high-grade gliomas, biliary tract cancer, and thyroid cancer.

Funding

This study was funded by the Japan Agency for Medical Research and Development (22ck0106622h0003) and a Health and Labour Sciences Research Grant (19EA1008).

Understanding the impact of numerical solvers on inference for differential equation models

Most ordinary differential equation (ODE) models used to describe biological or physical systems must be solved approximately using numerical methods. Perniciously, even those solvers that seem sufficiently accurate for the forward problem, i.e. for obtaining an accurate simulation, might not be sufficiently accurate for the inverse problem, i.e. for inferring the model parameters from data. We show that for both fixed step and adaptive step ODE solvers, solving the forward problem with insufficient accuracy can distort likelihood surfaces, which might become jagged, causing inference algorithms to get stuck in local 'phantom' optima. We demonstrate that biases in inference arising from numerical approximation of ODEs are potentially most severe in systems involving low noise and rapid nonlinear dynamics. We reanalyse an ODE change point model previously fit to the COVID-19 outbreak in Germany and show the effect of the step size on simulation and inference results. We then fit a more complicated rainfall run-off model to hydrological data and illustrate the importance of tuning solver tolerances to avoid distorted likelihood surfaces. Our results indicate that, when performing inference for ODE model parameters, adaptive step size solver tolerances must be set cautiously and likelihood surfaces should be inspected for characteristic signs of numerical issues.

Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamics

Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and skin conductance. Heart rate dynamics are of particular interest as they provide a way to track the sympathetic and parasympathetic outflow from the autonomic nervous system, which is known to play a key role in modulating attention, memory, decision-making, and emotional processing. However, extracting useful information from heartbeats about the autonomic outflow is still challenging due to the noisy estimates that result from standard signal-processing methods. To advance this state of affairs, we propose a novel approach in how to conceptualise and model heart rate: instead of being a mere summary of the observed inter-beat intervals, we introduce a modelling framework that views heart rate as a hidden stochastic process that drives the observed heartbeats. Moreover, by leveraging the rich literature of state-space modelling and Bayesian inference, our proposed framework delivers a description of heart rate dynamics that is not a point estimate but a posterior distribution of a generative model. We illustrate the capabilities of our method by showing that it recapitulates linear properties of conventional heart rate estimators, while exhibiting a better discriminative power for metrics of dynamical complexity compared across different physiological states.

Statistical analysis of parameters and adsorption isotherm models

The present work intends to discuss parameter estimation and statistical analysis in adsorption. The Langmuir and Tóth isotherm models are compared for a set of carbon dioxide adsorption data on 13X zeolite from literature at different temperatures: 303, 323, 373, and 423 K. Statistical analyses were performed under frequentist and Bayesian perspectives. Under the frequentist statistical view, parameters were estimated using Maximum Likelihood estimation (MLE). Statistical analyses of parameters were performed by confidence regions in terms of elliptical approximation and likelihood region, while the evaluation of models was performed by chi-square statistics. The results showed that, for these nonlinear models, the elliptical region offers a poor approximation of the parameter estimates' confidence region, especially for the most correlated parameter pairs. Additionally, the four-parameter Tóth's equation yields less correlated parameters than the three-parameter Langmuir model. From a Bayesian perspective, the Markov chain Monte Carlo (MCMC) technique facilitated the reconstruction of the probability density functions of parameters as well as enabled the propagation of parametric uncertainties in the model responses. Finally, the accurate assessment of experimental uncertainty significantly influences the evaluation of models and their respective parameters.

GBD Local and Small Area Estimation Family Planning Collaborators. Mapping heterogeneity in family planning indicators in Burkina Faso, Kenya, and Nigeria, 2000-2020

BACKGROUND

Family planning is fundamental to women's reproductive health and is a basic human right. Global targets such as Sustainable Development Goal 3 (specifically, Target 3.7) have been established to promote universal access to sexual and reproductive healthcare services. Country-level estimates of contraceptive use and other family planning indicators are already available and are used for tracking progress towards these goals. However, there is likely heterogeneity in these indicators within countries, and more local estimates can provide crucial additional information about progress towards these goals in specific populations. In this analysis, we develop estimates of six family indicators at a local scale, and use these estimates to describe heterogeneity and spatial-temporal patterns in these indicators in Burkina Faso, Kenya, and Nigeria.

METHODS

We used a Bayesian geostatistical modelling framework to analyse geo-located data on contraceptive use and family planning from 61 household surveys in Burkina Faso, Kenya, and Nigeria in order to generate subnational estimates of prevalence and associated uncertainty for six indicators from 2000 to 2020: contraceptive prevalence rate (CPR), modern contraceptive prevalence rate (mCPR), traditional contraceptive prevalence rate (tCPR), unmet need for modern methods of contraception, met need for family planning with modern methods, and intention to use contraception. For each country and indicator, we generated estimates at an approximately 5 × 5-km resolution and at the first and second administrative levels (regions and provinces in Burkina Faso; counties and sub-counties in Kenya; and states and local government areas in Nigeria).

RESULTS

We found substantial variation among locations in Burkina Faso, Kenya, and Nigeria for each of the family planning indicators estimated. For example, estimated CPR in 2020 ranged from 13.2% (95% Uncertainty Interval, 8.0-20.0%) in Oudalan to 38.9% (30.1-48.6%) in Kadiogo among provinces in Burkina Faso; from 0.4% (0.0-1.9%) in Banissa to 76.3% (58.1-89.6%) in Makueni among sub-counties in Kenya; and from 0.9% (0.3-2.0%) in Yunusari to 31.8% (19.9-46.9%) in Somolu among local government areas in Nigeria. There were also considerable differences among locations in each country in the magnitude of change over time for any given indicator; however, in most cases, there was more consistency in the direction of that change: for example, CPR, mCPR, and met need for family planning with modern methods increased nationally in all three countries between 2000 and 2020, and similarly increased in all provinces of Burkina Faso, and in large majorities of sub-counties in Kenya and local government areas in Nigeria.

