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Background: Exploring clinical trial data using alternative methods may enhance original study's findings and provide new insights. The SOAP II trial has been published more than 10 years ago; but there is still some speculation that some patients may benefit from dopamine administration for shock management. We aimed to reanalyse the trial under different approaches and evaluate for heterogeneity in treatment effect (HTE). Methods: All patients enrolled in SOAP II were eligible for reanalysis. We used a variety of methods including the win-ratio (WR), a Bayesian reanalysis stratified according to shock type, and both a risk-based and effect-based explorations for HTE. The methods were applied to different endpoints, including a hierarchy of death, new use of renal-replacement therapy (RRT), and new-onset arrhythmia; 28-day mortality; a composite endpoint (mortality, new use of RRT, and new-onset arrhythmia), and days alive and free of ICU at 28-days (DAFICU28). Results: A total of 1679 patients were included (average age was 64.9 years, 57% male, 62% with septic and 17% with cardiogenic shock). All analysis favoured norepinephrine over dopamine. Under the WR approach, dopamine had fewer wins compared to norepinephrine (WR 0.79; 95% confidence intervals [CI] 0.68–0.92; p = 0.003), evident in both cardiogenic and septic shock subgroups. The Bayesian reanalysis for type of shock showed, for dopamine, a probability of harm of 0.95 for mortality, > 0.99 probability of harm for composite endpoint, and 0.91 probability of harm for DAFICU28. The fewer DAFICU28 with dopamine was more apparent in those with cardiogenic shock (0.92). Under the risk-based HTE, there was a high probability that dopamine resulted fewer DAFICU28 in the highest quartile of predicted mortality risk. The effect-based HTE assessment model did not recommended dopamine over norepinephrine for any combination of possible modifiers including age, type of shock, presence of cardiomyopathy, and SOFA score. Receiving dopamine when the effect-based model recommended norepinephrine was associated with an absolute increase in composite endpoint of 6%. Conclusion: The harm associated with the use of dopamine for the management of shock appears to be present in both septic and cardiogenic shock patients. There was no suggestion of any subgroup in which dopamine was found to be favourable over norepinephrine. [ABSTRACT FROM AUTHOR]

Background: For decades, dengue has posed a significant threat as a viral infectious disease, affecting numerous human lives globally, particularly in tropical regions, yet no cure has been discovered. The genetic trait of vector competence in Aedes mosquitoes, which facilitates dengue transmission, is difficult to measure and highly sensitive to environmental changes. Methods: In this study we attempt, for the first time in a non-laboratory setting, to quantify the vector competence of Aedes mosquitoes assuming its homogeneity across both species; aegypti and albopictus and across the four Dengue serotypes. Estimating vector competence in relation to varying rainfall patterns was focused in this study to showcase the changes in this vector trait with respect to environmental variables. We quantify it using an existing mathematical model originally developed for malaria in a Bayesian inferencing setup. We conducted this study in the Colombo district of Sri Lanka where the highest number of human populations are threatened with dengue. Colombo district experiences continuous favorable temperature and humidity levels throughout the year creating ideal conditions for Aedes mosquitoes to thrive and transmit the Dengue disease. Therefore we only used the highly variable and seasonal rainfall as the primary environmental variable as it significantly influences the number of breeding sites and thereby impacting the population dynamics of Aedes. Results: Our research successfully deduced vector competence values for the four identified seasons based on Monsoon rainfalls experienced in Colombo within a year. We used dengue data from 2009 - 2022 to infer the estimates. These estimated values have been corroborated through experimental studies documented in the literature, thereby validating the malaria model to estimate vector competence for dengue disease. Conclusion: Our research findings conclude that environmental conditions can amplify vector competence within specific seasons, categorized by their environmental attributes. Additionally, the deduced vector competence offers compelling evidence that it impacts disease transmission, irrespective of geographical location, climate, or environmental factors. [ABSTRACT FROM AUTHOR]

Background: There is increasing focus on HIV-1 CRF55_01B in China. However, there is limited information regarding the dissemination of CRF55_01B across different regions and populations in Guangxi. This study was performed to elucidate the evolutionary history of the introduction and dissemination of CRF55_01B in Guangxi. Methods: Molecular network and phylogenetic analyses were used to investigate the transmission characteristics of CRF55_01B in China. The analyses particularly focused on the cross-provincial spatial and temporal transmission patterns between Guangdong Province and Guangxi, as well as the transmission dynamics among different regions and populations within Guangxi. Results: In total, 2226 partial pol sequences of CRF55_01B strains sampled from 2007 to 2022 were collected, including 1895 (85.09%) sequences from Guangdong, 199 (8.94%) sequences from Guangxi, and 172 (7.59%) sequences from other provinces of China. Most people living with HIV in Guangxi were infected with HIV-1 through heterosexuals (52.76%). Among these, 19.10% had a history of commercial heterosexual contact (CHC) and 15.58% had a history of non-marital non-commercial heterosexual contact (NMNCHC). Overall, 1418 sequences were identified in the molecular network. Notably, the sequences from Guangdong Province were most closely linked to those from Guangxi. Phylogenetic analysis showed that CRF55_01B was first introduced from Shenzhen City to Nanning City around 2007. Subsequently, CRF55_01B established local transmission within Guangxi, with Nanning City serving as the transmission center from 2008 to 2017. After 2017, the CRF55_01B strain spread to other regions of Guangxi. Men who have sex with men (MSM) and men with a history of CHC have played a significant role in the transmission of CRF55_01B among different populations in Guangxi. Conclusions: This study provides evidence on the transmission trajectory of CRF55_01B among different regions and populations in Guangxi. Given the bridging role of men with a history of CHC in the dissemination of CRF55_01B from MSM to the general population, it is imperative to enhance surveillance among key populations to mitigate the secondary transmission of HIV-1. [ABSTRACT FROM AUTHOR]

