Estimation for Flexible Weibull Extension-Burr XII Distribution under Adaptive Type-II Progressive Censoring Scheme

In this paper, based on an adaptive Type-II progressive censoring scheme, estimation of flexible Weibull extension-Burr XII distribution is discussed. Maximum likelihood estimation and asymptotic confidence intervals of the unknown parameters are obtained. The adaptive Metropolis (AM) method is applied to carry out a Bayesian estimation procedure under symmetric and asymmetric loss functions and calculate the credible intervals. A simulation study is carried out to assess the performance of the estimators. Finally, a real life data set is used for illustration purpose.

Estimation for Weibull Parameters with Generalized Progressive Hybrid Censored Data

In this paper, the interest is in estimating the Weibull products when the available data is obtained via generalized progressive hybrid censoring. The testing scheme conducts products of interest under a more flexible way and allows collecting failure data in efficient and adaptable experimental scenarios than traditional lifetime testing. When the latent lifetime of products follows Weibull distribution, classical and Bayesian inferences are considered for unknown parameters. The existence and uniqueness of maximum likelihood estimates are established, and approximate confidence intervals are also constructed via asymptotic theory. Bayes point estimates as well as the credible intervals of the parameters are obtained, and correspondingly, Monte Carlo sampling technique is also provided for complex posterior computation. Extensive numerical analysis is carried out, and the results show that the generalized progressive hybrid censoring is an adaptive procedure in practical lifetime experiment, both proposed classical and Bayesian inferential approaches perform satisfactorily, and the Bayesian results are superior to conventional likelihood estimates.

Bayesian paired comparison with the bpcs package

This article introduces the R package (Bayesian Paired Comparison in Stan) and the statistical models implemented in the package. This package aims to facilitate the use of Bayesian models for paired comparison data in behavioral research. Bayesian analysis of paired comparison data allows parameter estimation even in conditions where the maximum likelihood does not exist, allows easy extension of paired comparison models, provides straightforward interpretation of the results with credible intervals, has better control of type I error, has more robust evidence towards the null hypothesis, allows propagation of uncertainties, includes prior information, and performs well when handling models with many parameters and latent variables. The package provides a consistent interface for R users and several functions to evaluate the posterior distribution of all parameters to estimate the posterior distribution of any contest between items and to obtain the posterior distribution of the ranks. Three reanalyses of recent studies that used the frequentist Bradley–Terry model are presented. These reanalyses are conducted with the Bayesian models of the package, and all the code used to fit the models, generate the figures, and the tables are available in the online appendix.

A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features

Multiscale computational modelling is challenging due to the high computational cost of direct numerical simulation by finite elements. To address this issue, concurrent multiscale methods use the solution of cheaper macroscale surrogates as boundary conditions to microscale sliding windows. The microscale problems remain a numerically challenging operation both in terms of implementation and cost. In this work we propose to replace the local microscale solution by an Encoder-Decoder Convolutional Neural Network that will generate fine-scale stress corrections to coarse predictions around unresolved microscale features, without prior parametrisation of local microscale problems. We deploy a Bayesian approach providing credible intervals to evaluate the uncertainty of the predictions, which is then used to investigate the merits of a selective learning framework. We will demonstrate the capability of the approach to predict equivalent stress fields in porous structures using linearised and finite strain elasticity theories.

Childhood Immuno-metabolic Markers and Risk of Depression and Psychosis in Adulthood: A Prospective Birth Cohort Study

Metabolic and inflammatory disorders commonly co-occur with depression and psychosis, with emerging evidence implicating immuno-metabolic dysfunction in their aetiology. Previous studies have reported metabolic dysfunction and inflammation in adults with depression and psychosis. However, longitudinal studies testing the direction of association, and the effects of different dimensions of early-life immuno-metabolic dysfunction on adult psychopathology, are limited. Using data from 3875 birth cohort participants we examined longitudinal associations of three metabolic hormones (leptin, adiponectin, insulin) at age 9 with risks for depression- and psychosis-spectrum outcomes at age 24. In addition, using nine immuno-metabolic biomarkers, we constructed an exploratory bifactor model showing a general immuno-metabolic factor and three specific factors (adiposity, inflammation, and insulin resistance), which were also used as exposures. Childhood leptin was associated with adult depressive episode (adjusted odds ratio (aOR)=1.28; 95% CI, 1.00-1.64) and negative symptoms (aOR=1.12; 95% CI, 1.05-1.20). The general immuno-metabolic factor was associated with depressive symptoms (aOR=1.05; 95% CI, 1.01-1.08) and psychotic experiences (aOR=1.20; 95% CI, 1.01-1.42). The adiposity factor was associated with negative symptoms (aOR=1.07; 95% CI 1.02-1.12). All associations tended to be stronger in women, though 95% credible intervals overlapped with that for men. In women, the inflammatory factor was associated with depressive episode (aOR=1.23; 95% CI, 1.01-1.47) and atypical depressive symptoms (aOR=1.10; 95% CI, 1.02-1.19). While general immuno-metabolic dysfunction in childhood may contribute to risks for both psychotic and depressive symptoms in adulthood, childhood adiposity and inflammation are linked to affective (depressive, atypical, and negative) symptoms.

