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.     

Lessons from meta‐analyses of randomized clinical trials for analysis of distributed networks of observational databases

Networks of constellations of longitudinal observational databases, often electronic medical records or transactional insurance claims or both, are increasingly being used for studying the effects of medicinal products in real‐world use. Such databases are frequently configured as distributed networks. That is, patient‐level data are kept behind firewalls and not communicated outside of the data vendor other than in aggregate form. Instead, data are standardized across the network, and queries of the network are executed locally by data partners, and summary results provided to a central research partner(s) for amalgamation, aggregation, and summarization. Such networks can be huge covering years of data on upwards of 100 million patients. Examples of such networks include the FDA Sentinel Network, ASPEN, CNODES, and EU‐ADR. As this is a new emerging field, we note in this paper the conceptual similarities and differences between the analysis of distributed networks and the now well‐established field of meta‐analysis of randomized clinical trials (RCTs). We recommend, wherever appropriate, to apply learnings from meta‐analysis to help guide the development of distributed network analyses of longitudinal observational databases.     

A Bayesian method for safety signal detection in ongoing blinded randomised controlled trials

Sponsors have a responsibility to minimise risk to participants in clinical studies through safety monitoring. The FDA Final Rule for IND Safety Reporting requires routine aggregate safety evaluation, including in ongoing blinded studies. We are interested in estimating the probability that the true adverse event rate in the experimental arm exceeds that in the control arm. We developed a Bayesian approach that specifies an informative meta-analytic predictive prior on the event probability in the control arm and an uninformative prior on that in the experimental arm. We combined these priors with a mixture likelihood that considers each patient in the ongoing blinded study may belong to the experimental or control arm. This allowed us to estimate the quantity of interest without unblinding. We evaluated our method by simulation, pairing scenarios that differed only in whether a safety signal was present or missing, and quantifying the ability of our model to discriminate using signal detection theory. Our approach shows benefit. It detects safety signals more reliably with greater sample sizes and for common rather than rare events. Performance does not deteriorate markedly when historical studies exhibit heterogeneous hazards or non-constant hazards. Our method will allow us to monitor safety signals in ongoing blinded studies with the goal of earlier identification and risk mitigation. Our method could be adapted to use informative priors on both arms or predictive covariates where pertinent data exist. We stress that ongoing safety monitoring should involve a multi-disciplinary team where statistical methods are paired with medical judgement.     

What Level of Statistical Model Should We Use in Small Area Estimation?

If unit‐level data are available, small area estimation (SAE) is usually based on models formulated at the unit level, but they are ultimately used to produce estimates at the area level and thus involve area‐level inferences. This paper investigates the circumstances under which using an area‐level model may be more effective. Linear mixed models (LMMs) fitted using different levels of data are applied in SAE to calculate synthetic estimators and empirical best linear unbiased predictors (EBLUPs). The performance of area‐level models is compared with unit‐level models when both individual and aggregate data are available. A key factor is whether there are substantial contextual effects. Ignoring these effects in unit‐level working models can cause biased estimates of regression parameters. The contextual effects can be automatically accounted for in the area‐level models. Using synthetic and EBLUP techniques, small area estimates based on different levels of LMMs are investigated in this paper by means of a simulation study.     

Evaluation of program success for programs with multiple trials in binary outcomes

A late‐stage clinical development program typically contains multiple trials. Conventionally, the program's success or failure may not be known until the completion of all trials. Nowadays, interim analyses are often used to allow evaluation for early success and/or futility for each individual study by calculating conditional power, predictive power and other indexes. It presents a good opportunity for us to estimate the probability of program success (POPS) for the entire clinical development earlier. The sponsor may abandon the program early if the estimated POPS is very low and therefore permit resource savings and reallocation to other products. We provide a method to calculate probability of success (POS) at an individual study level and also POPS for clinical programs with multiple trials in binary outcomes. Methods for calculating variation and confidence measures of POS and POPS and timing for interim analysis will be discussed and evaluated through simulations. We also illustrate our approaches on historical data retrospectively from a completed clinical program for depression. Copyright © 2015 John Wiley & Sons, Ltd.     

医疗保险、社会资本与健康水平

中国经济发展的目标之一就是保障人民健康安全,实现这一目标必然要求从战略高度审视居民健康水平,把保障人民健康作为经济社会发展的基本优先目标。文章通过构建医疗保险、社会资本与健康水平的模型,采用2010—2018年CFPS五年的追踪数据,运用工具变量法,实证检验了医疗保险、社会资本与健康水平之间的相互关系。结果表明:医疗保险的参保类型会影响居民的健康水平,而在中国这样的传统型关系社会中,社会资本不仅对健康有着重要的影响,还可能加强或者减弱医疗保险对居民健康水平的影响。具体而言,"新农合"和认知性社会资本对居民健康有显著的正向作用,"城居保"和结构性社会资本对居民健康有显著的负向作用。进一步地,结构性社会资本对"新农合"产生正面影响起到了阻碍作用,却充当了"城居保"扩大负面影响的缓冲剂。     

Testing a Theory of Exact Aggregation

This article analyzes the importance of exact aggregation restrictions and the modeling of demographic effects in Jorgenson, Lau, and Stoker's (1982) model of aggregate consumer behavior. These issues are examined at the household level, using Canadian cross-sectional microdata. Exact aggregation restrictions and some implicit restrictions on household demographic effects are strongly rejected by our data. These results do not preclude pooling aggregate time series data with cross-sectional microdata to estimate a model of aggregate consumer behavior. They do suggest, however, an alternative basis for the aggregate model.     