Proper and timely characterization of the safety profile of a pharmaceutical product under development is imperative for assessing the overall benefit-risk relationship of the product and for making key development decisions. For ongoing clinical development, a comprehensive and robust safety monitoring and safety signal detection program which is based upon quantitative statistical reasoning is critical. Methods presented here can be applied to safety signal detection and periodic safety monitoring. Various statistical properties, distributions, and models, all utilizing a Bayesian framework are considered and further examined in order to identify robust methods applicable to a broad set of scenarios and situations. Methods developed for incidence counts (including those with under-dispersed distributions) with variable time-at-risk and with underlying constant or non-constant hazard rates, are proposed and compared to traditional methods designed to assess adverse event incidence rates or binomial incidence proportions (which assume an underlying constant hazard rate and subsequent Poisson distribution for modeling event counts). [ABSTRACT FROM AUTHOR]

Experiments are routinely conducted to evaluate the toxicity of water effluents or chemicals. In a Ceriodaphnia dubia reproduction test, organisms are exposed to varying concentration levels of the toxicant or other adverse treatment and the number of young produced after a given experiment period are recorded. To analyze the test outcomes, mean reproduction is modeled as a function of the concentration, and the estimated concentrations associated with specified levels of adverse effect often are used in risk management. While aquatic toxicity analyses often focus on outcomes from the current test, laboratories commonly have a history of conducting such tests using the same species, following a similar experimental protocol. So it seems reasonable to assume that the same underlying biological process generates the historical and current tests. In the present study, we propose using a calibrated power prior approach to incorporate historical control outcomes as prior input, and compare the behavior of the method when different congruence measures are used to determine the amount of historical input that will be incorporated. Simulation results show that three of the congruence measures can improve precision and reduce bias of the potency estimates.Supplementary material to this paper is provided online. [ABSTRACT FROM AUTHOR]

Background: Traditionally, meta‐analysis of time‐to‐event outcomes reports a single pooled hazard ratio assuming proportional hazards (PH). For health technology assessment evaluations, hazard ratios are frequently extrapolated across a lifetime horizon. However, when treatment effects vary over time, an assumption of PH is not always valid. The Royston‐Parmar (RP), piecewise exponential (PE), and fractional polynomial (FP) models can accommodate non‐PH and provide plausible extrapolations of survival curves beyond observed data. Methods: Simulation study to assess and compare the performance of RP, PE, and FP models in a Bayesian framework estimating restricted mean survival time difference (RMSTD) at 50 years from a pairwise meta‐analysis with evidence of non‐PH. Individual patient data were generated from a mixture Weibull distribution. Twelve scenarios were considered varying the amount of follow‐up data, number of trials in a meta‐analysis, non‐PH interaction coefficient, and prior distributions. Performance was assessed through bias and mean squared error. Models were applied to a metastatic breast cancer example. Results: FP models performed best when the non‐PH interaction coefficient was 0.2. RP models performed best in scenarios with complete follow‐up data. PE models performed well on average across all scenarios. In the metastatic breast cancer example, RMSTD at 50‐years ranged from −14.6 to 8.48 months. Conclusions: Synthesis of time‐to‐event outcomes and estimation of RMSTD in the presence of non‐PH can be challenging and computationally intensive. Different approaches make different assumptions regarding extrapolation and sensitivity analyses varying key assumptions are essential to check the robustness of conclusions to different assumptions for the underlying survival function. [ABSTRACT FROM AUTHOR]

Conservation decisions are often based on population models that rely on underlying assumptions made about species' demographic parameters. The Rio Grande Cooter (Pseudemys gorzugi) is a conservation-dependent riverine turtle, but our understanding of its population dynamics has been limited due to the absence of long-term surveys. We used mark-recapture data collected between 2016 and 2022 from the Black River, New Mexico, USA, to evaluate P. gorzugi somatic growth parameters. Females reached larger maximum size than males but exhibited a slower growth rate. We observed only a slight difference in maximum length and Brody growth coefficient of turtles from different locations, but the growth coefficients were overall significantly lower than any other Pseudemys species. Turtles on the Black River represent the northernmost robust population of P. gorzugi. In light of the intense human use of the river and riparian areas coupled with the impacts of climate change, it is imperative to maintain commitment to conservation efforts to resist population declines given the slow somatic growth rate of the population. [ABSTRACT FROM AUTHOR]

In the last decade Bayesian analysis of radiocarbon dates has become commonplace for archaeologists grappling with regional chronologies. Here we present Bayesian analysis for radiocarbon dates from Moche cultural contexts from the north coast of Peru with the aim to understand the duration of the phenomenon and to compile all known dates in a single location for future analysis and use. The analysis demonstrates that the long-standing Moche ceramic sequence does not provide a perfect proxy for tracking the timing of all social interactions. However, our results show general agreement with traditional relative Moche chronologies. Finally, our study shows that Moche was a shorter cultural phenomenon than has been previously argued, with the current data indicating that it began between the late 4th and early 6th centuries CE and lasted until the 9th century CE. [ABSTRACT FROM AUTHOR]

Researchers are often unsure about the quality of the data collected by third-party actors, such as survey firms. This may be because of the inability to measure data quality effectively at scale and the difficulty with communicating which observations may be the source of measurement error. Researchers rely on survey firms to provide them with estimates of data quality and to identify observations that are problematic, potentially because they have been falsified or poorly collected. To address these issues, I propose the QualMix model, a mixture modeling approach to deriving estimates of survey data quality in situations in which two sets of responses exist for all or certain subsets of respondents. I apply this model to the context of survey reinterviews, a common form of data quality assessment used to detect falsification and data collection problems during enumeration. Through simulation based on real-world data, I demonstrate that the model successfully identifies incorrect observations and recovers latent enumerator and survey data quality. I further demonstrate the model's utility by applying it to reinterview data from a large survey fielded in Malawi, using it to identify significant variation in data quality across observations generated by different enumerators. [ABSTRACT FROM AUTHOR]

SUMMARY: Despite their increasing popularity, n -of-1 designs employ data analyses that might not be as complete and powerful as they could be. Borrowing from existing advances in educational and psychological research, this article presents a few techniques and references for rigorous data analytic techniques in n -of-1 research. [ABSTRACT FROM AUTHOR]

Estimating correlations among demographic parameters is an important method in population ecology. A recent paper by Deane et al. (Ecology and Evolution 13:e9847, 2023) attempted to explore the effects of different priors for covariance matrices on inference when using mark‐recovery data. Unfortunately, Deane et al. (2023) made a mistake when parameterizing some of their models. Rather than exploring the effects of different priors, they examined the effects of the use of incorrect equations on inference. In this manuscript, we clearly describe the mistake in Deane et al. (2023). We then demonstrate the use of an alternative and appropriate method and reach different conclusions regarding the effects of priors on inference. Consistent with other recent literature, informative inverse Wishart priors can lead to flawed inference, while vague priors on covariance matrix components have little impact when sample sizes are adequate. [ABSTRACT FROM AUTHOR]

