In Memoriam: Simon Kuznets, 1901-1985

The profession of statistics has adopted too narrow a definition of itself. As a consequence, both statistics and statisticians play too narrow a role in policy formation and execution. Broadening that role will require statisticians to change the curriculum they use to train and develop their own professionals and what they teach nonstatisticians about statistics. Playing a proper role will require new research from statisticians that combines our skills in methods with other techniques of social scientists.     

Professional development of statisticians in the pharmaceutical sector: evolution over the past decade and into the future

The adoption of The International Conference on Harmonization Tripartite Guideline: Statistical Principles for Clinical Trials (ICH-E9) has provided a foundation for the application of statistical principles in clinical research and raised awareness of the value of a statistical contribution to the wider pharmaceutical R&D process. In addition, over the past decade globalization of the pharmaceutical R&D process and the measures taken to address reduced productivity and spiralling costs have impacted on the roles and career opportunities for statisticians working in the pharmaceutical sector. This has enhanced the need for continuing professional development to equip statisticians with the skills to fully contribute to creating innovative solutions. In the future, key areas of focus are the establishment of professional standards for statistical work and increasing the collaboration between statisticians working in industry, regulatory agencies and academia. In addition, the diversity of roles and potential career paths for statisticians embarking on a career in the pharmaceutical sector emphasizes the importance of mentoring and coaching. For the more experienced statisticians, there are unprecedented opportunities to lead and innovate.     

La revue canadiennede statistique statistical science: Concepts,opportunities and challenges

Advances in computing and information technology have had a profound impact on many areas of human activity. Statistical tools are ever more widely used, often in new and unconventional environments. Opportunities for statisticians have never been greater, and at the same time, the use of statistics by others is increasing. The author discusses challenges which arise from these conditions, with emphasis on the identity, activities and education of statisticians.     

Utility values in health technology assessments: a statistician's perspective

This paper provides an introduction to utilities for statisticians working mainly in clinical research who have not had experience of health technology assessment work. Utility is the numeric valuation applied to a health state based on the preference of being in that state relative to perfect health. Utilities are often combined with survival data in health economic modelling to obtain quality‐adjusted life years. There are several methods available for deriving the preference weights and the health states to which they are applied, and combining them to estimate utilities, and the clinical statistician has valuable skills that can be applied in ensuring the robustness of the trial design, data collection and analyses to obtain and handle this data. In addition to raising awareness of the subject and providing source references, the paper outlines the concepts and approaches around utilities using examples, discusses some of the key issues, and proposes areas where statisticians can collaborate with health economic colleagues to improve the quality of this important element of health technology assessment. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

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.     

Theory & Methods: Why do we do it? Statisticians and the Practice of Statistics

There has been no work done on why statisticians have chosen their particular profession. With the increasing emphasis on the development of biotechnology, it seems important to encourage people to take up statistics and to offer the perspective that the study of statistics brings. This paper is an attempt to develop a preliminary understanding, by open-ended in-depth interviewing, of the personal backgrounds that have encouraged present practising statisticians to move into this field, and to see how they experience their work.     

Statistical approaches for conducting network meta-analysis in drug development

We introduce health technology assessment and evidence synthesis briefly, and then concentrate on the statistical approaches used for conducting network meta-analysis (NMA) in the development and approval of new health technologies. NMA is an extension of standard meta-analysis where indirect as well as direct information is combined and can be seen as similar to the analysis of incomplete-block designs. We illustrate it with an example involving three treatments, using fixed-effects and random-effects models, and using frequentist and Bayesian approaches. As most statisticians in the pharmaceutical industry are familiar with SAS? software for analyzing clinical trials, we provide example code for each of the methods we illustrate. One issue that has been overlooked in the literature is the choice of constraints applied to random effects, and we show how this affects the estimates and standard errors and propose a symmetric set of constraints that is equivalent to most current practice. Finally, we discuss the role of statisticians in planning and carrying out NMAs and the strategy for dealing with important issues such as heterogeneity.     