CONCLUSIONS

Despite substantial increases in contraceptive use, too many women still have an unmet need for modern methods of contraception. Moreover, country-level estimates of family planning indicators obscure important differences among locations within the same country. The modelling approach described here enables estimating family planning indicators at a subnational level and could be readily adapted to estimate subnational trends in family planning indicators in other countries. These estimates provide a tool for better understanding local needs and informing continued efforts to ensure universal access to sexual and reproductive healthcare services.

A Bayesian functional approach to test models of life course epidemiology over continuous time

BACKGROUND

Life course epidemiology examines associations between repeated measures of risk and health outcomes across different phases of life. Empirical research, however, is often based on discrete-time models that assume that sporadic measurement occasions fully capture underlying long-term continuous processes of risk.

METHODS

We propose (i) the functional relevant life course model (fRLM), which treats repeated, discrete measures of risk as unobserved continuous processes, and (ii) a testing procedure to assign probabilities that the data correspond to conceptual models of life course epidemiology (critical period, sensitive period and accumulation models). The performance of the fRLM is evaluated with simulations, and the approach is illustrated with empirical applications relating body mass index (BMI) to mRNA-seq signatures of chronic kidney disease, inflammation and breast cancer.

RESULTS

Simulations reveal that fRLM identifies the correct life course model with three to five repeated assessments of risk and 400 subjects. The empirical examples reveal that chronic kidney disease reflects a critical period process and inflammation and breast cancer likely reflect sensitive period mechanisms.

CONCLUSIONS

The proposed fRLM treats repeated measures of risk as continuous processes and, under realistic data scenarios, the method provides accurate probabilities that the data correspond to commonly studied models of life course epidemiology. fRLM is implemented with publicly-available software.

Multilevel Bayesian analysis of monk parakeet contact calls shows dialects between European cities

Geographic differences in vocalizations provide strong evidence for animal culture, with patterns likely arising from generations of social learning and transmission. Most studies on the evolution of avian vocal variation have predominantly focused on fixed repertoire, territorial song in passerine birds. The study of vocal communication in open-ended learners and in contexts where vocalizations serve other functions is therefore necessary for a more comprehensive understanding of vocal dialect evolution. Parrots are open-ended vocal production learners that use vocalizations for social contact and coordination. Geographic variation in parrot vocalizations typically take the form of either distinct regional variations known as dialects or graded variation based on geographic distance known as clinal variation. In this study, we recorded monk parakeets (Myiopsitta monachus) across multiple spatial scales (i.e., parks and cities) in their European invasive range. We then compared calls using a multilevel Bayesian model and sensitivity analysis, with this novel approach allowing us to explicitly compare vocalizations at multiple spatial scales. We found support for founder effects and/or cultural drift at the city level, consistent with passive cultural processes leading to large-scale dialect differences. We did not find a strong signal for dialect or clinal differences between parks within cities, suggesting that birds did not actively converge on a group level signal, as expected under the group membership hypothesis. We demonstrate the robustness of our findings and offer an explanation that unifies the results of prior monk parakeet vocalization studies.

Go Multivariate: Recommendations on Bayesian Multilevel Hidden Markov Models with Categorical Data

The multilevel hidden Markov model (MHMM) is a promising method to investigate intense longitudinal data obtained within the social and behavioral sciences. The MHMM quantifies information on the latent dynamics of behavior over time. In addition, heterogeneity between individuals is accommodated with the inclusion of individual-specific random effects, facilitating the study of individual differences in dynamics. However, the performance of the MHMM has not been sufficiently explored. We performed an extensive simulation to assess the effect of the number of dependent variables (1-8), number of individuals (5-90), and number of observations per individual (100-1600) on the estimation performance of a Bayesian MHMM with categorical data including various levels of state distinctiveness and separation. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Moreover, including variables only consisting of random noise was generally not detrimental to model performance. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. However, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the level of state distinctiveness and separation and study objectives of the researcher.

The Bayesian simulation study (BASIS) framework for simulation studies in statistical and methodological research

Statistical simulation studies are becoming increasingly popular to demonstrate the performance or superiority of new computational procedures and algorithms. Despite this status quo, previous surveys of the literature have shown that the reporting of statistical simulation studies often lacks relevant information and structure. The latter applies in particular to Bayesian simulation studies, and in this paper the Bayesian simulation study framework (BASIS) is presented as a step towards improving the situation. The BASIS framework provides a structured skeleton for planning, coding, executing, analyzing, and reporting Bayesian simulation studies in biometrical research and computational statistics. It encompasses various features of previous proposals and recommendations in the methodological literature and aims to promote neutral comparison studies in statistical research. Computational aspects covered in the BASIS include algorithmic choices, Markov-chain-Monte-Carlo convergence diagnostics, sensitivity analyses, and Monte Carlo standard error calculations for Bayesian simulation studies. Although the BASIS framework focuses primarily on methodological research, it also provides useful guidance for researchers who rely on the results of Bayesian simulation studies or analyses, as current state-of-the-art guidelines for Bayesian analyses are incorporated into the BASIS.

Mapping the prevalence of cancer risk factors at the small area level in Australia

BACKGROUND

Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area-level data on cancer risk factors with high resolution and complete reach, which hinders the development of targeted prevention strategies.

METHODS

Using Australia as a case study, the 2017-2018 National Health Survey was used to generate prevalence estimates for 2221 small areas across Australia for eight cancer risk factor measures covering smoking, alcohol, physical activity, diet and weight. Utilising a recently developed Bayesian two-stage small area estimation methodology, the model incorporated survey-only covariates, spatial smoothing and hierarchical modelling techniques, along with a vast array of small area-level auxiliary data, including census, remoteness, and socioeconomic data. The models borrowed strength from previously published cancer risk estimates provided by the Social Health Atlases of Australia. Estimates were internally and externally validated.

