Statisticians in European regulatory agencies

The UK body of statisticians in the pharmaceutical industry, PSI, has called on heads of European regulatory agencies responsible for assessing applications for marketing authorizations for new medicines in the EU to employ full time statisticians. In order to assess the present situation a survey has been conducted to identify the number of agencies employing one or more full time statisticians. Out of 29 responding agencies, 12 employed one or more statisticians on a full time basis, whereas 17 did not. Among these 17, 7 involved external experts on a regular basis, 5 involved external statisticians on a case‐by‐case basis, whereas 5 never involved external statistical expertise. Failure to involve statisticians in the assessment of efficacy and safety of medicines does not automatically lead to reports of low quality or invalid assessment of benefit‐risk. However, in depth knowledge of statistical methodology is often necessary to uncover weaknesses and potentially biased efficacy estimates. This might be of importance for the final opinion on granting a marketing authorization, and statistical review should therefore be conducted by those who are professionally expert in the area. A positive trend toward an increased involvement of statistical expertise in the European network of regulatory agencies is observed. Copyright © 2009 John Wiley & Sons, Ltd.     

统计数据质量与用户满意度：测评量表设计与实证研究

 针对政府统计数据质量评估问题,综合若干国家统计部门与国际组织对统计质量的定义,确立政府统计数据质量维度体系;进而参照国际货币基金组织的“数据质量评估框架”设计具体的用户满意度测评量表。基于该量表的问卷调查结果显示,量表具有良好的测量信度与效度。研究中提出的八项质量维度可以充分反映政府统计数据质量的不同侧面,可以有效解构用户满意度的特征;用户对各质量维度的重要性认知、用户专业技术职务、对统计数据的依赖程度以及搜集渠道的选择等,与其满意度之间亦表现出明显的关联模式。     

Assessing student learning in the context of evaluating a chance course

This paper describes the use of different assessment methods to evaluate Chance courses in the NSF-funded Chance project. Multiple methods were used to measure changes in students' attitudes and beliefs about statistics, reasoning about probability and statistics, and ability to critique a newspaper article that reports statistical results. Samples of assessment materials and scoring rubrics used in the evaluation are included.     

Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning

Summary.  Forecasts of future dangerousness are often used to inform the sentencing decisions of convicted offenders. For individuals who are sentenced to probation or paroled to community supervision, such forecasts affect the conditions under which they are to be supervised. The statistical criterion for these forecasts is commonly called recidivism, which is defined as a charge or conviction for any new offence, no matter how minor. Only rarely do such forecasts make distinctions on the basis of the seriousness of offences. Yet seriousness may be central to public concerns, and judges are increasingly required by law and sentencing guidelines to make assessments of seriousness. At the very least, information about seriousness is essential for allocating scarce resources for community supervision of convicted offenders. The paper focuses only on murderous conduct by individuals on probation or parole. Using data on a population of over 60000 cases from Philadelphia's Adult Probation and Parole Department, we forecast whether each offender will be charged with a homicide or attempted homicide within 2 years of beginning community supervision. We use a statistical learning approach that makes no assumptions about how predictors are related to the outcome. We also build in the costs of false negative and false positive charges and use half of the data to build the forecasting model, and the other half of the data to evaluate the quality of the forecasts. Forecasts that are based on this approach offer the possibility of concentrating rehabilitation, treatment and surveillance resources on a small subset of convicted offenders who may be in greatest need, and who pose the greatest risk to society.     