Looking Forward into the 1980's: A Personal View of the Problems and Prospects for the Statistical Profession

The paper begins with a discussion of the state of research and development in Australia, and the effect of technological change, and then provides an assessment of consequent Government policy on the funding of research and development and of tertiary educational institutions. This is then related to likely consequences for the statistical profession. Various issues facing the Statistical Society and statisticians in general are discussed. These include public responsibility in the face of scepticism resulting from widespread statistical misrepresentation, employment opportunities for graduates, the relationships between theoreticians and practitioners, and the state of the statistical literature.     

Methods for national population forecasts: a review

"Three widely used classes of methods for forecasting national populations are reviewed: demographic accounting/cohort-component methods for long-range projections, statistical time series methods for short-range forecasts, and structural modeling methods for the simulation and forecasting of the effects of policy changes. In each case, the major characteristics, strengths, and weaknesses of the methods are described. Factors that place intrinsic limits on the accuracy of population forecasts are articulated. Promising lines of additional research by statisticians and demographers are identified for each class of methods and for population forecasting generally."     

Communication of statistcal concepts: examples in medical collaboration

While effective communication of statistical concepts is important for the enthusiastic adoption of these concepts by collaborators, statisticians are not necessarily trained in the process of communication with collaborators in other substantive fields. It is proposed that increased attention be paid to pedagogical techniques for communicating to our non-statistical colleagues what statisticians have to offer to the design and analysis aspects of a collaborative effort. One approach is to offer examples relevant to our colleagues’ fields when we explain statistical ideas. This paper provides several such examples from the field of neurology, focusing on the issue of sample selection bias and prospective study designs.     

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.     

Subgroup analyses of clinical effectiveness to support health technology assessments

Subgroup analysis is an integral part of access and reimbursement dossiers, in particular health technology assessment (HTA), and their HTA recommendations are often limited to subpopulations. HTA recommendations for subpopulations are not always clear and without controversies. In this paper, we review several HTA guidelines regarding subgroup analyses. We describe good statistical principles for subgroup analyses of clinical effectiveness to support HTAs and include case examples where HTA recommendations were given to subpopulations only. Unlike regulatory submissions, pharmaceutical statisticians in most companies have had limited involvement in the planning, design and preparation of HTA/payers submissions. We hope to change this by highlighting how pharmaceutical statisticians should contribute to payers' submissions. This includes early engagement in reimbursement strategy discussions to influence the design, analysis and interpretation of phase III randomized clinical trials as well as meta-analyses/network meta-analyses. The focus on this paper is on subgroup analyses relating to clinical effectiveness as we believe this is the first key step of statistical involvement and influence in the preparation of HTA and reimbursement submissions.     

Using panel methods to model waiting times for National Health Service surgery

Summary. Long waiting times for non-emergency (elective) procedures are a central feature of the UK's National Health Service, with about 1 million people waiting for surgery at any one time. This paper develops empirical models of the demand for and supply of elective surgery which simultaneously determine waiting times. The models are tested by using a panel of annual data for 5499 small areas from 1991 to 1998. Supply and demand functions are estimated for all specialties combined and for seven individual specialties, using panel data methods that incorporate simultaneously determined variables. The elasticity of demand with respect to waiting time varies between specialties but is always quite small. The results are discussed in the light of UK Government policy initiatives designed to reduce waiting times substantially. The analysis suggests that these initiatives will not stimulate demand markedly and therefore stand a good chance of succeeding provided that adequate additional resources are made available.     