RESULTS

We illustrated that in 2017-2018 health behaviours across Australia exhibited more spatial disparities than previously realised by improving the reach and resolution of formerly published cancer risk factors. The derived estimates revealed higher prevalence of unhealthy behaviours in more remote areas, and areas of lower socioeconomic status; a trend that aligned well with previous work.

CONCLUSIONS

Our study addresses the gaps in small area level cancer risk factor estimates in Australia. The new estimates provide improved spatial resolution and reach and will enable more targeted cancer prevention strategies at the small area level. Furthermore, by including the results in the next release of the Australian Cancer Atlas, which currently provides small area level estimates of cancer incidence and relative survival, this work will help to provide a more comprehensive picture of cancer in Australia by supporting policy makers, researchers, and the general public in understanding the spatial distribution of cancer risk factors. The methodology applied in this work is generalisable to other small area estimation applications and has been shown to perform well when the survey data are sparse.

pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods

BACKGROUND

Variability in datasets is not only the product of biological processes: they are also the product of technical biases. ComBat and ComBat-Seq are among the most widely used tools for correcting those technical biases, called batch effects, in, respectively, microarray and RNA-Seq expression data.

RESULTS

In this technical note, we present a new Python implementation of ComBat and ComBat-Seq. While the mathematical framework is strictly the same, we show here that our implementations: (i) have similar results in terms of batch effects correction; (ii) are as fast or faster than the original implementations in R and; (iii) offer new tools for the bioinformatics community to participate in its development. pyComBat is implemented in the Python language and is distributed under GPL-3.0 ( https://www.gnu.org/licenses/gpl-3.0.en.html ) license as a module of the inmoose package. Source code is available at https://github.com/epigenelabs/inmoose and Python package at https://pypi.org/project/inmoose .

CONCLUSIONS

We present a new Python implementation of state-of-the-art tools ComBat and ComBat-Seq for the correction of batch effects in microarray and RNA-Seq data. This new implementation, based on the same mathematical frameworks as ComBat and ComBat-Seq, offers similar power for batch effect correction, at reduced computational cost.

Dynamic and static predictive modelling of psychotherapy outcome-Comparison of two statistical approaches

OBJECTIVE

Improving prediction abilities in the therapy process can increase therapeutic success for a variety of reasons, such as more personalised treatment or resource optimisation. The increasingly applied methods of dynamic prediction seem to be very promising for this purpose. Prediction models are usually based on static approaches of frequentist statistics. However, the application of this statistical approach has been widely criticised in this research area. Bayesian statistics has been proposed in the literature as an alternative, especially for the task of dynamic modelling. In this study, we compare the performance of predicting therapy outcome over the course of therapy between both statistical approaches.

METHOD

Based on a sample of 341 patients, a logistic regression analysis was performed using both statistical approaches. Therapy success was conceptualised as reliable pre-post improvement in brief symptom inventory (BSI) scores. As predictors, we used the subscales of the Outcome Questionnaire (OQ-30) and the Helping Alliance Questionnaire (HAQ) measured every fifth session, as well as baseline BSI scores.

RESULTS

The influence of the predictors during therapy differs between the frequentist and the Bayesian approach. In contrast, predictive validity is comparable with a mean area under the curve (AUC) of 0.76 in both model types.

CONCLUSION

Bayesian statistic provides an innovative and useful alternative to the frequentist approach in predicting therapy outcome. The theoretical foundation is particularly well suited for dynamic prediction. Nevertheless, no differences in predictive validity were found in this study. More complex methodology as well as further research seems necessary to exploit the potential of Bayesian statistics in this area.

Detailed statistical analysis plan for a secondary Bayesian analysis of the SafeBoosC-III trial: a multinational, randomised clinical trial assessing treatment guided by cerebral oximetry monitoring versus usual care in extremely preterm infants

BACKGROUND

Extremely preterm infants have a high mortality and morbidity. Here, we present a statistical analysis plan for secondary Bayesian analyses of the pragmatic, sufficiently powered multinational, trial-SafeBoosC III-evaluating the benefits and harms of cerebral oximetry monitoring plus a treatment guideline versus usual care for such infants.

METHODS

The SafeBoosC-III trial is an investigator-initiated, open-label, randomised, multinational, pragmatic, phase III clinical trial with a parallel-group design. The trial randomised 1601 infants, and the frequentist analyses were published in April 2023. The primary outcome is a dichotomous composite outcome of death or severe brain injury. The exploratory outcomes are major neonatal morbidities associated with neurodevelopmental impairment later in life: (1) bronchopulmonary dysplasia; (2) retinopathy of prematurity; (3) late-onset sepsis; (4) necrotising enterocolitis; and (5) number of major neonatal morbidities (count of bronchopulmonary dysplasia, retinopathy of prematurity, and severe brain injury). The primary Bayesian analyses will use non-informed priors including all plausible effects. The models will use a Hamiltonian Monte Carlo sampler with 1 chain, a sampling of 10,000, and at least 25,000 iterations for the burn-in period. In Bayesian statistics, such analyses are referred to as 'posteriors' and will be presented as point estimates with 95% credibility intervals (CrIs), encompassing the most probable results based on the data, model, and priors selected. The results will be presented as probability of any benefit or any harm, Bayes factor, and the probability of clinical important benefit or harm. Two statisticians will analyse the blinded data independently following this protocol.

DISCUSSION

This statistical analysis plan presents a secondary Bayesian analysis of the SafeBoosC-III trial. The analysis and the final manuscript will be carried out and written after we publicise the primary frequentist trial report. Thus, we can interpret the findings from both the frequentists and Bayesian perspective. This approach should provide a better foundation for interpreting of our findings.