Bayesian networks for sex-related homicides: structure learning and prediction

Sex-related homicides tend to arouse wide media coverage and thus raise the urgency to find the responsible offender. However, due to the low frequency of such crimes, domain knowledge lacks completeness. We have therefore accumulated a large data-set and apply several structural learning algorithms to the data in order to combine their results into a single general graphic model. The graphical model broadly presents a distinction between an offender and a situation-driven crime. A situation-driven crime may be characterised by, amongst others, an offender lacking preparation and typically attacking a known victim in familiar surroundings. On the other hand, offender-driven crimes may be identified by the high level of forensic awareness demonstrated by the offender and the sophisticated measures applied to control the victim. The prediction performance of the graphical model is evaluated via a model averaging approach on the outcome variable offender's age. The combined graph undercuts the error rate of the single algorithms and an appropriate threshold results in an error rate of less than 10%, which describes a promising level for an actual implementation by the police.     

Scale estimators for symmetric stable distributions

This paper reports on a simulation comparison of scale estimators for symmetric stable distributions in terms of their ability to identify the population with the greater scale. The modified geometric mean is found to be superior to the sample standard deviation and the Fama-Roll estimator for the larger values of the characteristic exponent, while the Fama-Roll estimator is judged superior for the smaller values. Further, this study sheds some light on the question of the appropriate sample size for discriminating risk measurement in investment analysis when the samples are taken from symmetric stable distributions.     

Statistical aspects in comparative benefit-risk assessment: challenges and opportunities for pharmaceutical statisticians

Benefit-risk assessment is a fundamental element of drug development with the aim to strengthen decision making for the benefit of public health. Appropriate benefit-risk assessment can provide useful information for proactive intervention in health care settings, which could save lives, reduce litigation, improve patient safety and health care outcomes, and furthermore, lower overall health care costs. Recent development in this area presents challenges and opportunities to statisticians in the pharmaceutical industry. We review the development and examine statistical issues in comparative benefit-risk assessment. We argue that a structured benefit-risk assessment should be a multi-disciplinary effort involving experts in clinical science, safety assessment, decision science, health economics, epidemiology and statistics. Well planned and conducted analyses with clear consideration on benefit and risk are critical for appropriate benefit-risk assessment. Pharmaceutical statisticians should extend their knowledge to relevant areas such as pharmaco-epidemiology, decision analysis, modeling, and simulation to play an increasingly important role in comparative benefit-risk assessment.     

QUALITY CONTROL METHODS IN LARGE-SCALE ASSESSMENT PROCEDURES USING 'DOUBLE-MARKING' OR 'PARTIAL DOUBLE-MARKING'

Ensuring a standard of assessment in situations where marking panels are used is fraught with difficulties, particularly where essay-type responses are to be marked. This paper discusses statistical process control procedures, similar to those used in industry, to help moderate marking quality when ‘double-marking’ or ‘partial double-marking’ are used. When questions are assessed by the same two markers, the scores assigned to responses by each marker may be adjusted so that their assessments are on average equal in terms of location and scale. The paper also discusses methods of controlling sequential assessment, and demonstrates the application of these techniques in evaluating marker consistency, using data from school leaving examinations in geography.     

The probability of identification: applying ideas from forensic statistics to disclosure risk assessment

Summary.  The paper establishes a correspondence between statistical disclosure control and forensic statistics regarding their common use of the concept of 'probability of identification'. The paper then seeks to investigate what lessons for disclosure control can be learnt from the forensic identification literature. The main lesson that is considered is that disclosure risk assessment cannot, in general, ignore the search method that is employed by an intruder seeking to achieve disclosure. The effects of using several search methods are considered. Through consideration of the plausibility of assumptions and 'worst case' approaches, the paper suggests how the impact of search method can be handled. The paper focuses on foundations of disclosure risk assessment, providing some justification for some modelling assumptions underlying some existing record level measures of disclosure risk. The paper illustrates the effects of using various search methods in a numerical example based on microdata from a sample from the 2001 UK census.     

Model-based measurement of latent risk in time series with applications

Summary.  Risk is at the centre of many policy decisions in companies, governments and other institutions. The risk of road fatalities concerns local governments in planning countermeasures, the risk and severity of counterparty default concerns bank risk managers daily and the risk of infection has actuarial and epidemiological consequences. However, risk cannot be observed directly and it usually varies over time. We introduce a general multivariate time series model for the analysis of risk based on latent processes for the exposure to an event, the risk of that event occurring and the severity of the event. Linear state space methods can be used for the statistical treatment of the model. The new framework is illustrated for time series of insurance claims, credit card purchases and road safety. It is shown that the general methodology can be effectively used in the assessment of risk.     

