Posterior Odds Ratio Research Articles

High Positive End-expiratory Pressure (PEEP) with Recruitment Maneuvers versus Low PEEP during General Anesthesia for Surgery: A Bayesian Individual Patient Data Meta-analysis of Three Randomized Clinical Trials.

The influence of high positive end-expiratory pressure (PEEP) with recruitment maneuvers on the occurrence of postoperative pulmonary complications after surgery is still not definitively established. Bayesian analysis can help to gain further insights from the available data and provide a probabilistic framework that is easier to interpret. The objective was to estimate the posterior probability that the use of high PEEP with recruitment maneuvers is associated with reduced postoperative pulmonary complications in patients with intermediate-to-high risk under neutral, pessimistic, and optimistic expectations regarding the treatment effect. Multilevel Bayesian logistic regression analysis was performed on individual patient data from three randomized clinical trials carried out on surgical patients at intermediate to high risk for postoperative pulmonary complications. The main outcome was the occurrence of postoperative pulmonary complications in the early postoperative period. This study examined the effect of high PEEP with recruitment maneuvers versus low PEEP ventilation. Priors were chosen to reflect neutral, pessimistic, and optimistic expectations of the treatment effect. Using a neutral, pessimistic, or optimistic prior, the posterior mean odds ratio for high PEEP with recruitment maneuvers compared to low PEEP was 0.85 (95% credible interval, 0.71 to 1.02), 0.87 (0.72 to 1.04), and 0.86 (0.71 to 1.02), respectively. Regardless of prior beliefs, the posterior probability of experiencing a beneficial effect exceeded 90%. Subgroup analysis indicated a more pronounced effect in patients who underwent laparoscopy (odds ratio, 0.67 [0.50 to 0.87]) and those at high risk for postoperative pulmonary complications (odds ratio, 0.80 [0.53 to 1.13]). Sensitivity analysis, considering severe postoperative pulmonary complications only or applying a different heterogeneity prior, yielded consistent results. High PEEP with recruitment maneuvers demonstrated a moderate reduction in the probability of postoperative pulmonary complication occurrence, with a high posterior probability of benefit observed consistently across various prior beliefs, particularly among patients who underwent laparoscopy.

Trimodal skin health programme for childhood impetigo control in remote Western Australia (SToP): a cluster randomised, stepped-wedge trial.

Skin infections affect physical health and, through stigma, social-emotional health. When untreated, they can cause life-threatening conditions. We aimed to assess the effect of a holistic, co-designed, region-wide skin control programme on the prevalence of impetigo. The SToP (See, Treat, and Prevent Skin Sores and Scabies) trial is a pragmatic, open-cohort, stepped-wedge cluster randomised trial involving participants aged 0-18 years in nine remote communities of the Kimberley, Western Australia. The trial involves programmatic interventions in three domains: See (skin checks and skin infection recognition training), Treat (skin infection treatment training, sulfamethoxazole-trimethoprim for impetigo, and ivermectin for scabies), and Prevent (co-designed health promotion and environmental health). Four clusters, defined as pragmatic aggregations of communities, were randomised in two steps to progressively receive the activities during ten visits. The primary outcome was the proportion of school-aged children (aged 5-9 years) with impetigo. We adopted an intention-to-treat analysis and compared the intervention with the control (usual care before the start of intervention) states to derive a time and cluster averaged effect using Bayesian modelling. This study is registered with Australian New Zealand Clinical Trials Registry, ACTRN12618000520235. Between Sept 19, 2018, and Nov 22, 2022, 915 children were consented and 777 (85%) had skin checks performed on at least one of ten possible visits between May 5, 2019, and Nov 22, 2022. Of the participants, 448 (58%) of 777 were aged 5-9 years at one or more of the visit timepoints and were eligible for primary outcome assessment. A decline in impetigo occurred across all clusters, with the greatest decline during the observational period of baseline skin checks before commencement of the interventional trial activities activities. The mean (95% credible interval) for the conditional posterior odds ratio for observing impetigo in the intervention compared with the control period was 1·13 (0·71-1·70). The probability that the intervention reduced the odds of observing impetigo was 0·33. A decreased prevalence of impetigo during the observational period before the commencement of trial activities was sustained across the trial, attributable to the trimodal skin health initiative. Although the prevalence of impetigo reduced, there is no direct evidence to attribute this to the individual effects of the trial activities. The wholistic approach inclusive of skin checks collectively contributed to the sustained reduction in impetigo. Western Australia Department of Health, Australian National Health and Medical Research Council, and Healthway.

