Vaginal births after caesarean: What does Google think about it?

AimVaginal birth after caesarean (VBAC) is a relatively safe alternative to repeat caesarean birth in the proper context. This important decision to undergo an elective caesarean versus VBAC is ultimately a decision of the mother. The purpose of our study was to assess the quality of online information in relation to VBAC collected using the most common search engine: Google.MethodsThe 10 most common hit sites for the keywords “VBAC” and “Vaginal birth after caesarean” were evaluated using the search engine Google. The quality of websites was rated based on the Silberg scale for accountability, the modified Abbott's criteria for presentation and the SMOG index for readability. The content of each website was compared to the Society of Obstetricians and Gynecologists of Canada (SOGC) guidelines for VBAC.Findings13 out of 20 identified websites met the adequate criteria for accountability, with 85% of the websites indicating authorship. 11 websites were deemed aesthetically agreeable. The target audience, assessed by the readability score, was notably above the non-medical population with an average SMOG index score of 14.75. Only half of the websites contained recommendations, as detailed by the SOGC guidelines.ConclusionAlmost all sites target a higher academic level, making it beyond the comprehension of the general population. Woman friendly web-assessment tools should be provided to enable pregnant women to take an active role in their decision making.     

Inference and Decision Making for 21st-Century Drug Development and Approval

ABSTRACT

The cost and time of pharmaceutical drug development continue to grow at rates that many say are unsustainable. These trends have enormous impact on what treatments get to patients, when they get them and how they are used. The statistical framework for supporting decisions in regulated clinical development of new medicines has followed a traditional path of frequentist methodology. Trials using hypothesis tests of “no treatment effect” are done routinely, and the p-value < 0.05 is often the determinant of what constitutes a “successful” trial. Many drugs fail in clinical development, adding to the cost of new medicines, and some evidence points blame at the deficiencies of the frequentist paradigm. An unknown number effective medicines may have been abandoned because trials were declared “unsuccessful” due to a p-value exceeding 0.05. Recently, the Bayesian paradigm has shown utility in the clinical drug development process for its probability-based inference. We argue for a Bayesian approach that employs data from other trials as a “prior” for Phase 3 trials so that synthesized evidence across trials can be utilized to compute probability statements that are valuable for understanding the magnitude of treatment effect. Such a Bayesian paradigm provides a promising framework for improving statistical inference and regulatory decision making.     

互联网深化、信息不确定性与企业出口平稳性

互联网作为信息搜寻和传播的重要渠道,有利于缓解国际市场不确定性对企业出口决策的影响,从而有助于稳定企业出口预期,保障出口企业的持续生存。本文首先基于Fernandes 和 Tang(2014)的研究框架将互联网深化程度和出口集聚同时纳入到异质性贸易企业模型,在剥离出口集聚对企业出口行为影响的基础上,从理论视角揭示了互联网深化程度提高影响企业出口行为特别是出口持续生存的内在机理与方向。研究发现,不同于出口集聚会同时影响企业的需求冲击和成本冲击,互联网深化带来的信息溢出效应会通过贝叶斯法则(Bayesian Rule)使得企业修正其对国际市场不确定性的预期,提高其对目的国市场成本冲击的估计精度,从而在互联网深化程度较高的城市,企业进入出口市场的临界生产率较低,有利于其出口参与度和出口稳定性的提高。然而互联网深化程度提高不影响企业在目的国市场产品出口的集约边际。本文使用2000-2013 年中国工业企业数据库、中国海关数据库和中国城市统计年鉴数据库的匹配数据从企业-目的国-产品层面实证检验了本文的理论命题。研究表明,我国政府应充分发挥互联网发展降低信息不确定性的微观机制,以推动外贸的平稳增长。     

Inference and optimal design of multiple constant-stress testing for generalized half-normal distribution under type-II progressive censoring

The generalized half-normal (GHN) distribution and progressive type-II censoring are considered in this article for studying some statistical inferences of constant-stress accelerated life testing. The EM algorithm is considered to calculate the maximum likelihood estimates. Fisher information matrix is formed depending on the missing information law and it is utilized for structuring the asymptomatic confidence intervals. Further, interval estimation is discussed through bootstrap intervals. The Tierney and Kadane method, importance sampling procedure and Metropolis-Hastings algorithm are utilized to compute Bayesian estimates. Furthermore, predictive estimates for censored data and the related prediction intervals are obtained. We consider three optimality criteria to find out the optimal stress level. A real data set is used to illustrate the importance of GHN distribution as an alternative lifetime model for well-known distributions. Finally, a simulation study is provided with discussion.     

