Econometric Analysis of Costing System Components in Rail Rate Regulation

In response to Congressional directive, the Interstate Commerce Commission (ICC) has created a railroad costing system that includes as key components ratios designed to estimate variable costs associated with freight transportation. The estimated variability ratios are used to determine freight surcharges, jurisdictional threshold rates, and basic rail rates in administrative law and federal court proceedings. In this article we assess the quality and reliability of the estimated variability ratios and their components against standards from economic theory and statistical theory and practice. Our work includes reproduction of the naive ICC regressions, updated naive regressions for the latest data set, estimation based on more secure econometric foundations, and sensitivity analyses comparing alternative estimation procedures. Fundamental questions arise concerning the scientific and evidentiary standards that are required for econometric methodology in policy making and regulatory activities.     

A computationally simple bivariate survival estimator for efficacy and safety

Both treatment efficacy and safety are typically the primary endpoints in Phase II, and even in some Phase III, clinical trials. Efficacy is frequently measured by time to response, death, or some other milestone event and thus is a continuous, possibly censored, outcome. Safety, however, is frequently measured on a discrete scale; in Eastern Cooperative Oncology Group clinical trial E2290, it was measured as the number of weekly rounds of chemotherapy that were tolerable to colorectal cancer patients. For the joint analysis of efficacy and safety, we propose a non-parametric, computationally simple estimator for the bivariate survival function when one time-to-event is continuous, one is discrete, and both are subject to right-censoring. The bivariate censoring times may depend on each other, but they are assumed to be independent of both event times. We derive a closed-form covariance estimator for the survivor function which allows for inference to be based on any of several possible statistics of interest. In addition, we derive its covariance with respect to calendar time of analysis, allowing for its use in sequential studies.     

On the viral safety of plasma pools and plasma derivatives

Summary.  In the industrialized world, a range of medicinal products are manufactured on a large scale from pools made of thousands of human blood and plasma donations. Policy makers as well as the general public are aware of the hazards of contamination following accumulated risks of individual donations. Today, the manufacturing process must therefore consider a complex sequence of risk reducing interventions, including screening tests and quarantine periods on pools and individual donations. We estimate the residual risk of hepatitis C infection following such sequences of events. This is the most common blood-borne infection in the western world, which affects an estimated 170 million people worldwide. We investigate the benefits and drawbacks of each intervention and study at several stages the dependence of the screening process on operational parameters that can be optimized, such as the number of donations from different donors in the pool and the length of the quarantine period. This leads to alternative risk reducing strategies that may be more (cost) effective or optimal under specific criteria.     

统计数据、统计安全与统计法治

 本文以《统计违法违纪行为处分规定》的施行为背景,以统计数据质量为切入点,讨论了统计法治问题。文章阐述了统计数据质量与国家统计安全、统计本质与统计法治的关系,并对实现统计法治的途径进行了探讨。     

Elliptical safety region plots for C pk

The process capability index C _pk is widely used when measuring the capability of a manufacturing process. A process is defined to be capable if the capability index exceeds a stated threshold value, e.g. C _pk >4/3. This inequality can be expressed graphically using a process capability plot, which is a plot in the plane defined by the process mean and the process standard deviation, showing the region for a capable process. In the process capability plot, a safety region can be plotted to obtain a simple graphical decision rule to assess process capability at a given significance level. We consider safety regions to be used for the index C _pk . Under the assumption of normality, we derive elliptical safety regions so that, using a random sample, conclusions about the process capability can be drawn at a given significance level. This simple graphical tool is helpful when trying to understand whether it is the variability, the deviation from target, or both that need to be reduced to improve the capability. Furthermore, using safety regions, several characteristics with different specification limits and different sample sizes can be monitored in the same plot. The proposed graphical decision rule is also investigated with respect to power.     

Multivariate non-linear time series modelling of exposure and risk in road safety research

Summary.  A multivariate non-linear time series model for road safety data is presented. The model is applied in a case-study into the development of a yearly time series of numbers of fatal accidents (inside and outside urban areas) and numbers of kilometres driven by motor vehicles in the Netherlands between 1961 and 2000. The model accounts for missing entries in the disaggregated numbers of kilometres driven although the aggregated numbers are observed throughout. We consider a multivariate non-linear time series model for the analysis of these data. The model consists of dynamic unobserved factors for exposure and risk that are related in a non-linear way to the number of fatal accidents. The multivariate dimension of the model is due to its inclusion of multiple time series for inside and outside urban areas. Approximate maximum likelihood methods based on the extended Kalman filter are utilized for the estimation of unknown parameters. The latent factors are estimated by extended smoothing methods. It is concluded that the salient features of the observed time series are captured by the model in a satisfactory way.     