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In near coastal environments, nautical charts provide crucial information for navigation and routing both in real-time operations and during planning stages. The cost of data collection as well as capacity constraints in the processing pipeline make reliable bathymetric information in such areas sparse. Prioritization rules can help guide the efforts to where information is the most valuable. AIS data provide accounts of real ship movements, indicating both desirable paths and minimum depths. We propose a statistical model for combining sparse bathymetric soundings with AIS observations for improved prediction of depths for generation of feasible transportation corridors. The method relies on viewing AIS draughts as censored observations of the true depth. A case-study is performed for the southern archipelago of Gothenburg using the program R-INLA. The non-stationarity caused by having boundaries with known (zero) depth and holes (land) in the domain is handled through discretization. Varying amounts of AIS data, ranging from none to 1824 observations, are used in the experiments. Results show predicted depths within the range of data values, and that inclusion of AIS data serve to push the field down to ensure that traverseable areas are predicted as such revealing corridors in narrow passages where bathymetric soundings are lacking. [ABSTRACT FROM AUTHOR]

This study used the panel data from 15 emerging markets to examine the impact of restrictive macroprudential policies on income inequality from 2000–2019 using Bayesian panel vector autoregression and Bayesian panel dynamics generalised method of moments models. The chosen models are suitable for addressing multiple entity dynamics, accommodating a wide range of variables, handling dense parameterisation, and optimising formativeness and heterogeneous individual-specific factors. The empirical analysis utilised various macroprudential policy proxies and income inequality measures. The results show that when the central banks tighten systems using macroprudential policy instruments to sticker debt-to-income and financial instruments for lower-income borrowers (the bottom 40% of the income distribution), they promote income inequality in these countries while reducing income inequality for high-income borrowers (the high 1 percent of the income distribution). The impact of loan-to-value ratios was found to be insignificant in these countries. Fiscal policy through government expenditure and economic development reduces income inequality, while money supply and oil-price shocks exacerbate it. The study suggests implementing a progressive debt-to-income (DTI) ratio system in emerging markets to address income inequality among lower-income borrowers. This would adjust DTI thresholds based on income brackets, allowing lenient credit access for lower-income borrowers while maintaining stricter limits for higher-income borrowers. This would improve financial stability and reduce income disparities. Additionally, targeted financial literacy programs and a petroleum-linked basic income program could be implemented to distribute oil revenue to lower-income households. A monetary supply stabilisation fund could also be established to maintain financial stability and prevent excessive inflation. [ABSTRACT FROM AUTHOR]

Warming water temperatures as a result of climate change pose a major threat to coldwater organisms. However, the rate of warming is not spatially uniform due to surface‐ground‐water interactions and stream and watershed characteristics. Coldwater habitats that are most resistant to warming serve as thermal refugia and identifying their locations is critical to regional aquatic conservation planning. We quantified the thermal sensitivity of 203 streams providing current and potential habitat for brook trout (Salvelinus fontinalis) across nearly 1000 linear km of their native range in the southern and central Appalachian Mountains region, USA, and characterized their spatial variability with landscape variables available in the National Hydrography Dataset. Using the Bayesian framework, we calculated the maximum slope of the logistic function relating paired weekly mean air temperature and stream temperature as an index of stream thermal sensitivity. Streams differed greatly in thermal sensitivity and those with more resistant water temperature regimes (i.e., thermal refugia) were consistently characterized by southerly latitudes and groundwater input. Landscape variables derived from a principal component analysis explained 16% of the variation in thermal sensitivity, indicating that the existing landscape variables were modestly successful in explaining spatial thermal heterogeneity. Using our model and spatial interpolation, we predicted thermal sensitivity at 8695 stream segments potentially suitable for brook trout in the study region. Thermal refugia were more common southward presumably due to higher elevations, but elsewhere they were also clustered at finer spatial scales. Our analysis informs prioritizing habitat conservation and restoration of this native salmonid and other aquatic organisms that depend on coldwater habitats in a warming world. [ABSTRACT FROM AUTHOR]

Occupancy models are commonly used in statistical ecology to model binary detection/non-detection data. These hierarchical models make a distinction between detection/non-detection and presence/absence by treating true occupancy as a latent process. In this paper, we propose a multi-species, multi-season occupancy model to jointly model detection/non-detection data on multiple species. Existing literature has shown that models that account for various sources of dependence in the latent occupancy process improve estimation, especially in the single-survey setting. However, the detection process in the model has not received much attention, even though detectability of a species is expected to relate to the detectability of other species and to detectability in previous time periods. In this work, we propose a model to capture this phenomenon by incorporating a multivariate temporal random effect in the detection process. We perform a simulation study to show that the proposed model yields more accurate inference than models that only use covariates to quantify detection. We apply our model to detection/non-detection data on three species—Thomson's gazelle, zebra, and wildebeest—in Serengeti National Park of Tanzania, Africa. [ABSTRACT FROM AUTHOR]

The pelagic thresher shark (Alopias pelagicus) is considered overexploited; however, information about its biology in the Eastern Pacific is scarce, particularly age estimation and growth modeling. Age was estimated by counting growth bands in the cervical vertebrae. Specimens, ranging in size from 85.00 to 174.90 cm precaudal length (PCL), were collected by pelagic longliners in 2020 and 2021. Three different growth models (von Bertalanffy, Gompertz, and logistic) were applied to the length-at-age data. Bayesian methods using the Markov chain Monte Carlo algorithm were employed for parameter estimation. Based on the Watanabe–Akaike information criterion for model selection, the von Bertalanffy was selected as the best model for both sexes combined, with the estimated parameters being PCL0 = 86.20 cm (95% credible interval (CI95%), 83.03‒89.32), PCL∞ = 180.66 cm (CI95%, 172.96‒189.81), and k = 0.09 year−1 (CI95%, 0.07–0.10). These results suggest that females and males have the same growth pattern. Based on known size at maturity, age at maturity for the pelagic thresher shark was estimated at 7 years for males and 9 for females, with a maximum lifespan of 22 to 38 years. This study provides crucial life history information that can contribute to managing and conserving this endangered species. [ABSTRACT FROM AUTHOR]