Blinded sample size re‐estimation in crossover bioequivalence trials

In drug development, bioequivalence studies are used to indirectly demonstrate clinical equivalence of a test formulation and a reference formulation of a specific drug by establishing their equivalence in bioavailability. These studies are typically run as crossover studies. In the planning phase of such trials, investigators and sponsors are often faced with a high variability in the coefficients of variation of the typical pharmacokinetic endpoints such as the area under the concentration curve or the maximum plasma concentration. Adaptive designs have recently been considered to deal with this uncertainty by adjusting the sample size based on the accumulating data. Because regulators generally favor sample size re‐estimation procedures that maintain the blinding of the treatment allocations throughout the trial, we propose in this paper a blinded sample size re‐estimation strategy and investigate its error rates. We show that the procedure, although blinded, can lead to some inflation of the type I error rate. In the context of an example, we demonstrate how this inflation of the significance level can be adjusted for to achieve control of the type I error rate at a pre‐specified level. Furthermore, some refinements of the re‐estimation procedure are proposed to improve the power properties, in particular in scenarios with small sample sizes. Copyright © 2014 John Wiley & Sons, Ltd.     

A proposal for a new PhD level curriculum on quantitative methods for drug development

This paper provides an overview of “Improving Design, Evaluation and Analysis of early drug development Studies” (IDEAS), a European Commission–funded network bringing together leading academic institutions and small‐ to large‐sized pharmaceutical companies to train a cohort of graduate‐level medical statisticians. The network is composed of a diverse mix of public and private sector partners spread across Europe, which will host 14 early‐stage researchers for 36 months. IDEAS training activities are composed of a well‐rounded mixture of specialist methodological components and generic transferable skills. Particular attention is paid to fostering collaborations between researchers and supervisors, which span academia and the private sector. Within this paper, we review existing medical statistics programmes (MSc and PhD) and highlight the training they provide on skills relevant to drug development. Motivated by this review and our experiences with the IDEAS project, we propose a concept for a joint, harmonised European PhD programme to train statisticians in quantitative methods for drug development.     

Overcoming biases and misconceptions in ecological studies

The aggregate data study design provides an alternative group level analysis to ecological studies in the estimation of individual level health risks. An aggregate model is derived by aggregating a plausible individual level relative rate model within groups, such that population-based disease rates are modelled as functions of individual level covariate data. We apply an aggregate data method to a series of fictitious examples from a review paper by Greenland and Robins which illustrated the problems that can arise when using the results of ecological studies to make inference about individual health risks. We use simulated data based on their examples to demonstrate that the aggregate data approach can address many of the sources of bias that are inherent in typical ecological analyses, even though the limited between-region covariate variation in these examples reduces the efficiency of the aggregate study. The aggregate method has the potential to estimate exposure effects of interest in the presence of non-linearity, confounding at individual and group levels, effect modification, classical measurement error in the exposure and non-differential misclassification in the confounder.     

南非物流成本统计方法研究

宏观物流成本是评价一个国家经济发展质量的重要指标,客观、科学地统计宏观物流成本具有非常重要的意义。在系统地介绍了南非物流成本统计模型中的分解与合计统计方法的基础上,着重介绍了合计方法。合计方法是按每一种产品的生产量分别进行统计,比较符合物流运作的实际情况,可以为中国物流成本统计方法的改进提供有益的参考和借鉴。     

A course in constructing effective displays of data for pharmaceutical research personnel

Interpreting data and communicating effectively through graphs and tables are requisite skills for statisticians and non‐statisticians in the pharmaceutical industry. However, the quality of visual displays of data in the medical and pharmaceutical literature and at scientific conferences is severely lacking. We describe an interactive, workshop‐driven, 2‐day short course that we constructed for pharmaceutical research personnel to learn these skills. The examples in the course and the workshop datasets source from our professional experiences, the scientific literature, and the mass media. During the course, the participants are exposed to and gain hands‐on experience with the principles of visual and graphical perception, design, and construction of both graphic and tabular displays of quantitative and qualitative information. After completing the course, with a critical eye, the participants are able to construct, revise, critique, and interpret graphic and tabular displays according to an extensive set of guidelines. Copyright © 2013 John Wiley & Sons, Ltd.     