TRIAL REGISTRATION

ClinicalTrials.org, NCT03770741. Registered on 10 December 2018.

Statistics, philosophy, and health: the SMAC 2021 webconference

SMAC 2021 was a webconference organized in June 2021. The aim of this conference was to bring together data scientists, (bio)statisticians, philosophers, and any person interested in the questions of causality and Bayesian statistics, ranging from technical to philosophical aspects. This webconference consisted of keynote speakers and contributed speakers, and closed with a round-table organized in an unusual fashion. Indeed, organisers asked world renowned scientists to prepare two videos: a short video presenting a question of interest to them and a longer one presenting their point of view on the question. The first video served as a "teaser" for the conference and the second were presented during the conference as an introduction to the round-table. These videos and this round-table generated original scientific insights and discussion worthy of being shared with the community which we do by means of this paper.

A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies

Forecasting recruitments is a key component of the monitoring phase of multicenter studies. One of the most popular techniques in this field is the Poisson-Gamma recruitment model, a Bayesian technique built on a doubly stochastic Poisson process. This approach is based on the modeling of enrollments as a Poisson process where the recruitment rates are assumed to be constant over time and to follow a common Gamma prior distribution. However, the constant-rate assumption is a restrictive limitation that is rarely appropriate for applications in real studies. In this paper, we illustrate a flexible generalization of this methodology which allows the enrollment rates to vary over time by modeling them through B-splines. We show the suitability of this approach for a wide range of recruitment behaviors in a simulation study and by estimating the recruitment progression of the Canadian Co-infection Cohort.

Evidence for vocal signatures and voice-prints in a wild parrot

In humans, identity is partly encoded in a voice-print that is carried across multiple vocalizations. Other species also signal vocal identity in calls, such as shown in the contact call of parrots. However, it remains unclear to what extent other call types in parrots are individually distinct, and whether there is an analogous voice-print across calls. Here we test if an individual signature is present in other call types, how stable this signature is, and if parrots exhibit voice-prints across call types. We recorded 5599 vocalizations from 229 individually marked monk parakeets (Myiopsitta monachus) over a 2-year period in Barcelona, Spain. We examined five distinct call types, finding evidence for an individual signature in three. We further show that in the contact call, while birds are individually distinct, the calls are more variable than previously assumed, changing over short time scales (seconds to minutes). Finally, we provide evidence for voice-prints across multiple call types, with a discriminant function being able to predict caller identity across call types. This suggests that monk parakeets may be able to use vocal cues to recognize conspecifics, even across vocalization types and without necessarily needing active vocal signatures of identity.

Moderating Effect of eHealth Literacy on the Associations of Coronaphobia With Loneliness, Irritability, Depression, and Stigma in Chinese Young Adults: Bayesian Structural Equation Model Study

BACKGROUND

The COVID-19 pandemic has led to an increase in known risk factors for mental health problems. Although medical information available through the internet and smartphones has greatly expanded, people's ability to seek, eschew, and use reliable web-based medical information and services to promote their mental health remains unknown.

OBJECTIVE

This study aims to explore the associations between coronaphobia and 4 frequently reported mental health problems, loneliness, irritability, depression, and stigma, during the COVID-19 pandemic and to assess the moderating effects of eHealth literacy (eHL) on the adjustment of these relationships in Chinese young adults.

METHODS

The data used in this study were collected from a web-based survey of the general Chinese population, aged between 18 and 30 years, conducted in China between December 2022 and January 2023. A nonprobability snowball sampling method was used for data collection. A Bayesian structural equation model (BSEM) using parameter expansion was used to estimate the moderating effect of eHL on the relationship between coronaphobia and psychological problems. The posterior mean and 95% highest density intervals (HDIs) were estimated.

RESULTS

A total of 4119 participants completed the questionnaire and provided valid responses. Among them, 64.4% (n=2653) were female and 58.7% (n=2417) were rural residents. All measures showed statistically significant but minor-to-moderate associations (correlation coefficients ranged from -0.04 to 0.65). Significant heterogeneity was observed between rural and urban residents at the eHL level, and coronaphobia was observed. The BSEM results demonstrated that eHL was a significant moderator in reducing the negative effects of coronaphobia on loneliness (posterior mean -0.0016, 95% HDI -0.0022 to -0.0011), depression (posterior mean -0.006, 95% HDI -0.0079 to -0.004), stigma (posterior mean -0.0052, 95% HDI -0.0068 to -0.0036), and irritability (posterior mean -0.0037, 95% HDI -0.0052 to -0.0022). The moderating effects of eHL varied across the rural and urban subsamples.

CONCLUSIONS

Using BSEM, this study demonstrated that improving eHL can significantly mitigate the negative effects of coronaphobia on 4 COVID-19-related mental health problems in Chinese young adults. Future eHL initiatives should target rural communities to ensure equal access to information and resources that can help protect their mental health during the pandemic.

UNMF: a unified nonnegative matrix factorization for multi-dimensional omics data

Factor analysis, ranging from principal component analysis to nonnegative matrix factorization, represents a foremost approach in analyzing multi-dimensional data to extract valuable patterns, and is increasingly being applied in the context of multi-dimensional omics datasets represented in tensor form. However, traditional analytical methods are heavily dependent on the format and structure of the data itself, and if these change even slightly, the analyst must change their data analysis strategy and techniques and spend a considerable amount of time on data preprocessing. Additionally, many traditional methods cannot be applied as-is in the presence of missing values in the data. We present a new statistical framework, unified nonnegative matrix factorization (UNMF), for finding informative patterns in messy biological data sets. UNMF is designed for tidy data format and structure, making data analysis easier and simplifying the development of data analysis tools. UNMF can handle a wide range of data structures and formats, and works seamlessly with tensor data including missing observations and repeated measurements. The usefulness of UNMF is demonstrated through its application to several multi-dimensional omics data, offering user-friendly and unified features for analysis and integration. Its application holds great potential for the life science community. UNMF is implemented with R and is available from GitHub (https://github.com/abikoushi/moltenNMF).