On estimands and the analysis of adverse events in the presence of varying follow‐up times within the benefit assessment of therapies

The analysis of adverse events (AEs) is a key component in the assessment of a drug's safety profile. Inappropriate analysis methods may result in misleading conclusions about a therapy's safety and consequently its benefit‐risk ratio. The statistical analysis of AEs is complicated by the fact that the follow‐up times can vary between the patients included in a clinical trial. This paper takes as its focus the analysis of AE data in the presence of varying follow‐up times within the benefit assessment of therapeutic interventions. Instead of approaching this issue directly and solely from an analysis point of view, we first discuss what should be estimated in the context of safety data, leading to the concept of estimands. Although the current discussion on estimands is mainly related to efficacy evaluation, the concept is applicable to safety endpoints as well. Within the framework of estimands, we present statistical methods for analysing AEs with the focus being on the time to the occurrence of the first AE of a specific type. We give recommendations which estimators should be used for the estimands described. Furthermore, we state practical implications of the analysis of AEs in clinical trials and give an overview of examples across different indications. We also provide a review of current practices of health technology assessment (HTA) agencies with respect to the evaluation of safety data. Finally, we describe problems with meta‐analyses of AE data and sketch possible solutions.     

Subgroup specific incremental value of new markers for risk prediction

In many clinical applications, understanding when measurement of new markers is necessary to provide added accuracy to existing prediction tools could lead to more cost effective disease management. Many statistical tools for evaluating the incremental value (IncV) of the novel markers over the routine clinical risk factors have been developed in recent years. However, most existing literature focuses primarily on global assessment. Since the IncVs of new markers often vary across subgroups, it would be of great interest to identify subgroups for which the new markers are most/least useful in improving risk prediction. In this paper we provide novel statistical procedures for systematically identifying potential traditional-marker based subgroups in whom it might be beneficial to apply a new model with measurements of both the novel and traditional markers. We consider various conditional time-dependent accuracy parameters for censored failure time outcome to assess the subgroup-specific IncVs. We provide non-parametric kernel-based estimation procedures to calculate the proposed parameters. Simultaneous interval estimation procedures are provided to account for sampling variation and adjust for multiple testing. Simulation studies suggest that our proposed procedures work well in finite samples. The proposed procedures are applied to the Framingham Offspring Study to examine the added value of an inflammation marker, C-reactive protein, on top of the traditional Framingham risk score for predicting 10-year risk of cardiovascular disease.     

宏观统计数据质量评估的研究范畴与基本范式

 内容提要：不断推进宏观统计数据质量评估研究的体系化和规范化,是该领域学术价值存续和有效信息揭示的必由出路。本文旨在解决其中的两方面关键问题：一是界定宏观统计数据质量评估的研究范畴,藉此明晰数据质量评估研究的概念结构、方法原理、理论基础和功能边界;二是探讨对各种宏观统计数据质量评估方法的适用性进行检验的可行方法,以确保评估研究可以给出较为可靠的结论。进而明确宏观统计数据质量评估研究的基本范式,探讨研究中应遵循的一系列操作规则。     

A sequential method for the evaluation of the VaR model based on the run between exceedances

Summary: A technique for sequential assessment of the appropriateness of the VaR model is developed, by drawing on a tool from statistical process control, namely the control chart. We show that an EWMA control chart is the most appropriate instrument for detecting changes in the process of the magnitude of interest in risk management. The robustness of our procedure with respect to violations of some assumptions is examined and it is concluded that our model evaluation technique remains suitable in such cases.     