Impacts of a delayed and slow-paced vaccination on cases and deaths during the COVID-19 pandemic: a modelling study

Background: In Brazil, vaccination has always been cutting across party political and ideological lines, which have delayed its start and brought the whole process into disrepute. Such divergences put the immunisation of the population in the background and create additional hurdles beyond the pandemic, mistrust and scepticism over vaccines.Methods: We conduct a mathematical modelling study to analyse the impacts of late vaccination and with slowly increasing coverage, as well as how harmful it would be if part of the population refused to get vaccinated or missed the second dose. We analyse data from confirmed cases, deaths caused by COVID-19, and vaccination in the state of Rio de Janeiro in the period between March 10, 2020, and October 27, 2021. The classical SIR model is extended to consider the effect of vaccination (efficacy, interval between doses, and vaccination rate) and data sets are regularised using Gaussian Process Regression. The model parameter distributions are estimated using Bayesian inference, aiming to obtain credible intervals in the simulations.Findings: We estimate that if the start of vaccination had been 30 days earlier, combined with efforts to drive vaccination rates up, 31,657 (25,801–35,117) deaths could have been averted. Our results also indicate that the slow pace of vaccination and the low demand for the second dose could cause a resurgence of cases as early as 2022.Interpretation: The government's inaction and lack of a strategic plan to fight the pandemic meant that vaccination started late, leading to thousands of deaths that could have been prevented. Even when reaching the expected vaccination coverage for the first dose, it is still challenging to increase adherence to the second dose and maintain a high vaccination rate to avoid new outbreaks.Funding: Carlos Chagas Filho Foundation for Supporting Research in the State of Rio de Janeiro (FAPERJ) and Brazilian National Council for Scientific and Technological Development (CNPq).

Small area variations and factors associated with blood pressure and body-mass index in adult women in Accra, Ghana: Bayesian spatial analysis of a representative population survey and census data

Background Body-mass index (BMI) and blood pressure (BP) levels are rising in sub-Saharan African cities, particularly among women. However, there is very limited information on how much they vary within cities, which could inform targeted and equitable health policies. Our study aimed to analyse spatial variations in BMI and BP for adult women at the small area level in the city of Accra, Ghana. Methods and findings We combined a representative survey of adult women’s health in Accra, Ghana (2008 to 2009) with a 10% random sample of the national census (2010). We applied a hierarchical model with a spatial term to estimate the associations of BMI and systolic blood pressure (SBP) and diastolic blood pressure (DBP) with demographic, socioeconomic, behavioural, and environmental factors. We then used the model to estimate BMI and BP for all women in the census in Accra and calculated mean BMI, SBP, and DBP for each enumeration area (EA). BMI and/or BP were positively associated with age, ethnicity (Ga), being currently married, and religion (Muslim) as their 95% credible intervals (95% CrIs) did not include zero, while BP was also negatively associated with literacy and physical activity. BMI and BP had opposite associations with socioeconomic status (SES) and alcohol consumption. In 2010, 26% of women aged 18 and older had obesity (BMI ≥ 30 kg/m2), and 21% had uncontrolled hypertension (SBP ≥ 140 and/or DBP ≥ 90 mm Hg). The differences in mean BMI and BP between EAs at the 10th and 90th percentiles were 2.7 kg/m2 (BMI) and in BP 7.9 mm Hg (SBP) and 4.8 mm Hg (DBP). BMI was generally higher in the more affluent eastern parts of Accra, and BP was higher in the western part of the city. A limitation of our study was that the 2010 census dataset used for predicting small area variations is potentially outdated; the results should be updated when the next census data are available, to the contemporary population, and changes over time should be evaluated. Conclusions We observed that variation of BMI and BP across neighbourhoods within Accra was almost as large as variation across countries among women globally. Localised measures are needed to address this unequal public health challenge in Accra.

