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.     

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.     

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.     

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.     

The statistician's role in the prevention of missing data

Considerable statistical research has been performed in recent years to develop sophisticated statistical methods for handling missing data and dropouts in the analysis of clinical trial data. However, if statisticians and other study team members proactively set out at the trial initiation stage to assess the impact of missing data and investigate ways to reduce dropouts, there is considerable potential to improve the clarity and quality of trial results and also increase efficiency. This paper presents a Human Immunodeficiency Virus (HIV) case study where statisticians led a project to reduce dropouts. The first step was to perform a pooled analysis of past HIV trials investigating which patient subgroups are more likely to drop out. The second step was to educate internal and external trial staff at all levels about the patient types more likely to dropout, and the impact this has on data quality and sample sizes required. The final step was to work collaboratively with clinical trial teams to create proactive plans regarding focused retention efforts, identifying ways to increase retention particularly in patients most at risk. It is acknowledged that identifying the specific impact of new patient retention efforts/tools is difficult because patient retention can be influenced by overall study design, investigational product tolerability profile, current standard of care and treatment access for the disease under study, which may vary over time. However, the implementation of new retention strategies and efforts within clinical trial teams attests to the influence of the analyses described in this case study. Copyright © 2012 John Wiley & Sons, Ltd.     

网络技术下统计的变革和统计教育面临的任务

网络经济的发展使现行的统计信息生产方式和统计制度发生深刻变革,使统计人才的知识结构发生重大变化,从而对统计教育的着力点和培养目标、培养方式提出了新的要求。如何培养大量优秀的复合型统计人才,极大地提高统计人员的素质,是统计教育面临的艰巨任务。     

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.     

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.     

A review of cancer screening evaluation techniques, with some particular examples in breast cancer screening

Summary.  The instigation of mass screening for breast cancer has, over the last three decades, raised various statistical issues and led to the development of new statistical approaches. Initially, the design of screening trials was the main focus of research but, as the evidence in favour of population-based screening programmes mounts, a variety of other applications have also been identified. These include administrative and quality control tasks, for monitoring routine screening services, as well as epidemiological modelling of incidence and mortality. We review the commonly used methods of cancer screening evaluation, highlight some current issues in breast screening and, using examples from randomized trials and established screening programmes, illustrate the role that statistical science has played in the development of clinical research in this field.     

Statistical models for DNA copy number variation detection using read‐depth data from next generation sequencing experiments

In this ‘Big Data’ era, statisticians inevitably encounter data generated from various disciplines. In particular, advances in bio‐technology have enabled scientists to produce enormous datasets in various biological experiments. In the last two decades, we have seen high‐throughput microarray data resulting from various genomic studies. Recently, next generation sequencing (NGS) technology has been playing an important role in the study of genomic features, resulting in vast amount of NGS data. One frequent application of NGS technology is in the study of DNA copy number variants (CNVs). The resulting NGS read count data are then used by researchers to formulate their various scientific approaches to accurately detect CNVs. Computational and statistical approaches to the detection of CNVs using NGS data are, however, very limited at present. In this review paper, we will focus on read‐depth analysis in CNV detection and give a brief summary of currently used statistical analysis methods in searching for CNVs using NGS data. In addition, based on the review, we discuss the challenges we face and future research directions. The ultimate goal of this review paper is to give a timely exposition of the surveyed statistical methods to researchers in related fields.     

Data quality: A statistical perspective

We present the old-but-new problem of data quality from a statistical perspective, in part with the goal of attracting more statisticians, especially academics, to become engaged in research on a rich set of exciting challenges. The data quality landscape is described, and its research foundations in computer science, total quality management and statistics are reviewed. Two case studies based on an EDA approach to data quality are used to motivate a set of research challenges for statistics that span theory, methodology and software tools.     

Who wants to be an ICH Euro billionaire?

The ICH E9 guideline on Statistical Principles for Clinical Trials is a pivotal document for statisticians in clinical research in the pharmaceutical industry guiding, as it does, statistical aspects of the planning, conduct and analysis of regulatory clinical trials. New statisticians joining the industry require a thorough and lasting understanding of the 39-page guideline. Given the amount of detail to be covered, traditional (lecture-style) training methods are largely ineffective. Directed reading, perhaps in groups, may be a helpful approach, especially if experienced staff are involved in the discussions. However, as in many training scenarios, exercise-based training is often the most effective approach to learning. In this paper, we describe several variants of a training module in ICH E9 for new statisticians, combining directed reading with a game-based exercise, which have proved to be highly effective and enjoyable for course participants.     