Bayesian ex Post Evaluation of Recursive Multi-Step-Ahead Density Prediction

This research is focused on a formal Bayesian method of recursive multi-step-ahead density prediction and its ex post evaluation. Our approach remains within the framework of the standard (classical or orthodox) Bayesian paradigm based on the Bayes factor and on the use of the likelihood-based update. We propose a new decomposition of the predictive Bayes factor into the product of partial Bayes factors, for both a finite number of consecutive k-step-ahead forecasts (where k>1) and the recursive updates of the posterior odds ratios based on updated data sets. The first factor in the decomposition is related to the relative k-step-ahead forecasting ability of models, while the second one measures the updating effect. To illustrate the usefulness of the proposed measures, we apply the new decomposed predictive Bayes factors to compare the forecasting ability of models when the true data generating process (DGP) is known, using simulated data sets. Taking into account the effect of updating, the posterior odds ratios leads to the conclusion that the best model coincides with the true DGP. However, the highest k-step-ahead forecasting ability (considered alone) can be achieved by some other, less adequate models. Next, we investigate the predictive ability of different Vector Error Correction (VEC) models with conditional heteroscedasticity, combining three macroeconomic variables: unemployment, inflation and interest rates, separately for the US and Polish economies. The results show that the inference about the models’ predictive performance depends on the forecast horizon as well as on taking into account the updating effect.

Advancing health equity by understanding race disparities and other factors associated with PTSD symptom improvement following evidence-based psychotherapy

Several studies found that Black veterans demonstrate less posttraumatic stress disorder (PTSD) symptom improvement than White veterans following PTSD evidence-based psychotherapies (EBPs). We aimed to understand this disparity among veterans receiving EBPs by modeling race with demographic, clinical, and service utilization factors. Using electronic health records, we employed a cohort study of Iraq and Afghanistan War Veterans who initiated PTSD EBP treatment and completed > 2 PTSD symptom measures (N = 21,751). Using hierarchical Bayesian logistic regressions, we modeled the probability of PTSD symptom improvement. Black race was associated with less PTSD improvement (mean posterior odds ratio [MPOR] = 0.92; 95 % plausibility interval [PI] = 0.84, 1.0), as was group therapy (MPOR = 0.67; 95 % PI = 0.62, 0.73). Factors associated with greatest improvement included prolonged exposure (MPOR = 1.35; 95 % PI = 1.25, 1.45) and treatment density (MPOR = 1.40; 95 % PI = 1.36, 1.45). On average, Black veterans evidenced PTSD EBP improvement disparities. Clinical and utilization did not fully account for these disparities, although disproportionate representation of Black veterans in group CPT may explain some of these differences. Understanding experiences such as race-based trauma and chronic racism and discrimination is critical to provide Black veterans with the most effective PTSD care.

A lensed radio jet at milli-arcsecond resolution – II. Constraints on fuzzy dark matter from an extended gravitational arc

ABSTRACT Using a single gravitational lens system observed at ≲ 5 mas resolution with very long baseline interferometry, we place a lower bound on the mass of the fuzzy dark matter (FDM) particle, ruling out mχ ≤ 4.4 × 10−21 eV with a 20:1 posterior odds ratio relative to a smooth lens model. We generalize our result to non-scalar and multiple-field models, such as vector FDM, with mχ,vec > 1.4 × 10−21 eV. Due to the extended source structure and high angular resolution of the observation, our analysis is directly sensitive to the presence of granule structures in the main dark matter halo of the lens, which is the most generic prediction of FDM theories. A model based on well-understood physics of ultra-light dark matter fields in a gravitational potential well makes our result robust to a wide range of assumed dark matter fractions and velocity dispersions in the lens galaxy. Our result is competitive with other lower bounds on mχ from past analyses, which rely on intermediate modelling of structure formation and/or baryonic effects. Higher resolution observations taken at 10–100 GHz could improve our constraints by up to two orders of magnitude in the future.

