Sample size considerations for Japanese patients in a multi‐regional trial based on MHLW guidance

Since the publication of the International Conference on Harmonization E5 guideline, new drug approvals in Japan based on the bridging strategy have been increasing. To further streamline and expedite new drug development in Japan, the Ministry of Health, Labour and Welfare, the Japanese regulatory authority, recently issued the ‘Basic Principles on Global Clinical Trials' guidance to promote Japan's participation in multi‐regional trials. The guidance, in a Q&A format, provides two methods as examples for recommending the number of Japanese patients in a multi‐regional trial. Method 1 in the guidance is the focus of this paper. We derive formulas for the sample size calculations for normal, binary and survival endpoints. Computations and simulation results are provided to compare different approaches. Trial examples are used to illustrate the applications of the approaches. Copyright © 2009 John Wiley & Sons, Ltd.     

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.     

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.     

Missing data: Discussion points from the PSI missing data expert group

The Points to Consider Document on Missing Data was adopted by the Committee of Health and Medicinal Products (CHMP) in December 2001. In September 2007 the CHMP issued a recommendation to review the document, with particular emphasis on summarizing and critically appraising the pattern of drop‐outs, explaining the role and limitations of the ‘last observation carried forward’ method and describing the CHMP's cautionary stance on the use of mixed models. In preparation for the release of the updated guidance document, statisticians in the Pharmaceutical Industry held a one‐day expert group meeting in September 2008. Topics that were debated included minimizing the extent of missing data and understanding the missing data mechanism, defining the principles for handling missing data and understanding the assumptions underlying different analysis methods. A clear message from the meeting was that at present, biostatisticians tend only to react to missing data. Limited pro‐active planning is undertaken when designing clinical trials. Missing data mechanisms for a trial need to be considered during the planning phase and the impact on the objectives assessed. Another area for improvement is in the understanding of the pattern of missing data observed during a trial and thus the missing data mechanism via the plotting of data; for example, use of Kaplan–Meier curves looking at time to withdrawal. Copyright © 2009 John Wiley & Sons, Ltd.     

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.     

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.     

Adaptive designs for pivotal trials: discussion points from the PSI Adaptive Design Expert Group

The Committee for Medicinal Products for Human Use (CHMP) is currently preparing a guideline on 'methodological issues in confirmatory clinical trials with flexible design and analysis plan'. PSI (Statisticians in the Pharmaceutical Industry) sponsored a meeting of pharmaceutical statisticians with an interest in the area to share experiences and identify potential opportunities for adaptive designs in late-phase clinical drug development. This article outlines the issues raised, resulting discussions and consensus views reached. Adaptive designs have potential utility in late-phase clinical development. Sample size re-estimation seems to be valuable and widely accepted, but should be made independent of the observed treatment effect where possible. Where unblinding is necessary, careful consideration needs to be given to preserving the integrity of the trial. An area where adaptive designs can be particularly beneficial is to allow dose selection in pivotal trials via adding/dropping treatment arms; for example, combining phase II and III of the drug development program. The more adaptations made during a late-phase clinical trial, the less likely that the clinical trial would be considered as a confirmatory trial. In all cases it would be advisable to consult with regulatory agencies at the protocol design stage. All involved should remain open to scientifically valid opportunities to improve drug development.     

Web based resource for Statistical Consultants in the Pharmaceutical Industry

The intention of this article is to highlight sources of web-based reference material and software that will aid consulting statisticians when designing clinical trials. The article includes websites that provide links to explanation of statistical concepts for non-statisticians, regulatory guidelines, and free statistical study design software.     

Adjusting overall survival for treatment switches: commonly used methods and practical application

In parallel group trials, long‐term efficacy endpoints may be affected if some patients switch or cross over to the alternative treatment arm prior to the event. In oncology trials, switch to the experimental treatment can occur in the control arm following disease progression and potentially impact overall survival. It may be a clinically relevant question to estimate the efficacy that would have been observed if no patients had switched, for example, to estimate ‘real‐life’ clinical effectiveness for a health technology assessment. Several commonly used statistical methods are available that try to adjust time‐to‐event data to account for treatment switching, ranging from naive exclusion and censoring approaches to more complex inverse probability of censoring weighting and rank‐preserving structural failure time models. These are described, along with their key assumptions, strengths, and limitations. Best practice guidance is provided for both trial design and analysis when switching is anticipated. Available statistical software is summarized, and examples are provided of the application of these methods in health technology assessments of oncology trials. Key considerations include having a clearly articulated rationale and research question and a well‐designed trial with sufficient good quality data collection to enable robust statistical analysis. No analysis method is universally suitable in all situations, and each makes strong untestable assumptions. There is a need for further research into new or improved techniques. This information should aid statisticians and their colleagues to improve the design and analysis of clinical trials where treatment switch is anticipated. Copyright © 2013 John Wiley & Sons, Ltd.     

Wilcoxon's signed‐rank statistic: what null hypothesis and why it matters

In statistical literature, the term ‘signed‐rank test’ (or ‘Wilcoxon signed‐rank test’) has been used to refer to two distinct tests: a test for symmetry of distribution and a test for the median of a symmetric distribution, sharing a common test statistic. To avoid potential ambiguity, we propose to refer to those two tests by different names, as ‘test for symmetry based on signed‐rank statistic’ and ‘test for median based on signed‐rank statistic’, respectively. The utility of such terminological differentiation should become evident through our discussion of how those tests connect and contrast with sign test and one‐sample t‐test. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.     