Burden of bioinformatics in medical research: Statistical perspectives and controversies

The ongoing evolution of genomics and bioinformatics has an overwhelming impact on medical and clinical research, albeit this development is often marked by genuine controversies as well as lack of scientific clarities and acumen. The search for disease genes and the gene–environment interaction has drawn considerable interdisciplinary scientific attention: environmental health, clinical and medical sciences, biological as well as computational and statistical sciences are most noteworthy. Statistical reasoning (quantitative modeling and analysis perspectives) has a focal stand in this respect while data mining resolutions are far from being statistically fully understood or interpretable. The use of human subjects, though unavoidable, under various extraneous restraints, medical ethics perspectives, and human rights undercurrents, has raised concern all over the world, especially in the developing countries. In the genomics context, clinical trials may be designed on chips and yet there are greater challenges due to the curse of dimensionality perspectives. Some of these challenging statistical issues in medical and clinical research (with emphasis on clinical trials) are appraised in the light of existing statistical tools, which are available for less complex clinical research problems.     

煤矿企业安全氛围量表开发研究

采用标准化的量表开发程序,对煤矿企业安全氛围量表进行研究.通过文献分析、高管访谈与专家反馈确定量表的初始项目池,通过预测试样本的项目分析与探索性因子分析对量表题项进行精炼,通过大样本数据的验证性因子分析与回归分析对量表有效性进行确认.研究表明,煤矿企业安全氛围包括7个维度,即安全意识、管理允诺、监管者行为、安全政策、安全交流、安全培训和风险准备.本研究的开发过程不仅规范、可靠,得到的量表结构亦具有较高的信度和效度,能够为煤矿企业安全氛围管理提供理论指导和测量工具.     

Inference in the presence of ranking error in ranked set sampling

The author proposes inference techniques for ranked set sample data in the presence of judgment ranking errors. He bases his analysis on the models of Bohn & Wolfe (1994) and Frey (2007a, b), of which parameters are estimated by minimizing a distance measure. He then uses the fitted models to calibrate confidence intervals and tests. He shows the validity of his approach through simulation and illustrates its application through the construction of distribution‐free confidence intervals for the median area of apple tree leaves covered by a spray.     

高质量发展评价指标体系探讨

党的十九大作出我国经济已由高速增长阶段转向高质量发展阶段的重大判断。测度高质量发展的前提是，在准确理解和把握高质量发展内涵的基础上构建一套科学合理的评价指标体系。通过对高质量发展统计内涵的深入考察，本文在充分梳理、借鉴国内外有关同类评价指标体系的基础上，从“人民美好生活需要”和“不平衡不充分发展”这个社会主要矛盾的两个方面着手，构建了一个由经济活力、创新效率、绿色发展、人民生活、社会和谐5个部分共27个指标构成的高质量发展评价指标体系。该指标体系的特点是：紧扣高质量发展的内涵和新时代社会主要矛盾的变化，指标数量不多但覆盖新发展理念的各个方面，指标不重复，数据易获得。     

Leadership in Statistics: Increasing Our Value and Visibility

Scientists in every discipline are generating data more rapidly than ever before, resulting in an increasing need for statistical skills at a time when there is decreasing visibility for the field of statistics. Resolving this paradox requires stronger statistical leadership to guide multidisciplinary teams in the design and planning of scientific research and making decisions based on data. It requires more effective communication to nonstatisticians of the value of statistics in using data to answer questions, predict outcomes, and support decision-making in the face of uncertainty. It also requires a greater appreciation of the unique capabilities of alternative quantitative disciplines such as machine learning, data science, pharmacometrics, and bioinformatics which represent an opportunity for statisticians to achieve greater impact through collaborative partnership. Examples taken from pharmaceutical drug development are used to illustrate the concept of statistical leadership in a collaborative multidisciplinary team environment.     

Leveraging historical data into oncology development programs: Two case studies of phase 2 Bayesian augmented control trial designs

Leveraging historical data into the design and analysis of phase 2 randomized controlled trials can improve efficiency of drug development programs. Such approaches can reduce sample size without loss of power. Potential issues arise when the current control arm is inconsistent with historical data, which may lead to biased estimates of treatment efficacy, loss of power, or inflated type 1 error. Consideration as to how to borrow historical information is important, and in particular, adjustment for prognostic factors should be considered. This paper will illustrate two motivating case studies of oncology Bayesian augmented control (BAC) trials. In the first example, a glioblastoma study, an informative prior was used for the control arm hazard rate. Sample size savings were 15% to 20% by using a BAC design. In the second example, a pancreatic cancer study, a hierarchical model borrowing method was used, which enabled the extent of borrowing to be determined by consistency of observed study data with historical studies. Supporting Bayesian analyses also adjusted for prognostic factors. Incorporating historical data via Bayesian trial design can provide sample size savings, reduce study duration, and enable a more scientific approach to development of novel therapies by avoiding excess recruitment to a control arm. Various sensitivity analyses are necessary to interpret results. Current industry efforts for data transparency have meaningful implications for access to patient‐level historical data, which, while not critical, is helpful to adjust for potential imbalances in prognostic factors.     

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.