BG2: Bayesian variable selection in generalized linear mixed models with nonlocal priors for non-Gaussian GWAS data

BACKGROUND

Genome-wide association studies (GWASes) aim to identify single nucleotide polymorphisms (SNPs) associated with a given phenotype. A common approach for the analysis of GWAS is single marker analysis (SMA) based on linear mixed models (LMMs). However, LMM-based SMA usually yields a large number of false discoveries and cannot be directly applied to non-Gaussian phenotypes such as count data.

RESULTS

We present a novel Bayesian method to find SNPs associated with non-Gaussian phenotypes. To that end, we use generalized linear mixed models (GLMMs) and, thus, call our method Bayesian GLMMs for GWAS (BG2). To deal with the high dimensionality of GWAS analysis, we propose novel nonlocal priors specifically tailored for GLMMs. In addition, we develop related fast approximate Bayesian computations. BG2 uses a two-step procedure: first, BG2 screens for candidate SNPs; second, BG2 performs model selection that considers all screened candidate SNPs as possible regressors. A simulation study shows favorable performance of BG2 when compared to GLMM-based SMA. We illustrate the usefulness and flexibility of BG2 with three case studies on cocaine dependence (binary data), alcohol consumption (count data), and number of root-like structures in a model plant (count data).

The Significance of Bayesian Pharmacokinetics in Dosing for Critically Ill Patients: A Primer for Clinicians Using Vancomycin as an Example

Antibiotic use is becoming increasingly challenging with the emergence of multidrug-resistant organisms. Pharmacokinetic (PK) alterations result from complex pathophysiologic changes in some patient populations, particularly those with critical illness. Therefore, antibiotic dose individualization in such populations is warranted. Recently, there have been advances in dose optimization strategies to improve the utilization of existing antibiotics. Bayesian-based dosing is one of the novel approaches that could help clinicians achieve target concentrations in a greater percentage of their patients earlier during therapy. This review summarizes the advantages and disadvantages of current approaches to antibiotic dosing, with a focus on critically ill patients, and discusses the use of Bayesian methods to optimize vancomycin dosing. The Bayesian method of antibiotic dosing was developed to provide more precise predictions of drug concentrations and target achievement early in therapy. It has benefits such as the incorporation of personalized PK/PD parameters, improved predictive abilities, and improved patient outcomes. Recent vancomycin dosing guidelines emphasize the importance of using the Bayesian method. The Bayesian method is able to achieve appropriate antibiotic dosing prior to the patient reaching the steady state, allowing the patient to receive the right drug at the right dose earlier in therapy.

Improving the stability of bivariate correlations using informative Bayesian priors: a Monte Carlo simulation study

Objective

Much of psychological research has suffered from small sample sizes and low statistical power, resulting in unstable parameter estimates. The Bayesian approach offers a promising solution by incorporating prior knowledge into statistical models, which may lead to improved stability compared to a frequentist approach.

Methods

Simulated data from four populations with known bivariate correlations (ρ = 0.1, 0.2, 0.3, 0.4) was used to estimate the sample correlation as samples were sequentially added from the population, from n = 10 to n = 500. The impact of three different, subjectively defined prior distributions (weakly, moderately, and highly informative) was investigated and compared to a frequentist model.

Results

The results show that bivariate correlation estimates are unstable, and that the risk of obtaining an estimate that is exaggerated or in the wrong direction is relatively high, for sample sizes for below 100, and considerably so for sample sizes below 50. However, this instability can be constrained by informative Bayesian priors.

Conclusion

Informative Bayesian priors have the potential to significantly reduce sample size requirements and help ensure that obtained estimates are in line with realistic expectations. The combined stabilizing and regularizing effect of a weakly informative prior is particularly useful when conducting research with small samples. The impact of more informative Bayesian priors depends on one's threshold for probability and whether one's goal is to obtain an estimate merely in the correct direction, or to obtain a high precision estimate whose associated interval falls within a narrow range. Implications for sample size requirements and directions for future research are discussed.

PyMC: a modern, and comprehensive probabilistic programming framework in Python

PyMC is a probabilistic programming library for Python that provides tools for constructing and fitting Bayesian models. It offers an intuitive, readable syntax that is close to the natural syntax statisticians use to describe models. PyMC leverages the symbolic computation library PyTensor, allowing it to be compiled into a variety of computational backends, such as C, JAX, and Numba, which in turn offer access to different computational architectures including CPU, GPU, and TPU. Being a general modeling framework, PyMC supports a variety of models including generalized hierarchical linear regression and classification, time series, ordinary differential equations (ODEs), and non-parametric models such as Gaussian processes (GPs). We demonstrate PyMC's versatility and ease of use with examples spanning a range of common statistical models. Additionally, we discuss the positive role of PyMC in the development of the open-source ecosystem for probabilistic programming.

Spatial-temporal patterns and influencing factors for pulmonary tuberculosis transmission in China: an analysis based on 15 years of surveillance data

Profiting from a series of anti-tuberculosis programs in China, the number of tuberculosis (TB) cases has diminished dramatically in the past decades. However, long-term spatial-temporal variations, regional trends of prevalence, and mechanisms of determinant factors remain unclear. Age-period-cohort analysis and Bayesian space-time hierarchy statistics were conducted to identify high-risk populations and areas in mainland China, and the geographical detector model was used to evaluate the important drivers of the disease. The prevalence of pulmonary TB has declined from 73.3/100,000 in 2004 to 55.45/100,000 in 2018. A bimodal distribution was found in age groups, and the birth cohorts before 1978 had relative higher risk. The high-risk areas were mainly distributed in western China and south-central China, and several provinces in eastern China showed a potential increasing trend, including Beijing, Shanghai, Liaoning, and Guangdong province. The index of night light (Q = 0.46), the population density (Q = 0.41), PM10 (Q = 0.38), urbanization rate (Q = 0.32), and PM 2.5 (Q = 0.31) contributed substantially to the spatial distribution of pulmonary tuberculosis. The identifications of epidemic patterns, high-risk areas and influence factors would help design targeted intervention measures to achieve milestones of the end TB strategy.