Web‐based resources to assist the statistical analysis and presentation of data

The intention of this article is to highlight sources of web‐based reference material, courses and software that will aid statisticians and researchers. The article includes websites that: assist in writing a protocol or proposal; link to online statistical textbooks; and provide statistical calculators or links to free statistical software and other guidance documents. Copyright © 2005 John Wiley & Sons, Ltd.     

Carry‐over in cross‐over trials in bioequivalence: theoretical concerns and empirical evidence

There is now general agreement that pre‐testing for carry‐over in the AB/BA design is harmful and that efficient analysis of this design must proceed on the assumption that carry‐over has not affected the results to any appreciable degree. A general consensus has not been achieved in the case of higher‐order designs. Since particular forms of carry‐over can be estimated on a within‐patient basis and unbiased within‐patient treatment estimators are possible, some statisticians favour pre‐testing and some favour automatic adjustment for carry‐over. We present theoretical arguments that show that, just as in the AB/BA case, the strategy of pre‐testing is biased as a whole and also that the loss in terms of efficiency in adjusting is not negligible. We also present data from two large series of bioequivalence studies to provide empirical evidence that in this context carry‐over is either absent or rare. We conclude that adjusting or testing for carry‐over in bioequivalence studies is at worst harmful and at best pointless, and that this may also apply to other kinds of study. Copyright © 2004 John Wiley & Sons, Ltd.     

Assessing the impact of ICH E9

The ICH harmonized tripartite guideline 'Statistical Principles for Clinical Trials', more commonly referred to as ICH E9, was adopted by the regulatory bodies of the European Union, Japan and the USA in 1998. This document united related guidance documents on statistical methodology from each of the three ICH regions, and meant that for the first time clear consistent guidance on statistical principles was available to those conducting and reviewing clinical trials. At the 10th anniversary of the guideline's adoption, this paper discusses the influence of ICH E9 by presenting a perspective on how approaches to some aspects of clinical trial design, conduct and analysis have changed in that time in the context of regulatory submissions in the European Union.     

Review of guidelines and literature for handling missing data in longitudinal clinical trials with a case study

Missing data in clinical trials are inevitable. We highlight the ICH guidelines and CPMP points to consider on missing data. Specifically, we outline how we should consider missing data issues when designing, planning and conducting studies to minimize missing data impact. We also go beyond the coverage of the above two documents, provide a more detailed review of the basic concepts of missing data and frequently used terminologies, and examples of the typical missing data mechanism, and discuss technical details and literature for several frequently used statistical methods and associated software. Finally, we provide a case study where the principles outlined in this paper are applied to one clinical program at protocol design, data analysis plan and other stages of a clinical trial.     

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.     

Non‐Parametric Change‐Point Tests for Long‐Range Dependent Data

Abstract. We propose a non‐parametric change‐point test for long‐range dependent data, which is based on the Wilcoxon two‐sample test. We derive the asymptotic distribution of the test statistic under the null hypothesis that no change occurred. In a simulation study, we compare the power of our test with the power of a test which is based on differences of means. The results of the simulation study show that in the case of Gaussian data, our test has only slightly smaller power minus.3pt than the ‘difference‐of‐means’ test. For heavy‐tailed data, our test outperforms the ‘difference‐of‐means’ test.     

Subgroup analyses in cost‐effectiveness analyses to support health technology assessments

‘Success’ in drug development is bringing to patients a new medicine that has an acceptable benefit–risk profile and that is also cost‐effective. Cost‐effectiveness means that the incremental clinical benefit is deemed worth paying for by a healthcare system, and it has an important role in enabling manufacturers to obtain new medicines to patients as soon as possible following regulatory approval. Subgroup analyses are increasingly being utilised by decision‐makers in the determination of the cost‐effectiveness of new medicines when making recommendations. This paper highlights the statistical considerations when using subgroup analyses to support cost‐effectiveness for a health technology assessment. The key principles recommended for subgroup analyses supporting clinical effectiveness published by Paget et al. are evaluated with respect to subgroup analyses supporting cost‐effectiveness. A health technology assessment case study is included to highlight the importance of subgroup analyses when incorporated into cost‐effectiveness analyses. In summary, we recommend planning subgroup analyses for cost‐effectiveness analyses early in the drug development process and adhering to good statistical principles when using subgroup analyses in this context. In particular, we consider it important to provide transparency in how subgroups are defined, be able to demonstrate the robustness of the subgroup results and be able to quantify the uncertainty in the subgroup analyses of cost‐effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.     

REGULATORY UPDATES: ICH E14 draft consensus guideline on clinical evaluation of QT/QTc interval prolongation

ICH E14 calls for public comment by epidemiologists and statisticians on the practical implications of thresholds to be used for regulatory decision‐making. Readers involved in QT/QTc assessment in drug development and those with an interest in this area are encouraged to give the topic some thought and to be prepared to engage in public debate on the proposed ICH E14 guidance in late 2004 and early 2005. Copyright © 2004 John Wiley & Sons Ltd.     

A comparison of analysis procedures for correlated binary data in dedicated multi‐rater imaging trials

In this paper, three analysis procedures for repeated correlated binary data with no a priori ordering of the measurements are described and subsequently investigated. Examples for correlated binary data could be the binary assessments of subjects obtained by several raters in the framework of a clinical trial. This topic is especially of relevance when success criteria have to be defined for dedicated imaging trials involving several raters conducted for regulatory purposes. First, an analytical result on the expectation of the ‘Majority rater’ is presented when only the marginal distributions of the single raters are given. The paper provides a simulation study where all three analysis procedures are compared for a particular setting. It turns out that in many cases, ‘Average rater’ is associated with a gain in power. Settings were identified where ‘Majority significant’ has favorable properties. ‘Majority rater’ is in many cases difficult to interpret. Copyright © 2014 John Wiley & Sons, Ltd.