This paper extends the Bayesian semiparametric stochastic volatility (SV‐DPM) model. Instead of using a Dirichlet process mixture (DPM) to model return innovations, we use an infinite hidden Markov model (IHMM). This allows for time variation in the return density beyond that attributed to parametric latent volatility. The new model nests several special cases as well as the SV‐DPM. We also discuss posterior and predictive density simulation methods for the model. Applied to equity returns, foreign exchange rates, oil price growth and industrial production growth, the new model improves density forecasts, compared with the SV‐DPM, a stochastic volatility with Student's t innovations and other fat‐tailed volatility models. [ABSTRACT FROM AUTHOR]

Fluorescence lifetime imaging microscopy (FLIM) has become a standard tool in the quantitative characterization of subcellular environments. However, quantitative FLIM analyses face several challenges. First, spatial correlations between pixels are often ignored as signal from individual pixels is analyzed independently thereby limiting spatial resolution. Second, existing methods deduce photon ratios instead of absolute lifetime maps. Next, the number of fluorophore species contributing to the signal is unknown, while excited state lifetimes with

The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which cannot be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.

ABSTRACT Ondansetron is an anti‐emetic 5‐HT3 receptor antagonist being investigated for treating neonatal opioid withdrawal syndrome (NOWS). Sparse PK data were analyzed from a multicenter, double‐blind clinical trial with 98 mother/neonate dyads. Pregnant women with opioid use disorder were randomized to receive either placebo or ondansetron 8 mg intravenously within 4 h of delivery. Neonates born to mothers who were randomized to ondansetron received 0.07 mg/kg orally once every 24 h for up to five doses. Using current PK data, model parameters from a two‐compartmental structural model from the literature (i.e., a priori model) were updated with the Metropolis‐Hastings Markov‐chain Monte Carlo estimation algorithm in NONMEM. The updated Bayesian model indicated a slower absorption rate (KA) but no differences in model parameters (CL, V, V2, Q) after including body weight and postmenstrual age. Sensitivity analyses on CL prior revealed statistical improvement favoring larger body weights, but not changes in postmenstrual age. However, further model development using larger body weights did not illustrate superior performance through visual inspection of diagnostic plots. Overall, a cumulative AUC of at least 1000 ng*h/mL appears to be the threshold for reductions in symptom severity. Exposure‐response analyses suggest the total number of doses to be the primary driver for efficacy with respect to AUC, which reasonably aligns with the literature. Overall, it is suggested that at least three doses of the current oral ondansetron regimen are required to reduce symptom severity in neonates.

This article details a correction to: Lin, W., & Dolan, R. J. (2024). Decision-Making, Pro-variance Biases and Mood-Related Traits. Computational Psychiatry, 8(1), 142–158. https://doi.org/10.5334/cpsy.114.

IntroductionPrecise understanding of proliferative activity in breast cancer holds significant value in the monitoring of neoadjuvant treatment, while current immunostaining of Ki-67 from biopsy or resected tumour suffers from partial sampling error. Multi-compartment model of transverse relaxation time has been proposed to differentiate intra- and extra-cellular space and biochemical environment but susceptible to noise, with recent development of Bayesian algorithm suggested to improve robustness. We hence hypothesise that intra- and extra-cellular transverse relaxation times using Bayesian algorithm might be sensitive to proliferative activity.Materials and methodsTwenty whole tumour specimens freshly excised from patients with invasive ductal carcinoma were scanned on a 3 T clinical scanner. The overall transverse relaxation time was computed using a single-compartment model with the non-linear least squares algorithm, while intra- and extra-cellular transverse relaxation times were computed using a multi-compartment model with the Bayesian algorithm. Immunostaining of Ki-67 was conducted, yielding 9 and 11 cases with high and low proliferating activities respectively.ResultsFor single-compartment model, there was a significant higher overall transverse relaxation time (p = 0.031) in high (83.55 ± 7.38 ms) against low (73.30 ± 11.30 ms) proliferating tumours. For multi-compartment model, there was a significant higher intra-cellular transverse relaxation time (p = 0.047) in high (73.52 ± 10.92 ms) against low (61.30 ± 14.01 ms) proliferating tumours. There was no significant difference in extra-cellular transverse relaxation time (p = 0.203) between high and low proliferating tumours.ConclusionsOverall and Bayesian intra-cellular transverse relaxation times are associated with proliferative activities in breast tumours, potentially serving as a non-invasive imaging marker for neoadjuvant treatment monitoring.

Mechanistically based non-animal methods for assessing skin sensitization hazard have been developed, but are not considered sufficient, individually, to conclusively define the skin sensitization potential or potency of a chemical. This resulted in the development of defined approaches (DAs), as documented in OECD TG 497, for combining information sources in a prescriptive manner to provide a determination of risk or potency. However, there are currently no DAs within OECD TG 497 that can derive a point of departure (POD) for risk assessment. The Skin Allergy Risk Assessment – Integrated Chemical Environment (SARA-ICE) DA for skin sensitization is a Bayesian statistical model that estimates a human-relevant metric of sensitizer potency, the ED01, an estimate of the human predictive patch test dermal dose at which there is 1% chance of inducing sensitization, which can be used in a risk assessment paradigm. The model accounts for variability of input data and explicitly quantifies uncertainty. SARA-ICE derives the ED01 from a variety of in vitro and in vivo test method data and is built upon historical human, murine, and in vitro test data for 434 chemicals. In addition to the ED01 POD SARA-ICE DA also provides a Globally Harmonized System of Classification and Labelling of Chemicals (GHS) classification probability for GHS subcategories 1A, 1B and not classified (NC). Here we describe the SARA-ICE model and its evaluation, including performance versus benchmark PODs. In addition, via a case study with isothiazolinones (ITs), we demonstrate the utility of SARA-ICE for integrating different data inputs and compare the ED01 for six ITs to existing historical data.