Weight of evidence tools in the prediction of acute fish toxicity

Acute fish toxicity (AFT) is a key endpoint in nearly all regulatory implementations of environmental hazard assessments of chemicals globally. Although it is an early tier assay, the AFT assay is complex and uses many juvenile fish each year for the registration and assessment of chemicals. Thus, it is imperative to seek animal alternative approaches to replace or reduce animal use for environmental hazard assessments. A Bayesian Network (BN) model has been developed that brings together a suite of lines of evidence (LoEs) to produce a probabilistic estimate of AFT without the testing of additional juvenile fish. Lines of evidence include chemical descriptors, mode of action (MoA) assignment, knowledge of algal and daphnid acute toxicity, and animal alternative assays such as fish embryo tests and in vitro fish assays (e.g., gill cytotoxicity). The effort also includes retrieval, assessment, and curation of quality acute fish toxicity data because these act as the baseline of comparison with model outputs. An ideal outcome of this effort would be to have global applicability, acceptance and uptake, relevance to predominant fish species used in chemical assessments, be expandable to allow incorporation of future knowledge, and data to be publicly available. The BN model can be conceived as having incorporated principles of tiered assessment and whose outcomes will be directed by the available evidence in combination with prior information. We demonstrate that, as additional evidence is included in the prediction of a given chemical's ecotoxicity profile, both the accuracy and the precision of the predicted AFT can increase. Ultimately an improved environmental hazard assessment will be achieved. Integr Environ Assess Manag 2023;19:1220-1234. © 2022 SETAC.

Accurately summarizing an outbreak using epidemiological models takes time

Recent outbreaks of Mpox and Ebola, and worrying waves of COVID-19, influenza and respiratory syncytial virus, have all led to a sharp increase in the use of epidemiological models to estimate key epidemiological parameters. The feasibility of this estimation task is known as the practical identifiability (PI) problem. Here, we investigate the PI of eight commonly reported statistics of the classic susceptible-infectious-recovered model using a new measure that shows how much a researcher can expect to learn in a model-based Bayesian analysis of prevalence data. Our findings show that the basic reproductive number and final outbreak size are often poorly identified, with learning exceeding that of individual model parameters only in the early stages of an outbreak. The peak intensity, peak timing and initial growth rate are better identified, being in expectation over 20 times more probable having seen the data by the time the underlying outbreak peaks. We then test PI for a variety of true parameter combinations and find that PI is especially problematic in slow-growing or less-severe outbreaks. These results add to the growing body of literature questioning the reliability of inferences from epidemiological models when limited data are available.

False-negative probability in the SEM/EDS automated discovery of iGSR particles: A Bayesian approach

The automated search software integrated with a scanning electron microscope (SEM/EDS) has been the standard tool for detecting inorganic gunshot residues (iGSR) for several decades. The detection of these particles depends on various factors such as collection, preservation, contamination with organic matter, and the method for sample analysis. This article focuses on the influence of equipment resolution setup on the backscattered electron images of the sample. The pixel size of these images plays a crucial role in determining the detectability of iGSR particles, especially those with sizes close to the pixel size. In this study, we calculated the probability of missing all characteristic iGSR particles in a sample using an SEM/EDS automated search and how it depends on the image pixel resolution setup. We developed and validated an iGSR particle detection model that links particle size with equipment registers and applied it to 320 samples analyzed by a forensic science laboratory. Our results show that the probability of missing all characteristic iGSR particles due to their size is below 5% for pixel sizes below 0.32 μm2 . These findings indicate that pixel sizes as large as twice the one commonly used in laboratory casework, that is, 0.16 μm2 , are effective for initial sample scanning, yielding good detection rates of characteristic particles that could exponentially reduce laboratory workload.

Estimating conflict losses and reporting biases

Determining the number of casualties and fatalities suffered in militarized conflicts is important for conflict measurement, forecasting, and accountability. However, given the nature of conflict, reliable statistics on casualties are rare. Countries or political actors involved in conflicts have incentives to hide or manipulate these numbers, while third parties might not have access to reliable information. For example, in the ongoing militarized conflict between Russia and Ukraine, estimates of the magnitude of losses vary wildly, sometimes across orders of magnitude. In this paper, we offer an approach for measuring casualties and fatalities given multiple reporting sources and, at the same time, accounting for the biases of those sources. We construct a dataset of 4,609 reports of military and civilian losses by both sides. We then develop a statistical model to better estimate losses for both sides given these reports. Our model accounts for different kinds of reporting biases, structural correlations between loss types, and integrates loss reports at different temporal scales. Our daily and cumulative estimates provide evidence that Russia has lost more personnel than has Ukraine and also likely suffers from a higher fatality to casualty ratio. We find that both sides likely overestimate the personnel losses suffered by their opponent and that Russian sources underestimate their own losses of personnel.

Quantum Knowledge in Phase Space

Quantum physics through the lens of Bayesian statistics considers probability to be a degree of belief and subjective. A Bayesian derivation of the probability density function in phase space is presented. Then, a Kullback-Liebler divergence in phase space is introduced to define interference and entanglement. Comparisons between each of these two quantities and the entropy are made. A brief presentation of entanglement in phase space to the spin degree of freedom and an extension to mixed states completes the work.