Quantile three-factor model with heteroskedasticity, skewness, and leptokurtosis

The Fama-French three-factor model advances the capital asset pricing model by expanding size risk and value risk factors to market risk factors. A quantile Fama-French three-factor model with GARCH-type dynamics, leptokurtosis, and skewness via asymmetric Student t errors is proposed to overcome the limitations of the existing ones. One can investigate how the daily volatility and market risk factors act under different market conditions represented by quantile levels via the proposed model. Bayesian adaptive Markov chain Monte Carlo methods are used to estimate model parameters in the proposed model over various quantile levels. This study assesses the Bayesian inference performance via simulation studies in which the designated models are misspecified and considers some daily stock returns from NASDAQ to help further select the best model via the posterior odds ratio. It is clear that the various market conditions and the GARCH effect should be incorporated into the model. Findings show that the estimation of the size factor turns insignificant for lower quantiles - i.e., when the market is in a panic, investors ignore the size effect of a company's assets.

Inference algorithms for the useful life of safety instrumented systems under small failure sample data

Safety instrumented systems(SIS) have been widely used in petroleum and chemical plants to detect and respond to dangerous events and prevent them from developing into accidents. The in-service time of SIS does not exceed its useful life is one of the crucial assumptions of IEC functional safety standards. The testing method recommended in the IEC standard is essentially a chi-square testing, where the testing effect is proportional to the sample size and, therefore, not suitable for testing the type of data distribution under small samples. In this paper, a rapid inference method of useful life (RIUL) is proposed to: i) determine whether the distribution type of failure data is exponential under small samples with the help of Anderson-Darling testing, and ii) use the Bayesian sequential testing method for estimating the useful life. The sequential posterior odds ratio testing is introduced to test the equipment failure rate one by one. The proposed RIUL approach is applied to the liquid-level protection circuit of the hot high-pressure separator. The engineering simulation results show that compared with IEC standard methods, the proposed method can be performed with fewer failure data, providing a theoretical basis for reasonable maintenance and replacement of equipment.

Pediatric delirium is associated with increased brain injury marker levels in cardiac surgery patients

Despite global consensus on the importance of screening pediatric delirium, correlations between pediatric delirium during acute brain injury and adult delirium are unclear. Therefore, we hypothesized that similar pediatric biomarkers reflect acute brain injury as in adult delirium. We observed pediatric cardiac surgery patients from neonatal age to 18 years, who were admitted to our pediatric intensive care unit after cardiovascular operations between October 2019 to June 2020, up to post-operative day 3 (4 days total). We recorded age, sex, risk score (Risk Adjustment in Congenital Heart Surgery [RACHS-1]), midazolam/dexmedetomidine/fentanyl dosage, and pediatric Sequential Organ Failure Assessment (pSOFA). Richmond Agitation-Sedation Scale (RASS), Cornell Assessment of Pediatric Delirium (CAPD), Face, Leg, Activity, Consolability (FLACC) behavioral scale, and Withdrawal Assessment Tool (WAT-1) scales were used and serum sampling for neuron specific enolase (NSE) was conducted. Consciousness status was considered hierarchical (coma > delirium > normal) and associations between conscious status and NSE were conducted by hierarchical Bayesian modeling. We analyzed 134 data points from 40 patients (median age 12 months). In the multi-regression model, NSE was positively associated with coma [posterior odds ratio (OR) = 1.1, 95% credible interval (CrI) 1.01–1.19] while pSOFA [posterior OR = 1.63, 95% CrI 1.17–2.5], midazolam [posterior OR = 1.02, 95% CrI 1.01–1.04], and dexmedetomidine [posterior OR = 9.52, 95% CrI 1.02–108.85] were also associated. We also evaluated consciousness state probability at each NSE concentration and confirmed both that consciousness was hierarchically sorted and CAPD scores were also associated with NSE [posterior OR = 1.32, 95% CrI 1.09–1.58]. “Eye contact” (r = 0.55) was the most correlated component with NSE within the pain, withdrawal syndrome, and PD items. PD within the hierarchy of consciousness (coma, delirium, normal) and CAPD scores are associated with brain injury marker levels. Using pediatric delirium assessment tools for monitoring brain injury, especially eye contact, is a reliable method for observing PD.