Background: Although financial globalisation has been a widely debated topic recently, there remains a wide range of conflicting views on the matter. Additionally, there is a lack of empirical research on this subject in middle-income countries, particularly those examining the differential impacts of financial globalisation on economic growth between upper-middle-income and lower-middle-income countries. Objective: This study investigates the impact of financial globalisation on economic growth in middle-income countries in Asia from 2002-2021. It specifically examines the differential effects of financial globalisation between upper-middle-income and lower-middle-income countries. Methodology: The study employs the Bayesian approach to estimate the impact of financial globalisation on economic growth, offering insights into both the extent and probability of this relationship. Additionally, the Generalised Method of Moments (GMM) is utilised to validate the robustness of the findings. Result: The findings indicate a significant and positive impact of financial globalisation on economic growth across middle-income countries in Asia, with a probability of 100 percent. Importantly, the impact is more pronounced in lower-middle-income countries than their upper-middle-income counterparts. Conclusion: These findings suggest that middle-income countries in Asia can effectively leverage financial globalisation to foster economic growth. Unique Contribution: This study significantly contributes to theory and practice by clarifying the impact of financial globalisation on economic growth. Furthermore, it sheds light on the differences in this impact between lower-middle-income and upper-middle-income countries. Key Recommendation: This study's findings provide a reliable basis for the countries in the sample to identify appropriate solutions to promote financial globalisation linked with economic growth. Accordingly, middle-income countries in Asia should make further efforts to improve their level of financial globalisation, which is particularly crucial for lower-middle-income countries.

Response propensity (RP) models are widely used in survey research to analyse response processes. One application is to predict sample members who are likely to be survey nonrespondents. The potential nonrespondents can then be targeted using responsive and adaptive strategies with the aim of increasing response rates and reducing survey costs. Generally, however, RP models exhibit low predictive power, which limits their effective application in survey research to improve data collection. This paper explores whether the use of a Bayesian framework can improve the predictions of response propensity models in longitudinal data. In the Bayesian approach existing knowledge regarding model parameters is used to specify prior distributions. In this paper we apply this approach and analyse data from the UK household longitudinal study, Understanding Society (first five waves) and estimate informative priors from previous waves data. We use estimates from RP models fitted to response outcomes from earlier waves as our source for specifying prior distributions. Our findings indicate that conditioning on previous wave data leads to negligible improvement of the response propensity models’ predictive power and discriminative ability.

Abstract: Introduction: The validity of measurement, which refers to how accurately tools measure what they are intended to measure, is essential in science. Researchers rely on statistical approaches to test the validity of their measures. One such approach is correlation analysis. Even though correlation analysis can capture high nonsystematic errors between measures, it can often lead to misleading conclusions when observations are measured with systematic errors. Methods: We used Monte Carlo simulations with 10,000 iterations to generate the data in each simulation. Results: We demonstrate how correlation analysis is commonly used to test for validity and how this method can fail with systematic error. We further propose an alternative to correlation analysis – the Bayesian one-sample t-test – for cases where using a simple statistical test can be justified. We provide additional simulations as well as an application to real data, showcasing the implementation of the Bayesian one-sample t-test and how to use it to address the limitations of correlation analysis. Discussion: We suggest using the Bayesian one-sample t-test to identify both systematic and nonsystematic error and moreover to provide evidence for the null hypothesis of no differences between two measures. Conclusion: As a test of validity, the Bayesian one-sample t-test supersedes correlation analysis.

Accurate characterization of carbonate reservoirs remains a significant challenge due to complex facies variations and the substantial effects of wave propagation. We propose a facies-constrained reflectivity inversion strategy. The method establishes a relationship between logging data and seismic waveforms, applies clustering analysis using the Self-Organizing Map (SOM) technique, and utilizes the clustering results to constrain the construction of an initial model with realistic lateral variations. Based on this initial model, a Bayesian-based reflectivity inversion is performed, incorporating a modified Cauchy prior distribution to enhance inversion accuracy and stability. The reflectivity method offers a one-dimensional analytical solution to the wave equation, tacking thin layer thicknesses and wave propagation effects into consideration, thereby significantly alleviating inversion problems encountered in marl reservoirs. Compared to traditional inversion methods based on the Zoeppritz equation, the facies-constrained reflectivity inversion delivers higher accuracy and resolution. The application of this technique to identify marl reservoirs in the Lei32 sub-member of the Sichuan Basin has yielded promising results, effectively delineating favorable reservoir areas of approximately 210 km2 and offering strong support for future exploration and development.

IntroductionStillbirth remains a major public health problem in developing countries due to low health coverage and services. Approximately two million stillbirths occur every year; in particular, stillbirths are highly prevalent in low- and middle-income countries such as Ethiopia. However, there is scarce information available in Ethiopia regarding the determinants of stillbirth.MethodsA facility-based, unmatched case-control study was conducted at Bishoftu General Hospital from April 1 to April 30, 2023. A systematic random sampling technique was used to select samples of the study subjects. The collected data were entered into Epi-info version 7.2. A Bayesian logistic regression model was used in this study, with a 95% Credible interval to determine the association between independent variables and stillbirth. All analyses were performed using STATA version 17 software.ResultsA total of 403 (81 cases and 322 controls) participants were included in the study. The average age of participants was 26 years (SD: ±5.03). According to the adjusted model, mothers who attained a middle school [adjusted odds ratio [AOR] = 0.23; 95% credible interval [CrI]: 0.11, 0.43], diploma and above (AOR = 0.25; 95% CrI: 0.12, 0.46), rural residence (AOR = 2.55; 95% CrI: 1.11, 4.99), married women (AOR = 0.46; 95% CrI: 0.20, 0.93), ANC visits more than 4 (AOR = 0.35; 95% CrI: 0.17, 0.63), history of prior stillbirth (AOR = 8.71; 95% CI: 3.22, 17.69), previous history of abortion (AOR = 3.29; 95% CI: 1.13, 7.666), duration of labor more than 24 h (AOR = 3.71; 95% CI: 1.28, 7.83); normal birth weight (AOR = 0.39; 95% CI: 0.15, 0.57) were significantly associated with stillbirth.ConclusionsThis study revealed that maternal education, rural residence, marital status, history of prior stillbirth, ANC visit, previous history of abortion, duration of labor, and birth weight were identified as determinants of stillbirth. Pregnant mothers should be identified early and given appropriate care, including comprehensive prenatal care and other maternal services.