A statistical signalling model for use in surveillance of adverse drug reaction data

SUMMARY This paper presents a statistically superior lag-adjusted model for detecting increased frequency of reports of adverse drug event (ADE) rates. The effect of a significant lag time between ADE occurrence and report dates is studied. The approach in this paper to analyzing ADE data of this nature involves proposing a statistical model that utilizes a lag density function. The statistical method proposed was the development of an 'exact' procedure to monitor drugs that have a low incidence of ADEs. The approach determines statistically whether a change in the frequency of a specific ADE exists between two predetermined time intervals. There exist immense public health implications associated with the early detection of serious ADEs. The reduced risk of unfavorable outcomes associated with medication therapy is the goal of all involved. Simulated illustrations and discussion are provided, along with a detailed FORTRAN program used to implement the newly suggested lag-adjusted procedure.     

Statistical problems in environmental research

Growing concern about the health effects of exposure to pollutants and other chemicals in the environment has stimulated new research to detect and quantify environmental hazards. This research has generated many interesting and challenging methodological problems for statisticians. One type of statistical research develops new methods for the design and analysis of individual studies. Because current research of this type is too diverse to summarize in a single article, we discuss current work in two areas of application: the carcinogen bioassay in small rodents and epidemiologic studies of air pollution. To assess the risk of a potentially harmful agent, one must frequently combine evidence from different and often quite dissimilar studies. Hence, this paper also discusses the central role of data synthesis in risk assessment, reviews some of the relevant statistical literature, and considers the role of statisticians in evaluating and combining evidence from diverse sources.     

Low dose risk estimation via simultaneous statistical inferences

Summary.  The paper develops and studies simultaneous confidence bounds that are useful for making low dose inferences in quantitative risk analysis. Application is intended for risk assessment studies where human, animal or ecological data are used to set safe low dose levels of a toxic agent, but where study information is limited to high dose levels of the agent. Methods are derived for estimating simultaneous, one-sided, upper confidence limits on risk for end points measured on a continuous scale. From the simultaneous confidence bounds, lower confidence limits on the dose that is associated with a particular risk (often referred to as a bench-mark dose ) are calculated. An important feature of the simultaneous construction is that any inferences that are based on inverting the simultaneous confidence bounds apply automatically to inverse bounds on the bench-mark dose.     

Inference for Multi‐dimensional High‐frequency Data with an Application to Conditional Independence Testing

We find the asymptotic distribution of the multi‐dimensional multi‐scale and kernel estimators for high‐frequency financial data with microstructure. Sampling times are allowed to be asynchronous and endogenous. In the process, we show that the classes of multi‐scale and kernel estimators for smoothing noise perturbation are asymptotically equivalent in the sense of having the same asymptotic distribution for corresponding kernel and weight functions. The theory leads to multi‐dimensional stable central limit theorems and feasible versions. Hence, they allow to draw statistical inference for a broad class of multivariate models, which paves the way to tests and confidence intervals in risk measurement for arbitrary portfolios composed of high‐frequently observed assets. As an application, we enhance the approach to construct a test for investigating hypotheses that correlated assets are independent conditional on a common factor.     

Disease clusters: should they be investigated, and, if so, when and how?

Individual cluster reports are subject to several difficulties in interpretation. Although they rarely lead to new aetiological insights, a public health response to delineate the size and extent of any excess risk may be warranted. Further investigation, where merited, should usually include an examination of data for different areas and/or different time periods. A statistical evaluation of disease clusters is often secondary to a detailed appreciation of issues such as the availability and quality of data, confounding and bias in the selection of areas for study.     

关于AHP统计构权方法的几点看法

在多指标统计综合评价中,权数是影响评价结论的一个重要因素。不同的权数体系有可能导致不同的评价结论。最近几年来,人们指出了不少构造统计权数的方法。其中行之有效的构权方法当首推AHP构权法与DELPHI构权法。本文拟就AHP构权法谈几点自己的看法。