Bayesian inference of polymerase dynamics over the exclusion process

Transcription is a complex phenomenon that permits the conversion of genetic information into phenotype by means of an enzyme called RNA polymerase, which erratically moves along and scans the DNA template. We perform Bayesian inference over a paradigmatic mechanistic model of non-equilibrium statistical physics, i.e. the asymmetric exclusion processes in the hydrodynamic limit, assuming a Gaussian process prior for the polymerase progression rate as a latent variable. Our framework allows us to infer the speed of polymerases during transcription given their spatial distribution, while avoiding the explicit inversion of the system's dynamics. The results, which show processing rates strongly varying with genomic position and minor role of traffic-like congestion, may have strong implications for the understanding of gene expression.

Improving the efficiency of clinical trials in multiple sclerosis

BACKGROUND

Phase 3 clinical trials for disease-modifying therapies in relapsing-remitting multiple sclerosis (RRMS) have utilized a limited number of conventional designs with a high degree of success. However, these designs limit the types of questions that can be addressed, and the time and cost required. Moreover, trials involving people with progressive multiple sclerosis (MS) have been less successful.

OBJECTIVE

The objective of this paper is to discuss complex innovative trial designs, intermediate and composite outcomes and to improve the efficiency of trial design in MS and broaden questions that can be addressed, particularly as applied to progressive MS.

METHODS

We held an international workshop with experts in clinical trial design.

RESULTS

Recommendations include increasing the use of complex innovative designs, developing biomarkers to enrich progressive MS trial populations, prioritize intermediate outcomes for further development that target therapeutic mechanisms of action other than peripherally mediated inflammation, investigate acceptability to people with MS of data linkage for studying long-term outcomes of clinical trials, use Bayesian designs to potentially reduce sample sizes required for pediatric trials, and provide sustained funding for platform trials and registries that can support pragmatic trials.

CONCLUSION

Novel trial designs and further development of intermediate outcomes may improve clinical trial efficiency in MS and address novel therapeutic questions.

Associations between Gun Ownership and Firearm Homicide Rates in US States

The United States combine high rates of firearm homicides with high gun prevalence. In the past, a significant positive association was found between the two. This study revisits the gun prevalence-gun homicide debate using more elaborate estimates of gun ownership for the 50 States. Longitudinal data (1999-2016) were analysed with Bayesian multilevel Gamma-Poisson models. The results demonstrated a very small positive association that diminished after adjusting for crime rates. Findings suggest that the association either attenuated in more recent years, or previous studies had overestimated this association.

When not responding to food changes food value: The role of timing

Establishing behavior change toward appetitive foods can be crucial to improve people's health. Food go/no-go training (GNG), in which people respond to some food items and not to other food items depending on the presentation of a go or no-go cue, is a means to establish behavior change. GNG changes the perceived value of food items and food consumption. After GNG, no-go items are rated as less attractive than go and/or untrained items, an empirical phenomenon called the NoGo-devaluation-effect. This effect is not always found, however. One theory-based explanation for these inconsistent results may be found in the timing of the go and no-go cues, which is also inconsistent across studies. Hence, in the present work we conducted two experiments to examine the possible role of go and no-go cue presentation timing in eliciting the NoGo-devaluation-effect. In Experiment 1, we presented the food items before the presentation of go/no-go cues, whereas we reversed this order in Experiment 2. As predicted, the NoGo-devaluation-effect was obtained in Experiment 1. This effect was absent in Experiment 2. Moreover, recognition memory for stimulus-action contingencies moderated the devaluation effect in Experiment 1, but not in Experiment 2. These results show that NoGo devaluation is dependent on the timing of the NoGo cue, which has theoretical and applied implications for understanding how and when go/no-go training influences food consumption. We propose that the value of food items is updated during go/no-go training to minimize prediction errors, and that this updating process is boosted by attention.

Statistical methods to identify mechanisms in studies of eco-evolutionary dynamics

While the reciprocal effects of ecological and evolutionary dynamics are increasingly recognized as an important driver for biodiversity, detection of such eco-evolutionary feedbacks, their underlying mechanisms, and their consequences remains challenging. Eco-evolutionary dynamics occur at different spatial and temporal scales and can leave signatures at different levels of organization (e.g., gene, protein, trait, community) that are often difficult to detect. Recent advances in statistical methods combined with alternative hypothesis testing provides a promising approach to identify potential eco-evolutionary drivers for observed data even in non-model systems that are not amenable to experimental manipulation. We discuss recent advances in eco-evolutionary modeling and statistical methods and discuss challenges for fitting mechanistic models to eco-evolutionary data.

The 10 "Cardinal Sins" in the Clinical Diagnosis and Treatment of Endometriosis: A Bayesian Approach

Evidence-based data for endometriosis management are limited. Experiments are excluded without adequate animal models. Data are limited to symptomatic women and occasional observations. Hormonal medical therapy cannot be blinded if recognised by the patient. Randomised controlled trials are not realistic for surgery, since endometriosis is a variable disease with low numbers. Each diagnosis and treatment is an experiment with an outcome, and experience is the means by which Bayesian updating, according to the past, takes place. If the experiences of many are similar, this holds more value than an opinion. The combined experience of a group of endometriosis surgeons was used to discuss problems in managing endometriosis. Considering endometriosis as several genetically/epigenetically different diseases is important for medical therapy. Imaging cannot exclude endometriosis, and diagnostic accuracy is limited for superficial lesions, deep lesions, and cystic corpora lutea. Surgery should not be avoided for emotional reasons. Shifting infertility treatment to IVF without considering fertility surgery is questionable. The concept of complete excision should be reconsidered. Surgeons should introduce quality control, and teaching should move to explain why this occurs. The perception of information has a personal bias. These are the major problems involved in managing endometriosis, as identified by the combined experience of the authors, who are endometriosis surgeons.