空气污染治理与公众健康———来自“大气十条”政策的证据

为探究《大气污染防治行动计划》 (以下简称“大气十条”)的健康效应,本文基于中国健康与营养调查(CHNS)数据及省级面板数据,利用基于多项Logit模型的双重差分法(Logit-DID),从微观与宏观两个维度考察“大气十条”政策的健康改善效应及其传导机制,并进一步通过异质性分析探讨环境健康不平等的内在原因 研究发现:“大气十条”实施带来显著的健康效应,该政策不仅能有效控制与空气污染高度相关疾病的发病率和死亡占比,还能够间接降低受空气污染影响较小的疾病病情。其作用机理主要通过控制PM2.5浓度改善公众健康,同时该政策对二氧化硫(SO2)、氮氧化物、烟(粉)尘等其 他污染物浓度的下降起到了协同作用。进一步分析显示环境福利不公平的现象依然存在,“大气十条”政 策显著降低了疾病高发人群的发病率,且对女性、城镇居民和低、高年龄段等疾病高发人群的健康影响更显著。本文为政府构建环境健康公平发展路径,推进“健康中国”战略提供了经验证据和政策借鉴。     

Leading beyond regulatory approval: Opportunities for statisticians to optimize evidence generation and impact clinical practice

The role and value of statistical contributions in drug development up to the point of health authority approval are well understood. But health authority approval is only a true ‘win’ if the evidence enables access and adoption into clinical practice. In today's complex and evolving healthcare environment, there is additional strategic evidence generation, communication, and decision support that can benefit from statistical contributions. In this article, we describe the history of medical affairs in the context of drug development, the factors driving post-approval evidence generation needs, and the opportunities for statisticians to optimize evidence generation for stakeholders beyond health authorities in order to ensure that new medicines reach appropriate patients.     

Three-Factor Cartographic Representation

Statistics and statisticians have contributed to industry. Attention has been given to the appropriate training of statisticians for careers in industry. Statistics is used with benefit to industry in quality control, experimental design in research and development, and management decisions. Statistics can benefit from industry's assistance, the subject of this article. Five premises are set forth, three of them suggesting problems for statistics and industry. They relate to public understanding of statistics, the recruitment of students to statistics, the recruitment of statisticians to industry, and the relevance of research in statistics to the needs of industry. Thirteen recommendations are made on what industry can do for statistics and suggestions are made on how the American Statistical Association and the American Society for Quality Control can provide leadership in meeting the problems posed with the help of industry.     

It shouldn't happen to a statistician

Helen Clough and Patrick Brown are statisticians working in a cross-disciplinary veterinary epidemiology collaboration, uniting statisticians, mathematical modellers, veterinarians, molecular biologists and microbiologists. They describe the strengths that statisticians bring to the collaboration and the creative tensions that arise when both statistical and mathematical modelling techniques are used.     

Life and death decisions

It has recently been suggested 1 that many clinical trials should have a data monitoring and ethics committee, and that on this committee should be a statistician. Such committees are attached to individual trials and are distinct from local ethics committees, which are not required to have a statistician and are not concerned solely with trials. Given the plethora of trials, there will be increasing demand for statisticians to sit on these committees. Although it is both an honour and a privilege, Mike Campbell warns that membership should not be undertaken lightly.     

Limitations of P-Values and R-squared for Stepwise Regression Building: A Fairness Demonstration in Health Policy Risk Adjustment

ABSTRACT

Stepwise regression building procedures are commonly used applied statistical tools, despite their well-known drawbacks. While many of their limitations have been widely discussed in the literature, other aspects of the use of individual statistical fit measures, especially in high-dimensional stepwise regression settings, have not. Giving primacy to individual fit, as is done with p-values and R², when group fit may be the larger concern, can lead to misguided decision making. One of the most consequential uses of stepwise regression is in health care, where these tools allocate hundreds of billions of dollars to health plans enrolling individuals with different predicted health care costs. The main goal of this “risk adjustment” system is to convey incentives to health plans such that they provide health care services fairly, a component of which is not to discriminate in access or care for persons or groups likely to be expensive. We address some specific limitations of p-values and R² for high-dimensional stepwise regression in this policy problem through an illustrated example by additionally considering a group-level fairness metric.