Enhanced Lot Acceptance Testing Based on Defect Counts and Posterior Odds Ratios

Optimal defects-per-unit test plans based on posterior odds ratios are developed for the disposition of product lots. The number of nonconformities per unit is modeled by the Conway–Maxwell–Poisson distribution rather than the typical Poisson model. In essence, a submitted batch is conforming if its posterior acceptability is sufficiently large. First, a useful approximation of the optimal test plan is derived in closed form using the asymptotic normality of the log ratio. A mixed-integer nonlinear programming problem is then solved via Monte Carlo simulation to find the smallest number of inspected items per lot and the maximum tolerable posterior odds ratio. The methodology is applied to the manufacturing of paper and glass. The suggested sampling plan for lot sentencing provides the specified protections to both manufacturers and customers and minimizes the needed sample size. In terms of inspection effort and accuracy, the proposed approach is virtually an advantageous extension of the classical frequentist perspective. In many practical cases, it yields more precise assessments of the current consumer and producer risks, as well as more realistic decision rules.

Assessing spatial patterns of HIV prevalence and interventions in semi-urban settings in South Africa. Implications for spatially targeted interventions.

Equitable allocation of resources targeting the human immunodeficiency virus (HIV) at the local level requires focusing interventions in areas of the greatest need. Understanding the geographical variation in the HIV epidemic and uptake of selected HIV prevention and treatment programmes are necessary to identify such areas. Individual-level HIV data were obtained from a 2012 national HIV survey in South Africa. Spatial regression models on each outcome measure (HIV infection, sub-optimal condom use or non-anti-retroviral treatment (ART) adjusted for spatial random effects at the ward level were fitted using WINBUGS software. In addition, ward-level data was utilized to estimate condom use coverage and ART initiation rates which were obtained from routinely collected data in 2012. Ordinary Kriging was used to produce smoothed maps of HIV infection, condom use coverage and ART initiation rates. HIV infection was associated with individuals undertaking tertiary education [posterior odds ratio (POR): 19.53; 95% credible intervals (CrI): 3.22- 84.93]. Sub-optimal condom use increased with age (POR: 1.09; 95%CrI: 1.06-1.11) and was associated with being married (POR: 4.14; 95%CrI: 1.23-4.28). Non-ART use was associated with being married (POR: 6.79; 95%CrI: 1.43-22.43). There were clusters with high HIV infection, sub-optimal condom use, and non- ART use in Ekurhuleni, an urban and semi-urban district in Gauteng province, South Africa. Findings show the need for expanding condom programmes and/or strengthening other HIV prevention programmes such as pre-exposure prophylaxis and encouraging sustained engagement in HIV care and treatment in the identified areas with the greatest need in Ekurhuleni Metropolitan Municipality.

Six-canal video head impulse test in patients with labyrinthine and retrolabyrinthine pathology: detecting vestibulo-ocular reflex deficits.

Abnormal gains in six-canal video head impulse test are attributed to semi-circular canal deficits. However, as video head impulse test responses are linked to the vestibulo-ocular reflex, it was hypothesised that abnormal gains can be caused by vestibulo-ocular reflex pathway deficits. This study compared video head impulse test gains in 20 patients with superior semi-circular canal dehiscence (labyrinthine cause) and 20 side- and gender-matched patients with vestibular schwannomas (retrolabyrinthine cause), and investigated correlations between them (Mann-Kendall trend test). Vestibular schwannoma but not superior semi-circular canal dehiscence was significantly associated with abnormal lateral (odds ratio = 9.00 (95 per cent confidence interval = 1.638-49.44), p = 0.011) and posterior (odds ratio = 9.00 (95 per cent confidence interval = 2.151-37.659), p = 0.003) canal status. In vestibular schwannoma patients, there was a statistically significant degree of dependence between all ipsilesional canal video head impulse test gains; such dependence was not observed in superior semi-circular canal dehiscence. Vestibulo-ocular reflex gains differ in patients with labyrinthine and retrolabyrinthine disease; this suggests that abnormal gains can indicate deficits not only in the semi-circular canals but also elsewhere along the vestibulo-ocular reflex pathway.

Modeling Structural Breaks in Disturbances Precision or Autoregressive Parameter in Dynamic Model: A Bayesian Approach

The focus of this paper is the examination of dynamic models in the presence of structural changes either due to disturbances precision or autoregressive parameter under the Bayesian framework. The Bayesian analysis of the dynamic model has been carried out under the mixture of prior distributions for the parameters. The posterior distribution of parameters is derived to obtain Bayes estimators under quadratic loss function ignoring the possibility of structural breaks in regression coefficients. The posterior odds ratio has been developed under the assumption that disturbance precision leads to structural change as against the autoregressive parameter. The theoretical framework is also empirically tested employing data set of Indian companies considering financial variables like debt, profitability, investment etc.; covering the global financial crisis (GFC) period. The empirical exercise carried out highlights 2008–09 as the major structural breakpoint when many Indian companies suffered losses.