Modeling of environmental phenomena is usually confounded by the influence of multiple factors existing at different time and spatial scales. Bayesian modeling is presumed to be the best approach for modeling such complex systems. Using a Bayesian hierarchical inferential framework, we employed a multi-source data approach (i.e. remote sensing derived anthropogenic, climatic and topographic set of variables) to model Carbon (C) stock in a managed plantation forest ecosystem in Zimbabwe’s Eastern Highlands. We therefore investigated how two related multi-data sources of new generation remote sensing derived ancillary information influence C stock prediction required for building sustainable capacity in C monitoring and reporting. Two mainstream models constructed from Landsat-8 and Sentinel-2 derived vegetation indices coupled with climatic and topographic covariates were used to predict C stocks using forest inventory data collected using spatial coverage sampling. A multi-source data driven approach to C stock prediction yielded slightly lower predictions for both the Landsat-8 ([Formula: see text] and the Sentinel-2 ([Formula: see text]-based C stock models than C stock predictions published in related studies. Distance to settlements ([Formula: see text]) and [Formula: see text] are significant predictors of C stock with the Sentinel-2-based C stock model outperforming its Landsat-8 model variant in terms of prediction accuracy. The Sentinel-2-based C stock model resulted in a 1.17 MgCha−1 Root Mean Square Error (RMSE) with a ([Formula: see text] 95% credible interval whilst the Landsat-8-based C stock counterpart gave a 2.16 MgCha−1 RMSE with a ([Formula: see text] associated 95% credible interval. Despite a multi-source data prediction approach to the modeling of C stock in a managed plantation forest ecosystem set-up, the issues of scale still play a major role in modeling spatial variability of natural resource variables. Both climatic and topographic derived ancillary data are not significant predictors of C stock under the present modeling conditions. Accurate and precise accounting of C stock for climate change mitigation and action can best be done at landscape scales rather than local scale as the scale of variation for climate-change-related variables vary at larger spatial scales than the ones utilized in the present study.

The critical nitrogen (Nc) dilution curve is considered an effective method for crop nitrogen (N) diagnosis, fertilization decision-making, and crop modeling. The Nc dilution curve is typically described using a power function curve (Nc = A1W−A2). This study established a universal Nc dilution curve and evaluated its potential application in flue-cured tobacco (Nicotiana tabacum L.) N diagnosis, yield, and quality regulation. Field experiments were conducted involving three tobacco varieties (Honghuadajinyuan, Yunyan87, and K326) and four N application rates (0 to > 90 kg N ha−1). The study was conducted in Southwest China from 2021 to 2022. The universal Nc curve obtained across all experimental data was 3.45 W−0.39. The parameter A2 was more susceptible to genotype and environment interactions than parameter A1. The uncertainty first rapidly decreased and then remained at a lower value as biomass increased. The relationship between leaf biomass and days after transplantation and accumulated growth degree was robust. The N nutrition index (NNI) and accumulated N deficiency effectively determined leaf N status and N demand, respectively. There was an inverse correlation between the NNI, leaf yield, and between the superior and medium leaf ratios. The quantitative relationship can be used to adjust the synergistic production of tobacco yield and leaf quality.

As one of the Emerging and Growth-Leading Economies (EAGLES), Vietnam is maintaining rapid economic development at the cost of environmental degradation. However, the impact of economic growth and the role of fiscal policy tools in Vietnam’s pollution equation remains unclear. This study uses a Bayesian approach and threshold estimation to quantitatively assess the relationship between economic growth, fiscal policy tools, and environmental degradation in Vietnam from 1990 to 2021. The results indicate that tools of the fiscal policy significantly contribute to environmental degradation, with government expenditure having a greater impact than taxation. Economic growth exhibits a U-shaped relationship with degradation, implying the existence of an inverted Environmental Kuznets Curve (EKC) hypothesis in a transitional economy. In the early stages of Vietnam’s development, characterized by an agricultural economy, economic growth positively affected the environment. However, economic growth exacerbates environmental degradation in the pre-industrialization and industrialization phases. Diagnostic tests are also applied to confirm the reliability and validity of the empirical estimates, providing valuable insights for proposed policy implications in the study.

We investigate the role of hybrid and nucleonic equations of state (EOSs) within neutron star (NS) interiors using Bayesian inference to evaluate their alignment with recent observational data from NICER and LIGO-Virgo (LV) collaborations. We find that smooth hybrid EOSs are slightly favoured in explaining NS mass-radius relations, particularly for pulsars such as PSR J0030+0451 and PSR J0740+6620. However, this preference is not definitive, as gravitational wave (GW) data does not significantly differentiate between our hybrid and nucleonic models. Our analysis also reveals tensions between older NICER data and recent measurements for PSR J0437−4715, highlighting the need for more flexible EOS models. Through two sampling approaches — one fixing the hadronic EOS set and the other without fixing the same, we demonstrate that the hybrid EOS model can incorporate stiffer EOSs, resulting in a better agreement with NICER data but leading to higher tidal deformability, which is less consistent with GW observations. In some recent publications a parameter dc, related to the trace anomaly and its derivative, is used to indicate the presence of deconfined quark matter. We find that our hadronic model, which does not include phase transition to deconfined matter, under the influence of imposed constraints, is able to predict values below 0.2 for dc at around five times saturation density. The hybrid model goes below this threshold at lower densities under the same conditions.

Highlights: We analyzed the Foreign Accent Syndrome (FAS) in a 44-year-old woman. We discuss the effect of auditory exclusion on fFAS. The abolition of the auditory feedback has left the aberrant internal prior unconstrained cause a worsening. [ABSTRACT FROM AUTHOR]

Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power. We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84-197 subjects in each study with 562-888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as prior information into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels. Using classic statistics and Benjamini-Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients. Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used.