Mobile safety cameras: estimating casualty reductions and the demand for secondary healthcare

We consider a fully Bayesian analysis of road casualty data at 56 designated mobile safety camera sites in the Northumbria Police Force area in the UK. It is well documented that regression to the mean (RTM) can exaggerate the effectiveness of road safety measures and, since the 1980s, an empirical Bayes (EB) estimation framework has become the gold standard for separating real treatment effects from those of RTM. In this paper we suggest some diagnostics to check the assumptions underpinning the standard estimation framework. We also show that, relative to a fully Bayesian treatment, the EB method is over-optimistic when quantifying the variability of estimates of casualty frequency. Implementing a fully Bayesian analysis via Markov chain Monte Carlo also provides a more flexible and complete inferential procedure. We assess the sensitivity of estimates of treatment effectiveness, as well as the expected monetary value of prevention owing to the implementation of the safety cameras, to different model specifications, which include the estimation of trend and the construction of informative priors for some parameters.     

Matrix decomposition in meta-analysis for extraction of adverse event pattern and patient-level safety profile

The purpose of assessing adverse events (AEs) in clinical studies is to evaluate what AE patterns are likely to occur during treatment. In contrast, it is difficult to specify which of these patterns occurs in each patient. To tackle this challenging issue, we constructed a new statistical model including nonnegative matrix factorization by incorporating background knowledge of AE-specific structures such as severity and drug mechanism of action. The model uses a meta-analysis framework for integrating data from multiple clinical studies because insufficient information is derived from a single trial. We demonstrated the proposed method by applying it to real data consisting of three Phase III studies, two mechanisms of action, five anticancer treatments, 3317 patients, 848 AE types, and 99,546 AEs. The extracted typical treatment-specific AE patterns coincided with medical knowledge. We also demonstrated patient-level safety profiles using the data of AEs that were observed by the end of the second cycle.     

Extreme value modelling of laboratory safety data from clinical studies

Most clinical studies collect several safety‐related laboratory variables. Generally, it is the extreme values of these variables that indicate potential safety issues. We illustrate the novel application of extreme value modelling to such data, with the aim of predicting the incidence of severe adverse drug reactions. By applying the methods to a clinical trial data set, we identify a dose–response relationship and use Bayesian techniques to identify a potential safety concern by making predictions from the fitted model, despite the small sample size. Copyright © 2012 John Wiley & Sons, Ltd.     

Why a Bayesian approach to safety analysis in pharmacovigilance is important

Large databases of routinely collected data are a valuable source of information for detecting potential associations between drugs and adverse events (AE). A pharmacovigilance system starts with a scan of these databases for potential signals of drug-AE associations that will subsequently be examined by experts to aid in regulatory decision-making. The signal generation process faces some key challenges: (1) an enormous volume of drug-AE combinations need to be tested (i.e. the problem of multiple testing); (2) the results are not in a format that allows the incorporation of accumulated experience and knowledge for future signal generation; and (3) the signal generation process ignores information captured from other processes in the pharmacovigilance system and does not allow feedback. Bayesian methods have been developed for signal generation in pharmacovigilance, although the full potential of these methods has not been realised. For instance, Bayesian hierarchical models will allow the incorporation of established medical and epidemiological knowledge into the priors for each drug-AE combination. Moreover, the outputs from this analysis can be incorporated into decision-making tools to help in signal validation and posterior actions to be taken by the regulators and companies. We discuss in this paper the apparent advantage of the Bayesian methods used in safety signal generation and the similarities and differences between the two widely used Bayesian methods. We will also propose the use of Bayesian hierarchical models to address the three key challenges and discuss the reasons why Bayesian methodology still have not been fully utilised in pharmacovigilance activities.     

Multivariate extreme value modelling of laboratory safety data from clinical studies

Generally, in the interpretation of clinical safety laboratory data, it is extreme values that indicate potential safety issues. We illustrate the application of multivariate extreme value modelling to such data. Applying the methods to a clinical trial dataset, we find unexpected extremal relationships that have potentially important implications for the interpretation of such data. Copyright © 2012 John Wiley & Sons, Ltd.     

建立生态安全评价指标体系的几个理论问题

全球环境日趋恶化已是不争的事实。人类未能从生态系统的角度去思考问题 ,其根源在于缺乏关于生态系统如何影响我们以及它们 (生态系统 )所处状态的信息 ,而缺少这些信息 ,在宏观上要制定有效的环境政策是不可能的。而在微观上 ,公众和企业的行为选择和行为调整也会因信息缺损而一事无成。　　一、生态安全的概念及本质关于生态安全的概念 ,学术界并没有一个标准的定义。笔者认为 ,生态安全是指生态系统保持过程连续、结构稳定和功能完整的一种超稳定状态。实现生态安全 ,就是要使生态环境能够有利于经济活动效率的提高 ,有利于人类健康和生…     

Evaluation of statistical methods for safety signal detection: a simulation study

Proactive evaluation of drug safety with systematic screening and detection is critical to protect patients' safety and important in regulatory approval of new drug indications and postmarketing communications and label renewals. In recent years, quite a few statistical methodologies have been developed to better evaluate drug safety through the life cycle of the product development. The statistical methods for flagging safety signals have been developed in two major areas – one for data collected from spontaneous reporting system, mostly in the postmarketing area, and the other for data from clinical trials. To our knowledge, the methods developed for one area have not been applied to the other one so far. In this article, we propose to utilize all such methods for flagging safety signals in both areas regardless of which specific area they were originally developed for. Therefore, we selected eight typical methods, that is, proportional reporting ratios, reporting odds ratios, the maximum likelihood ratio test, Bayesian confidence propagation neural network method, chi‐square test for rates comparison, Benjamini and Hochberg procedure, new double false discovery rate control procedure, and Bayesian hierarchical mixture model for systematic comparison through simulations. The Benjamini and Hochberg procedure and new double false discovery rate control procedure perform best overall in terms of sensitivity and false discovery rate. The likelihood ratio test also performs well when the sample sizes are large. Copyright © 2014 John Wiley & Sons, Ltd.     