SPECT myocardial perfusion imaging identifies myocardial ischemia in patients with a history of COVID-19 without coronary artery disease.

We aimed to examine the effect of a history of COVID-19 on myocardial ischemia in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) in patients who presented with shortness of breath and/or chest pain after recovery. For this single-center retrospective study, patients who presented at cardiology outpatient clinics and had SPECT-MPI were screened. A total of 1888 patients were included in the study, 340 of whom had a history of COVID-19. 64 patients with > 50% stenosis on coronary angiography were excluded from the study. The primary outcome of the study was abnormal MPI. In the study population, the median age was 56 (49–64 IQR) years, and 1127 (65%) of the patients were female. Abnormal MPI was detected in 77 patients (23%) in the COVID-19 group and in 244 patients (16%) in the non–COVID-19 group. After adjustment was performed for clinical predictors using Bayesian logistic regression, an important association was found between the presence of a confirmed prior COVID-19 infection and abnormal MPI (posterior median odds ratio, 1.70 [95% CrI, 1.20–2.40], risk difference, 9.6% [95% CrI, 1.8%, 19.7%]). In SPECT-MPI, ischemia rates were observed to be higher in COVID-19 group and it was found that a confirmed prior COVID-19 might predict of abnormal MPI.

Efficacy and Safety of Adjunctive Aripiprazole, Metformin, and Paeoniae-Glycyrrhiza Decoction for Antipsychotic-Induced Hyperprolactinemia: A Network Meta-Analysis of Randomized Controlled Trials.

Aripiprazole, metformin, and paeoniae–glycyrrhiza decoction (PGD) have been widely used as adjunctive treatments to reduce antipsychotic (AP)-induced hyperprolactinemia in patients with schizophrenia. However, the comparative efficacy and safety of these medications have not been previously studied. A network meta-analysis of randomized controlled trials (RCTs) was conducted to compare the efficacy and safety between aripiprazole, metformin, and PGD as adjunctive medications in reducing AP-induced hyperprolactinemia in schizophrenia. Both international (PubMed, PsycINFO, EMBASE, and Cochrane Library databases) and Chinese (WanFang, Chinese Biomedical, and Chinese National Knowledge infrastructure) databases were searched from their inception until January 3, 2019. Data were analyzed using the Bayesian Markov Chain Monte Carlo simulations with the WinBUGS software. A total of 62 RCTs with 5,550 participants were included in the meta-analysis. Of the nine groups of treatments included, adjunctive aripiprazole (<5 mg/day) was associated with the most significant reduction in prolactin levels compared to placebo (posterior MD = −65.52, 95% CI = −104.91, −24.08) and the other eight treatment groups. Moreover, adjunctive PGD (>1:1) was associated with the lowest rate of all-cause discontinuation compared to placebo (posterior odds ratio = 0.45, 95% CI = 0.10, 3.13) and adjunctive aripiprazole (>10 mg/day) was associated with fewer total adverse drug events than placebo (posterior OR = 0.93, 95% CI = 0.65, 1.77) and other eight treatment groups. In addition, when risperidone, amisulpride, and olanzapine were the primary AP medications, adjunctive paeoniae/glycyrrhiza = 1:1, aripiprazole <5 mg/day, and aripiprazole >10 mg/day were the most effective treatments in reducing the prolactin levels, respectively. Adjunctive aripiprazole, metformin, and PGD showed beneficial effects in reducing AP-induced hyperprolactinemia in schizophrenia, with aripiprazole (<5 mg/day) being the most effective one.

The Geography of Child Stunting in Kenya: What Can We Learn From the Sub-National County Variations of the Kenya Household Surveys?