Abstract High childhood disease prevalence and under-five mortality rates have been consistently reported in Nigeria. Vaccination is a cost-effective preventive strategy against childhood diseases. Therefore, this study aimed to identify the determinants of Incomplete Vaccination (IV) among children aged 12–23 months in Nigeria. This cross-sectional design study utilized the 2018 Nigeria Demographic and Health Survey (NDHS) dataset. A two-stage cluster sampling technique was used to select women of reproductive age who have children (n = 5475) aged 12–23 months. The outcome variable was IV of children against childhood diseases. Data were analyzed using Integrated Nested Laplace Approximation and Bayesian binary regression models (α0.05). Visualization of incomplete vaccination was produced using the ArcGIS software. Children’s mean age was 15.1 ± 3.2 months and the median number of vaccines received was four. Northern regions contributed largely to the IV. The likelihood of IV was lower among women aged 25–34 years (aOR = 0.67, 95% CI = 0.54–0.82, p

Abstract We have introduced the R package jmBIG to facilitate the analysis of large healthcare datasets and the development of predictive models. This package provides a comprehensive set of tools and functions specifically designed for the joint modelling of longitudinal and survival data in the context of big data analytics. The jmBIG package offers efficient and scalable implementations of joint modelling algorithms, allowing for integrating large-scale healthcare datasets. By utilizing the capabilities of jmBIG, researchers and analysts can effectively handle the challenges associated with big healthcare data, such as high dimensionality and complex relationships between multiple outcomes. With the support of jmBIG, analysts can seamlessly fit Bayesian joint models, generate predictions, and evaluate the performance of the models. The package incorporates cutting-edge methodologies and harnesses the computational capabilities of parallel computing to accelerate the analysis of large-scale healthcare datasets significantly. In summary, jmBIG empowers researchers to gain deeper insights into disease progression and treatment response, fostering evidence-based decision-making and paving the way for personalized healthcare interventions that can positively impact patient outcomes on a larger scale.

Abstract Background Despite the successful efforts in controlling malaria in Vietnam, the disease remains a significant health concern, particularly in Central Vietnam. This study aimed to assess correlations between environmental, climatic, and socio-economic factors in the district with malaria cases. Methods The study was conducted in 15 provinces in Central Vietnam from January 2018 to December 2022. Monthly malaria cases were obtained from the Institute of Malariology, Parasitology, and Entomology Quy Nhon, Vietnam. Environmental, climatic, and socio-economic data were retrieved using a Google Earth Engine script. A multivariable Zero-inflated Poisson regression was undertaken using a Bayesian framework with spatial and spatiotemporal random effects with a conditional autoregressive prior structure. The posterior random effects were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling. Results There was a total of 5,985 Plasmodium falciparum and 2,623 Plasmodium vivax cases during the study period. Plasmodium falciparum risk increased by five times (95% credible interval [CrI] 4.37, 6.74) for each 1-unit increase of normalized difference vegetation index (NDVI) without lag and by 8% (95% CrI 7%, 9%) for every 1ºC increase in maximum temperature (TMAX) at a 6-month lag. While a decrease in risk of 1% (95% CrI 0%, 1%) for a 1 mm increase in precipitation with a 6-month lag was observed. A 1-unit increase in NDVI at a 1-month lag was associated with a four-fold increase (95% CrI 2.95, 4.90) in risk of P. vivax. In addition, the risk increased by 6% (95% CrI 5%, 7%) and 3% (95% CrI 1%, 5%) for each 1ºC increase in land surface temperature during daytime with a 6-month lag and TMAX at a 4-month lag, respectively. Spatial analysis showed a higher mean malaria risk of both species in the Central Highlands and southeast parts of Central Vietnam and a lower risk in the northern and north-western areas. Conclusion Identification of environmental, climatic, and socio-economic risk factors and spatial malaria clusters are crucial for designing adaptive strategies to maximize the impact of limited public health resources toward eliminating malaria in Vietnam.

In this paper, we introduce a new two-parameter lifetime distribution based on the type II Topp-Leone-G family and the inverse length biased exponential distribution which is called the type II Topp-Leone inverse length biased exponential (TIITL-ILBE) distribution. Various statistical properties of the proposed distribution, including the ordinary moments, moment generating function, incomplete moments, conditional moments, vitality and mean residual life functions, moments of the residual and reversed residual lifetime functions and Rényi entropy are derived. The method of maximum likelihood for estimation and some simulation results for the model parameters of the TIITL-ILBE distribution under complete samples is investigated. Also, The methods of maximum likelihood and Bayesian for estimation and some simulation results for the model parameters of the TIITL-ILBE distribution under joint censored samples is investigated. The real life data set results utilizing that the proposed distribution exhibits greater flexibility and evaluated empirically under complete and joint censored data.

Abstract Hierarchical models are common for ecological analysis, but determining appropriate model selection methods remains an ongoing challenge. To confront this challenge, a suitable method is needed to evaluate and compare available candidate models. We compared performance of conditional WAIC, a joint-likelihood approach to WAIC (WAICj), and posterior-predictive loss for selecting between candidate N-mixture models. We tested these model selection criteria on simulated single-season N-mixture models, simulated multi-season N-mixture models with temporal auto-correlation, and three case studies of single-season N-mixture models based on eBird data. WAICj proved more accurate than the standard conditional formulation or posterior-predictive loss, even when models were temporally correlated, suggesting WAICj is a robust alternative to model selection for N-mixture models.

Abstract The Platform trial In COVID-19 priming and BOOsting (PICOBOO) is a multi-site, adaptive platform trial designed to generate evidence of the immunogenicity, reactogenicity, and cross-protection of different booster vaccination strategies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants, specific for the Australian context. The PICOBOO trial randomises participants to receive one of three COVID-19 booster vaccine brands (Pfizer, Moderna, Novavax) available for use in Australia, where the vaccine brand subtypes vary over time according to the national vaccine roll out strategy, and employs a Bayesian hierarchical modelling approach to efficiently borrow information across consecutive booster doses, age groups and vaccine brand subtypes. Here, we briefly describe the PICOBOO trial structure and report the statistical considerations for the estimands, statistical models and decision making for trial adaptations. This paper should be read in conjunction with the PICOBOO Core Protocol and PICOBOO Sub-Study Protocol 1: Booster Vaccination. PICOBOO was registered on 10 February 2022 with the Australian and New Zealand Clinical Trials Registry ACTRN12622000238774.