The potential for bias in reporting of industry-sponsored clinical trials

Concerns about potentially misleading reporting of pharmaceutical industry research have surfaced many times. The potential for duality (and thereby conflict) of interest is only too clear when you consider the sums of money required for the discovery, development and commercialization of new medicines. As the ability of major, mid-size and small pharmaceutical companies to innovate has waned, as evidenced by the seemingly relentless decline in the numbers of new medicines approved by Food and Drug Administration and European Medicines Agency year-on-year, not only has the cost per new approved medicine risen: so too has the public and media concern about the extent to which the pharmaceutical industry is open and honest about the efficacy, safety and quality of the drugs we manufacture and sell. In 2005 an Editorial in Journal of the American Medical Association made clear that, so great was their concern about misleading reporting of industry-sponsored studies, henceforth no article would be published that was not also guaranteed by independent statistical analysis. We examine the precursors to this Editorial, as well as its immediate and lasting effects for statisticians, for the manner in which statistical analysis is carried out, and for the industry more generally.     

基于内部民调的地方统计数据准确性及影响因素研究

当前作为衡量经济和社会发展状况的统计数据的准确性已成为社会大众关注的焦点问题之一。以政府统计工作者为对象调查其对地方统计数据准确性及影响因素的判断,结果表明:当前中国各地统计数据质量总体比较可靠,但是一些地区统计指标的数据准确性较低,"水分"较大,领导干预是影响统计数据质量第一因素。     

Developing a Career in the Practice of Statistics: The Mentor's Perspective

The W.J. Dixon Award for Excellence in Statistical Consulting is given by the American Statistical Association to “a distinguished individual who has demonstrated excellence in statistical consulting or developed and contributed new methods, software, or ways of thinking that improve statistical practice in general.” In this article, five of the seven past recipients of this career-capping award share their experiences and perspectives through 10 stepping stones that move a practicing statistician from consultant to collaborator to leader. We highlight the need for mentorship throughout the discussion, and provide direction for statisticians who would like to incorporate this advice into their careers.     

Some applications of genetics in statistical ecology

Genetic data are in widespread use in ecological research, and an understanding of this type of data and its uses and interpretations will soon be an imperative for ecological statisticians. Here, we provide an introduction to the subject, intended for statisticians who have no previous knowledge of genetics. Although there are numerous types of genetic data, we restrict attention to multilocus genotype data from microsatellite loci. We look at two application areas in wide use: investigating population structure using genetic assignment and related techniques; and using genotype data in capture–recapture studies for estimating population size and demographic parameters. In each case, we outline the conceptual framework and draw attention to both the strengths and weaknesses of existing approaches to analysis and interpretation.     

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.     

Some perspectives on statistical computing

The author argues for an increased emphasis on computing in the training of statisticians and in their professional practice. He describes some of the current technological challenges and demonstrates the importance for statisticians of becoming more active in computational aspects of their work and specifically in producing software for carrying out statistical procedures. Such a reorientation will require substantial changes in thinking, pedagogy and infrastructure; the author mentions some of the conditions required to achieve these goals.     

大数据时代统计学的重构与创新

本文集中介绍了多位顶尖统计学家在大数据研究方面的新进展,内容涉及大数据背景下政府统计需求,统计设计,统计学理论框架的重构,统计学利用大数据在基因学、天文学、宇宙学、流行病学、经济金融学、生命科学和工程学等领域中的应用,以及大数据人才培养问题等。     

A scientist's viewpoint on promoting effective use of experimental design: Ten things a scientist wants to know about experimental design

This paper tackles the practical application of statistical tools. Chemical development is a rich area for collaboration between statisticians and chemists, but most statisticians encounter difficulties in promoting the tools to this client group. This paper, presented from a chemist's perspective, is intended to help bridge the gap between the two professions. The paper explores differences in uptake and attitudes towards adopting these techniques and suggests what has hindered or helped the process of effective application of experimental design. Copyright © 2003 John Wiley & Sons, Ltd.