Hierarchical Bayesian Modeling of Post-Earthquake Ignition Probabilities Considering Inter-Earthquake Heterogeneity

Post-earthquake fires are high-consequence events with extensive damage potential. They are also low-frequency events, so their nature remains underinvestigated. One difficulty in modeling post-earthquake ignition probabilities is reducing the model uncertainty attributed to the scarce source data. The data scarcity problem has been resolved by pooling the data indiscriminately collected from multiple earthquakes. However, this approach neglects the inter-earthquake heterogeneity in the regional and seasonal characteristics, which is indispensable for risk assessment of future post-earthquake fires. Thus, the present study analyzes the post-earthquake ignition probabilities of five major earthquakes in Japan from 1995 to 2016 (1995 Kobe, 2003 Tokachi-oki, 2004 Niigata–Chuetsu, 2011 Tohoku, and 2016 Kumamoto earthquakes) by a hierarchical Bayesian approach. As the ignition causes of earthquakes share a certain commonality, common prior distributions were assigned to the parameters, and samples were drawn from the target posterior distribution of the parameters by a Markov chain Monte Carlo simulation. The results of the hierarchical model were comparatively analyzed with those of pooled and independent models. Although the pooled and hierarchical models were both robust in comparison with the independent model, the pooled model underestimated the ignition probabilities of earthquakes with few data samples. Among the tested models, the hierarchical model was least affected by the source-to-source variability in the data. The heterogeneity of post-earthquake ignitions with different regional and seasonal characteristics has long been desired in the modeling of post-earthquake ignition probabilities but has not been properly considered in the existing approaches. The presented hierarchical Bayesian approach provides a systematic and rational framework to effectively cope with this problem, which consequently enhances the statistical reliability and stability of estimating post-earthquake ignition probabilities.     

The Myth of the Computer Hacker

The seriousness of computer hacking is not exaggerated, it is far worse than that. The computer hacker has attained the status of myth; society associates all computer crime with a mythical perpetrator that bears no resemblance to reality. This paper will argue that in the early stages of the myth the computer hacker was regarded as a highly skilled but mentally disturbed youth who has an unhealthy association with computers. The new reality of electronic commerce resulted in pressures that culminated in the computer hacker becoming regarded as a dangerous criminal. A thorough analysis of the statistics will demonstrate that the majority of computer intruders are neither dangerous nor highly skilled, and thus nothing like the mythical hacker.     

Multiparameter evidence synthesis in epidemiology and medical decision-making: current approaches

Summary.  Alongside the development of meta-analysis as a tool for summarizing research literature, there is renewed interest in broader forms of quantitative synthesis that are aimed at combining evidence from different study designs or evidence on multiple parameters. These have been proposed under various headings: the confidence profile method, cross-design synthesis, hierarchical models and generalized evidence synthesis. Models that are used in health technology assessment are also referred to as representing a synthesis of evidence in a mathematical structure. Here we review alternative approaches to statistical evidence synthesis, and their implications for epidemiology and medical decision-making. The methods include hierarchical models, models informed by evidence on different functions of several parameters and models incorporating both of these features. The need to check for consistency of evidence when using these powerful methods is emphasized. We develop a rationale for evidence synthesis that is based on Bayesian decision modelling and expected value of information theory, which stresses not only the need for a lack of bias in estimates of treatment effects but also a lack of bias in assessments of uncertainty. The increasing reliance of governmental bodies like the UK National Institute for Clinical Excellence on complex evidence synthesis in decision modelling is discussed.     

Moderate Deviations for Bayes Posteriors

Let ( X_k ) k be a sequence of i.i.d. random variables taking values in a set , and consider the problem of estimating the law of X₁ in a Bayesian framework. We prove, under mild conditions on the prior, that the sequence of posterior distributions satisfies a moderate deviation principle.     

Bayesian modelling of catch in a north-west Atlantic fishery

Summary. We model daily catches of fishing boats in the Grand Bank fishing grounds. We use data on catches per species for a number of vessels collected by the European Union in the context of the Northwest Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics (such as the size of the ship, the fishing technique used and the mesh size of the nets) are obvious candidates, but one can also consider the season or the actual location of the catch. Our database leads to 28 possible regressors (arising from six continuous variables and four categorical variables, whose 22 levels are treated separately), resulting in a set of 177 million possible linear regression models for the log-catch. Zero observations are modelled separately through a probit model. Inference is based on Bayesian model averaging, using a Markov chain Monte Carlo approach. Particular attention is paid to the prediction of catches for single and aggregated ships.     

A Bayesian Adjustment for Covariate Misclassification with Correlated Binary Outcome Data

Estimated associations between an outcome variable and misclassified covariates tend to be biased when the methods of estimation that ignore the classification error are applied. Available methods to account for misclassification often require the use of a validation sample (i.e. a gold standard). In practice, however, such a gold standard may be unavailable or impractical. We propose a Bayesian approach to adjust for misclassification in a binary covariate in the random effect logistic model when a gold standard is not available. This Markov Chain Monte Carlo (MCMC) approach uses two imperfect measures of a dichotomous exposure under the assumptions of conditional independence and non-differential misclassification. A simulated numerical example and a real clinical example are given to illustrate the proposed approach. Our results suggest that the estimated log odds of inpatient care and the corresponding standard deviation are much larger in our proposed method compared with the models ignoring misclassification. Ignoring misclassification produces downwardly biased estimates and underestimate uncertainty.