Adaptive and repeated cumulative meta‐analyses of safety data during a new drug development process

During a new drug development process, it is desirable to timely detect potential safety signals. For this purpose, repeated meta‐analyses may be performed sequentially on accumulating safety data. Moreover, if the amount of safety data from the originally planned program is not enough to ensure adequate power to test a specific hypothesis (e.g., the noninferiority hypothesis of an event of interest), the total sample size may be increased by adding new studies to the program. Without appropriate adjustment, it is well known that the type I error rate will be inflated because of repeated analyses and sample size adjustment. In this paper, we discuss potential issues associated with adaptive and repeated cumulative meta‐analyses of safety data conducted during a drug development process. We consider both frequentist and Bayesian approaches. A new drug development example is used to demonstrate the application of the methods. Copyright © 2015 John Wiley & Sons, Ltd.     

Two-stage phase II trials with early stopping for effectiveness and safety as well as ineffectiveness or harm

This article proposes new optimal and minimax designs, which allow early stopping not only for ineffectiveness or toxicity but also for sufficient effectiveness and safety. These designs may facilitate effective drug development by detecting sufficient effectiveness and safety at an early stage or by detecting ineffectiveness or excessive toxicity at an early stage. The proposed design has advantage over other designs in the sense that it can control the type I error rate and is robust against the real association parameter. Comparing to Jin's design, it is always advantageous in terms of expected sample size.     

Empirical Bayes estimates of finite mixture of negative binomial regression models and its application to highway safety

The empirical Bayes (EB) method is commonly used by transportation safety analysts for conducting different types of safety analyses, such as before–after studies and hotspot analyses. To date, most implementations of the EB method have been applied using a negative binomial (NB) model, as it can easily accommodate the overdispersion commonly observed in crash data. Recent studies have shown that a generalized finite mixture of NB models with K mixture components (GFMNB-K) can also be used to model crash data subjected to overdispersion and generally offers better statistical performance than the traditional NB model. So far, nobody has developed how the EB method could be used with finite mixtures of NB models. The main objective of this study is therefore to use a GFMNB-K model in the calculation of EB estimates. Specifically, GFMNB-K models with varying weight parameters are developed to analyze crash data from Indiana and Texas. The main finding shows that the rankings produced by the NB and GFMNB-2 models for hotspot identification are often quite different, and this was especially noticeable with the Texas dataset. Finally, a simulation study designed to examine which model formulation can better identify the hotspot is recommended as our future research.     

Sparsely clustered survival data: application to the evaluation of the safety and effectiveness of medical devices

The effectiveness and safety of implantable medical devices is a critical public health concern. We consider analysis of data in which it is of interest to compare devices but some individuals may be implanted with two or more devices. Our motivating example is based on orthopedic devices, where the same individual can be implanted with as many as two devices for the same joint but on different sides of the body, referred to as bilateral cases. Different methods of analysis are considered in a simulation study and real data example, including both marginal and conditional survival models, fitting single and separate models for bilateral and non-bilateral cases, and combining estimates from these two models. The results of simulations suggest that in the context of orthopedic devices, where implants failures are rare, models fit on both bilateral and non-bilateral cases simultaneously could be quite misleading, and that combined estimates from fitting two separate models performed better under homogeneity. A real data example illustrates the issues surrounding analysis of orthopedic device data with bilateral cases. Our findings suggest that research studies of orthopedic devices should at minimum consider fitting separate models to bilateral and non-bilateral cases.     

Assessing safety at the end of clinical trials using system organ classes: A case and comparative study

Recent approaches to the statistical analysis of adverse event (AE) data in clinical trials have proposed the use of groupings of related AEs, such as by system organ class (SOC). These methods have opened up the possibility of scanning large numbers of AEs while controlling for multiple comparisons, making the comparative performance of the different methods in terms of AE detection and error rates of interest to investigators. We apply two Bayesian models and two procedures for controlling the false discovery rate (FDR), which use groupings of AEs, to real clinical trial safety data. We find that while the Bayesian models are appropriate for the full data set, the error controlling methods only give similar results to the Bayesian methods when low incidence AEs are removed. A simulation study is used to compare the relative performances of the methods. We investigate the differences between the methods over full trial data sets, and over data sets with low incidence AEs and SOCs removed. We find that while the removal of low incidence AEs increases the power of the error controlling procedures, the estimated power of the Bayesian methods remains relatively constant over all data sizes. Automatic removal of low-incidence AEs however does have an effect on the error rates of all the methods, and a clinically guided approach to their removal is needed. Overall we found that the Bayesian approaches are particularly useful for scanning the large amounts of AE data gathered.