Abstract Background New therapeutics in oncology have presented challenges to existing paradigms and trial designs in all phases of drug development. As a motivating example, we considered an ongoing phase II trial planned to evaluate the combination of a MET inhibitor and an anti-PD-L1 immunotherapy to treat advanced oesogastric carcinoma. The objective of the paper was to exemplify the planning of an adaptive phase II trial with novel anti-cancer agents, including prolonged observation windows and joint sequential evaluation of efficacy and toxicity. Methods We considered various candidate designs and computed decision rules assuming correlations between efficacy and toxicity. Simulations were conducted to evaluate the operating characteristics of all designs. Results Design approaches allowing continuous accrual, such as the time-to-event Bayesian Optimal Phase II design (TOP), showed good operating characteristics while ensuring a reduced trial duration. All designs were sensitive to the specification of the correlation between efficacy and toxicity during planning, but TOP can take that correlation into account more easily. Conclusions While specifying design working hypotheses requires caution, Bayesian approaches such as the TOP design had desirable operating characteristics and allowed incorporating concomittant information, such as toxicity data from concomitant observations in another relevant patient population (e.g., defined by mutational status).

Abstract Numerous modelling techniques exist to estimate abundance of plant and animal populations. The most accurate methods account for multiple complexities found in ecological data, such as observational biases, spatial autocorrelation, and species correlations. There is, however, a lack of user‐friendly and computationally efficient software to implement the various models, particularly for large data sets. We developed the spAbundance R package for fitting spatially explicit Bayesian single‐species and multi‐species hierarchical distance sampling models, N‐mixture models, and generalized linear mixed models. The models within the package can account for spatial autocorrelation using Nearest Neighbour Gaussian Processes and accommodate species correlations in multi‐species models using a latent factor approach, which enables model fitting for data sets with large numbers of sites and/or species. We provide three vignettes and three case studies that highlight spAbundance functionality. We used spatially explicit multi‐species distance sampling models to estimate density of 16 bird species in Florida, USA, an N‐mixture model to estimate black‐throated blue warbler (Setophaga caerulescens) abundance in New Hampshire, USA, and a spatial linear mixed model to estimate forest above‐ground biomass across the continental USA. spAbundance provides a user‐friendly, formula‐based interface to fit a variety of univariate and multivariate spatially explicit abundance models. The package serves as a useful tool for ecologists and conservation practitioners to generate improved inference and predictions on the spatial drivers of abundance in populations and communities.

Abstract Background Although immune checkpoint inhibitors (ICIs) have brought survival benefits to non-small cell lung cancer (NSCLC), disease progression still occurs, and there is no consensus on the treatment options for these patients. We designed a network meta-analysis (NMA) to evaluate systemic treatment options for NSCLC after failure of ICIs. Methods PubMed, Embase, Web of Science and Cochrane Library databases were searched, then literature screening was followed by NMA. We included all Phase II and III randomized controlled trials (RCTs). Progression-free survival (PFS) and overall survival (OS) used hazard ratio (HR) for evaluation. Objective response rate (ORR) and adverse events (AEs) used odds ratio (OR) and relative risk (RR) effect sizes, respectively. R software was applied to compare the Bayesian NMA results. Results We finally included 6 studies. 1322 patients received ICI plus Chemotherapy (ICI + Chemo), ICI plus Anti-angiogenic monoclonal antibody (ICI + Antiangio-Ab), ICI plus Tyrosine kinase inhibitor (ICI + TKI), Tyrosine kinase inhibitor plus Chemotherapy (TKI + Chemo), Standard of Care (SOC), Chemotherapy (Chemo). TKI + Chemo is associated with longer PFS, higher ORR (surface under cumulative ranking curve [SUCRA], 99.7%, 88.2%), ICI + TKI achieved the longest OS (SUCRA, 82.7%). ICI + Antiangio-Ab was granted the highest safety rating for adverse events (AEs) of any grade, AEs greater than or equal to grade 3 and AEs of any grade leading to discontinuation of treatment (SUCRA, 95%, 82%, 93%). Conclusions For NSCLC after failure of ICIs, TKI + Chemo was associated with longer PFS and higher ORR, while ICI + TKI was associated with the longest OS. In terms of safety, ICI + Antiangio-Ab was the highest.

Abstract Maternal mortality ratio (MMR) estimates have been studied over time for understanding its variation across the country. However, it is never sufficient without accounting for presence of variability across in terms of space, time, maternal and system level factors. The study endeavours to estimate and quantify the effect of exposures encompassing all maternal health indicators and system level indicators along with space–time effects influencing MMR in India. Using the most recent level of possible -factors of MMR, maternal health indicators from the National Family Health Survey (NFHS: 2019–21) and system level indicators from government reports a heatmap compared the relative performance of all 19 SRS states. Facet plots with a regression line was utilised for studying patterns of MMR for different states in one frame. Using Bayesian Spatio-temporal random effects, evidence for different MMR patterns and quantification of spatial risks among individual states was produced using estimates of MMR from SRS reports (2014–2020). India has witnessed a decline in MMR, and for the majority of the states, this drop is linear. Few states exhibit cyclical trend such as increasing trends for Haryana and West Bengal which was evident from the two analytical models i.e., facet plots and Bayesian spatio- temporal model. Period of major transition in MMR levels which was common to all states is identified as 2009–2013. Bihar and Assam have estimated posterior probabilities for spatial risk that are relatively greater than other SRS states and are classified as hot spots. More than the individual level factors, health system factors account for a greater reduction in MMR. For more robust findings district level reliable estimates are required. As evident from our study the two most strong health system influencers for reducing MMR in India are Institutional delivery and Skilled birth attendance.

In the current paper, we review existing tools for solving variable selection problems in psychology. Modern regularization methods such as lasso regression have recently been introduced in the field and are incorporated into popular methodologies, such as network analysis. However, several recognized limitations of lasso regularization may limit its suitability for psychological research. In this paper, we compare the properties of lasso approaches used for variable selection to Bayesian variable selection approaches. In particular we highlight advantages of stochastic search variable selection (SSVS), that make it well suited for variable selection applications in psychology. We demonstrate these advantages and contrast SSVS with lasso type penalization in an application to predict depression symptoms in a large sample and an accompanying simulation study. We investigate the effects of sample size, effect size, and patterns of correlation among predictors on rates of correct and false inclusion and bias in the estimates. SSVS as investigated here is reasonably computationally efficient and powerful to detect moderate effects in small sample sizes (or small effects in moderate sample sizes), while protecting against false inclusion and without over-penalizing true effects. We recommend SSVS as a flexible framework that is well-suited for the field, discuss limitations, and suggest directions for future development.