Evaluation of the performance of the IFN-γ release assay in bovine tuberculosis free herds from five European countries

The diagnostic methods for granting and maintenance of the official tuberculosis-free (OTF) status and for intra-Community movement of cattle are the tuberculin skin tests (single or comparative) and the interferon-γ (IFN-γ) release assay (IGRA). However, until now, IGRAs have been primarily applied in infected farms in parallel to the skin test to maximize the number of infected animals detected. Therefore, an evaluation of the performance of IGRAs in OTF herds to assess whether if their specificity is equal to or higher than that of the skin tests is needed. For this, a panel of 4365 plasma samples coming from 84 OTF herds in six European regions (five countries) was assembled and analysed using two IGRA kits, the ID Screen^® Ruminant IFN-g (IDvet) and the Bovigam™ TB Kit (Bovigam). Results were evaluated using different cut-offs, and the impact of herd and animal-level factors on the probability of positivity was assessed using hierarchical Bayesian multivariable logistic regression models. The percentage of reactors ranged from 1.7 to 21.0% (IDvet: S/P ≥ 35%), and 2.1-26.3% (Bovigam: OD_bovis-OD_PBS ≥ 0.1 and OD_bovis-OD_avium ≥ 0.1) depending on the region, with Bovigam disclosing more reactors in all regions. The results suggest that specificity of IGRAs can be influenced by the production type, age and region of origin of the animals. Changes in the cut-offs could lead to specificity values above 98-99% in certain OTF populations, but no single cut-off yielding a sufficiently high specificity (equal or higher than that of skin tests) in all populations was identified. Therefore, an exploratory analysis of the baseline IFN-γ reactivity in OTF populations could help to assess the usefulness of this technique when applied for the purpose of maintaining OTF status.

Outcome prediction of electroconvulsive therapy for depression

INTRODUCTION

We developed and tested a Bayesian network(BN) model to predict ECT remission for depression, with non-response as a secondary outcome.

METHODS

We performed a systematic literature search on clinically available predictors. We combined these predictors with variables from a dataset of clinical ECT trajectories (performed in the University Medical Center Utrecht) to create priors and train the BN. Temporal validation was performed in an independent sample.

RESULTS

The systematic literature search yielded three meta-analyses, which provided prior knowledge on outcome predictors. The clinical dataset consisted of 248 treatment trajectories in the training set and 44 trajectories in the test set at the same medical center. The AUC for the primary outcome remission estimated on an independent validation set was 0.686 (95%CI 0.513-0.859) (AUC values of 0.505 - 0.763 observed in 5-fold cross validation of the model within the train set). Accuracy 0.73 (balanced accuracy 0.67), sensitivity 0.55, specificity 0.79, after temporal validation in the independent sample. Prior literature information marginally reduced CI width.

DISCUSSION

A BN model comprised of prior knowledge and clinical data can predict remission of depression after ECT with reasonable performance. This approach can be used to make outcome predictions in psychiatry, and offers a methodological framework to weigh additional information, such as patient characteristics, symptoms and biomarkers. In time, it may be used to improve shared decision-making in clinical practice.

Examining mechanisms linking economic insecurity to interparental conflict among couples with low income

Objective

The current study used the family stress model to test the mechanisms by which economic insecurity contributes to mothers' and fathers' mental health and couples' relationship functioning.

Background

Although low household income has been a focus of poverty research, material hardship-defined as everyday challenges related to making ends meet including difficulties paying for housing, utilities, food, or medical care-is common among American families.

Methods

Participants were from the Building Strong Families project. Couples were racially diverse (43.52% Black; 28.88% Latinx; 17.29% White; 10.31% Other) and living with low income (N = 2,794). Economic insecurity included income poverty and material hardship. Bayesian mediation analysis was employed, taking advantage of the prior evidence base of the family stress model.

Results

Material hardship, but not income poverty, predicted higher levels of both maternal and paternal depressive symptoms. Only paternal depressive symptoms were linked with higher levels of destructive interparental conflict (i.e., moderate verbal aggression couples use that could be harmful to the partner relationship). Mediation analysis confirmed that material hardship operated primarily through paternal depressive symptoms in its association with destructive interparental conflict.

Conclusion

The economic stress of meeting the daily material needs of the family sets the stage for parental mental health problems that carry over to destructive interparental conflict, especially through paternal depressive symptoms.

Implications

Family-strengthening programs may want to consider interventions to address material hardship (e.g., comprehensive needs assessments, connections to community-based resources, parents' employment training) as part of their efforts to address parental mental health and couples' destructive conflict behaviors.

Explaining the description-experience gap in risky decision-making: learning and memory retention during experience as causal mechanisms

When making decisions based on probabilistic outcomes, people guide their behavior using knowledge gathered through both indirect descriptions and direct experience. Paradoxically, how people obtain information significantly impacts apparent preferences. A ubiquitous example is the description-experience gap: individuals seemingly overweight low probability events when probabilities are described yet underweight them when probabilities must be experienced firsthand. A leading explanation for this fundamental gap in decision-making is that probabilities are weighted differently when learned through description relative to experience, yet a formal theoretical account of the mechanism responsible for such weighting differences remains elusive. We demonstrate how various learning and memory retention models incorporating neuroscientifically motivated learning mechanisms can explain why probability weighting and valuation parameters often are found to vary across description and experience. In a simulation study, we show how learning through experience can lead to systematically biased estimates of probability weighting when using a traditional cumulative prospect theory model. We then use hierarchical Bayesian modeling and Bayesian model comparison to show how various learning and memory retention models capture participants' behavior over and above changes in outcome valuation and probability weighting, accounting for description and experience-based decisions in a within-subject experiment. We conclude with a discussion of how substantive models of psychological processes can lead to insights that heuristic statistical models fail to capture.