Background: Reduction in child malnutrition is one of the key components of the 2030 Sustainable Development Goals. National-level monitoring efforts are very useful for closing inequalities in the burden of stunting, but they usually mask huge sub-national variations, making targeting of the most affected population groups in specific locations difficult. In this study, we examine the geographical variation of stunting at the sub-national county-level in Kenya, to highlight the importance of estimates at sub-national level for programming and for monitoring of the 2025 internationally agreed targets for stunting and the 2030 SDG target 2.2. Methods: We used a nationally representative cross-sectional household sample of 18,600 children with anthropometric measurements for height-for-age (stunting) who were under five years in the 2014 Kenya Demographic and Health Survey. We analysed geographical variations of stunting across the 47 counties of Kenya and used a Bayesian geo-additive mixed model while accounting for observable and unobservable risk factors. This approach isolates geographical disparities, net of known covariates associated with stunting, and may point to unobservable factors that need attention. Findings: In 2014, prior to the launch of the SDGs and agreement of the 2025 Global Targets for Stunting, the national stunting rate in Kenya was 26 percent, meaning that if the 2025 targets of reducing stunting by 40 percent are to be achieved, the country should be aiming at stunting rates among under-five children of 10.4 percent or lower. Furthermore, the national average masks sub-national county level variations ranging from 10.7 percent in Kirinyaga to 37.7 percent in Kitui. Factors significantly associated with higher risk of stunting were found for boys [posterior odds ratio and 95% credible region: 1.40 (1.24, 1.58)]; children from the Pokomo ethnic group: 2.54 (1.34, 4.98); children from households with 10 or more members: 1.35 (0.99, 1.75); child’s age ( higher up to 10 months of age then levelling off at 20 months); and lower risk of stunting among children from mothers with secondary education; children from the highest wealth quintile: 0.38 (0.27, 0.53) and children with >24-month birth interval: 0.71 (0.62, 0.83). After adjusting for known covariates, there were striking residual geographical variations of stunting at county-level with the highest risk found in Madera county [odds ratio and 95% credible region: 2.00 (1.20, 3.61)], a county bordering Ethiopia and Somalia, and the lowest risk was found in the counties of Kirinyaga [0.90(0.60, 1.34)], Nairobi [1.10(0.79, 1.60)] and Nyeri [0.98(0.67, 1.43)]. Interpretation: Overall, the observed sub-national stunting estimates point to great variations in child stunting in Kenya, which show that the task of achieving the 2025 international targets for stunting and the achievement of the SDG2 is made harder by geographical disparities that would need to be addressed. The geographic patterns in undernutrition rates show the potential role of intervening at sub-national and county levels. Uneven distribution of resources, and poor access to health services and nutrition programs may explain some of these county-level differentials. Funding Information: This study was funded by the Children’s Investment Fund Foundation (CIFF). Declaration of Interests: We declare no competing interests.

A Feasibility Study to Increase Chronic Hepatitis C Virus RNA Testing and Linkage to Care among Clients Attending Homeless Services in Amsterdam, The Netherlands.

People who inject drugs (PWID) are disproportionately affected by hepatitis C virus (HCV) infections and are frequently homeless. To improve HCV case finding in these individuals, we examined the feasibility of rapid HCV RNA testing in homeless services in Amsterdam. In 2020, we provided a comprehensive service to homeless facilities, which included workshops on HCV for personnel, a “hepatitis ambassador” at each facility, a rapid, onsite HCV RNA fingerstick test service, and assistance with linkage to care. Risk factors for HCV RNA-positive status were examined using Bayesian logistic regression. Of the 152 participants enrolled, 150 (87% men; median age: 47 years) accepted rapid HCV testing. Seven tested HCV RNA positive (4.7%, 95%CrI = 1.31–8.09; 7/150). Of these, five (71%) were linked to care, of whom four (57%, 4/7) initiated treatment and one (14%, 1/7) delayed treatment due to a drug–drug interaction. Of these four people, two completed treatment (50%), of whom one (25%) achieved sustained virologic response after 12 weeks. HCV RNA-positive individuals were more likely to originate from Eastern Europe (posterior-odds ratio (OR) = 3.59 (95% credible interval (CrI) = 1.27–10.04)) and to inject drugs (ever: posterior-OR = 3.89 (95% CrI = 1.37–11.09); recent: posterior-OR = 3.94 (95% CrI = 1.29–11.71)). We identified HCV RNA-positive individuals and linkage to care was relatively high. Screening in homeless services with rapid testing is feasible and could improve HCV case finding for PWID who do not regularly attend primary care or other harm reduction services for people who use drugs.

New Estimators of the Bayes Factor for Models with High-Dimensional Parameter and/or Latent Variable Spaces.