Analysis for Xgamma Parameters of Life under Type-II Adaptive Progressively Hybrid Censoring with Applications in Engineering and Chemistry

Censoring mechanisms are widely used in various life tests, such as medicine, engineering, biology, etc., as they save (overall) test time and cost. In this context, we consider the problem of estimating the unknown xgamma parameter and some survival characteristics, such as reliability and failure rate functions in the presence of adaptive type-II progressive hybrid censored data. For this purpose, the maximum likelihood and Bayesian inferential approaches are used. Using the observed Fisher information under s-normal approximation, different asymptotic confidence intervals for any function of the unknown parameter were constructed. Using the gamma flexible prior, Bayes estimators against the squared-error loss were developed. Two procedures of Bayesian approximations—Lindley’s approximation and Metropolis–Hastings algorithm—were used to carry out the Bayes estimates and to construct the associated credible intervals. An extensive simulation study was implemented to compare the performance of the different methods. To validate the proposed methodologies of inference—two practical studies using datasets that form engineering and chemical fields are discussed.

P.072 Ticagrelor vs Clopidogrel in Addition to Aspirin in Minor Ischemic Stroke/ TIA – a Systematic Review & Network Meta-Analysis (NMA)

Background: Dual antiplatelet therapy (DAPT) is recommended after minor ischemic stroke/ transient ischemic attack (TIA), but Clopidogrel/ Aspirin has never been compared directly to Ticagrelor/ Aspirin. Our objective is to compare these regimens in terms of efficacy and safety. Methods: Medline, Embase, and Cochrane were searched for randomized controlled trials (RCTs) that enrolled adults with minor stroke/ TIA and administered antiplatelets within 72 hours. The primary efficacy outcome is recurrent stroke or death at 90 days. We performed a Bayesian-approach NMA. Between group comparisons were presented as odds-ratios (OR) with 95% credible intervals (95%CI). Sucraplots were based on calculated probabilities of rankings for individual outcomes. Results: 9/4014 studies were included: 5 RCTs and 4 subgroup analyses. 22,098 patients were analyzed. At 90 days, both DAPT regimens were superior to Aspirin in the prevention of recurrent stroke/ death. There was no significant difference between Clopidogrel/ ASA compared to Ticagrelor/ ASA (OR 0.90 [95%CI 0.74 – 1.09]), although Clopidogrel/ Aspirin was ranked #1 using Sucraplots. There was no significant difference between the interventions for mortality, bleeding, or adverse events. Conclusions: DAPT was superior to ASA in the prevention of recurrent strokes/ death, but there was no difference between Clopidogrel/ ASA and Ticagrelor/ ASA.

Analysis of Mechanistic Pathways in the Treatment of Non-Alcoholic Steatohepatitis. Evidence from a Bayesian Network Meta-Analysis

Background and Aims: Non-alcoholic steatohepatitis (NASH) is the most common cause of liver disease contributing to significant disease burden worldwide. However, there is lack of comparison of efficacy between different NASH drug classes. We conducted a network meta-analysis evaluating drug classes through comparing histological outcomes and targets of drugs. Approach & Results: Medline, EMBASE and CENTRAL were searched for articles evaluating NASH drugs in biopsy-proven NASH patients. Primary outcomes included NASH resolution without worsening of fibrosis, 2-point reduction in Non-alcoholic fatty liver disease Activity Score (NAS) without worsening of fibrosis and 1-point reduction in fibrosis. Treatments were classified into inflammation, energy, bile acid, and fibrosis modulators. The analysis was conducted with Bayesian network model and surface under the cumulative ranking curve (SUCRA) analysis. From the 48 trials included, treatments modulating energy (Risk ratio (RR): 1.84, Credible intervals (Crl): 1.29 - 2.65) were the most likely to achieve NASH resolution followed by treatments modulating fibrosis (RR 1.68, Crl: 0.55 - 5.28), bile acids (RR: 1.34, Crl: 0.78 - 2.26) and inflammation (RR: 0.94, Crl: 0.59 - 1.46). Energy and bile acids modulation were effective in 2-point NAS reduction without worsening of fibrosis (RR: 1.60, Crl 1.13 - 2.30 and RR: 1.79, Crl 1.14 - 2.86) and 1-point fibrosis (RR: 1.27, Crl:1.05 - 1.52 and RR: 1.54, Crl: 1.20 - 1.97). Conclusions: This network analysis demonstrates the relative superiority of drugs modulating energy pathways and bile acids in NASH treatment. This guides the development and selection of drugs for combination therapies.