Formal Bayesian comparison of two competing models, based on the posterior odds ratio, amounts to estimation of the Bayes factor, which is equal to the ratio of respective two marginal data density values. In models with a large number of parameters and/or latent variables, they are expressed by high-dimensional integrals, which are often computationally infeasible. Therefore, other methods of evaluation of the Bayes factor are needed. In this paper, a new method of estimation of the Bayes factor is proposed. Simulation examples confirm good performance of the proposed estimators. Finally, these new estimators are used to formally compare different hybrid Multivariate Stochastic Volatility–Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MSV-MGARCH) models which have a large number of latent variables. The empirical results show, among other things, that the validity of reduction of the hybrid MSV-MGARCH model to the MGARCH specification depends on the analyzed data set as well as on prior assumptions about model parameters.

Prior Choices of Between-Study Heterogeneity in Contemporary Bayesian Network Meta-analyses: an Empirical Study.

Network meta-analysis (NMA) is a popular tool to compare multiple treatments in medical research. It is frequently implemented via Bayesian methods. The prior choice of between-study heterogeneity is critical in Bayesian NMAs. This study evaluates the impact of different priors for heterogeneity on NMA results. We identified all NMAs with binary outcomes published in The BMJ, JAMA, and The Lancet during 2010-2018, and extracted information about their prior choices for heterogeneity. Our primary analyses focused on those with publicly available full data. We re-analyzed the NMAs using 3 commonly-used non-informative priors and empirical informative log-normal priors. We obtained the posterior median odds ratios and 95% credible intervals of all comparisons, assessed the correlation among different priors, and used Bland-Altman plots to evaluate their agreement. The kappa statistic was also used to evaluate the agreement among these priors regarding statistical significance. Among the selected Bayesian NMAs, 52.3% did not specify the prior choice for heterogeneity, and 84.1% did not provide rationales. We re-analyzed 19 NMAs with full data available, involving 894 studies, 173 treatments, and 395,429 patients. The correlation among posterior median (log) odds ratios using different priors were generally very strong for NMAs with over 20 studies. The informative priors produced substantially narrower credible intervals than non-informative priors, especially for NMAs with few studies. Bland-Altman plots and kappa statistics indicated strong overall agreement, but this was not always the case for a specific NMA. Priors should be routinely reported in Bayesian NMAs. Sensitivity analyses are recommended to examine the impact of priors, especially for NMAs with relatively small sample sizes. Informative priors may produce substantially narrower credible intervals for such NMAs.

Optimal lot sentencing based on defective counts and prior acceptability

Optimal inspection schemes for lot sentencing based on defective counts and prior lot acceptability, which provide appropriate protections to manufacturers and customers, are derived by minimizing the sampling inspection effort. An efficient computational procedure to determine the best test plan is presented. Moreover, an explicit approximation of the optimal scheme is deduced and its accuracy is analyzed. The achieved results practically solve the underlying constrained optimization problem in closed-form. The developed methodology is applied to the design of reliability demonstration test plans using failure count data and posterior odds ratios. Some illustrative examples are also presented. In most practical cases, the suggested approach is quite robust to small deviations in the prior acceptability, and allows the practitioners to substantially reduce the required sample size for screening lots of incoming and outgoing goods, as well as to appreciably improve the evaluations of the actual producer and consumer risks. Furthermore, it provides straightforward ways to combine multiple expert opinions and to update the optimal inspection scheme using the current estimate of the prior lot acceptability. In addition, the proposed test plans usually outperform the noninformative/frequentist schemes in terms of sampling cost and are quasi-optimal when the previous knowledge is slightly modified.

Bayesian Unit Root Test for AR(1) Model with Trend Approximated

The objective of present study is to develop a time series model for handling the non-linear trend process using a spline function. Spline function is a piecewise polynomial segment concerning the time component. The main advantage of spline function is the approximation, non linear time trend, but linear time trend between the consecutive join points. A unit root hypothesis is projected to test the non stationarity due to presence of unit root in the proposed model. In the autoregressive model with linear trend, the time trend vanishes under the unit root case. However, when non-linear trend is present and approximated by the linear spline function, through the trend component is absent under the unit root case, but the intercept term makes a shift with r knots. For decision making under the Bayesian perspective, the posterior odds ratio is used for hypothesis testing problems. We have derived the posterior probability for the assumed hypotheses under appropriate prior information. A simulation study and an empirical application are presented to examine the performance of theoretical outcomes.